HELSINKI UNIVERSITY OF TECHNOLOGY SMARAD Centre of Excellence Sylvain Ranvier
Path loss models
S-72.333 Physical layer methods in wireless communication systems
Sylvain Ranvier / Radio Laboratory / TKK 23 November 2004
[email protected] HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models Line out 1. Introduction 2. Macrocell path loss models 2.1 Empirical models 2.2 Semi-empirical models 2.3 Deterministic models
3. Microcell path loss models 3.1 Empirical model 3.2 deterministic model 4. Picocell path loss models 4.1 Empirical model 4.2 Semi-empirical model 5. Conclusion
Slide 2 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models Definition of path loss :
The path loss is the difference (in dB) between the transmitted power and the received power
Represents signal level attenuation caused by free space propagation, reflection, diffraction and scattering
Necessary to calculate link budget
Slide 3 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
Empirical models
Three kinds of models Semi-deterministic models
Deterministic models
• Empirical models : based on measurement data, simple (few parameters), use statistical properties, not very accurate
• Semi-deterministic models : based on empirical models + deterministic aspects
• Deterministic models : site-specific, require enormous number of geometry information about the cite, very important computational effort, accurate
Slide 4 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
Different types of cells :
each model is define for a specific environement
Slide 5 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models 2. Macrocell path loss models 2.1 Empirical models
Why empirical models, so called “simplified models” ? Purely theoretical treatment of urban and suburban propagation is very complicated
Not all required geometric descriptions of coverage area are available (e.g. description of all trees, buildings etc…)
Excessive computational effort
Important parameter for cells designer : overall area covered
NOT the specific field strength at particular locations
Slide 6 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models Example To remove effect of fast fading : each measurement = average of set of samples : local mean ( small area around 10-50 m )
Slide 7 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
Okumura-Hata model [1]
Most popular model
Based on measurements made in and around Tokyo in 1968
between 150 MHz and 1500 MHz
• Predictions from series of graphs approximate in a set of formulae (Hata)
• Output parameter : mean path loss (median path loss) LdB
• Validity range of the model : • Frequency f between 150 MHz and 1500 Mhz
•TX height hb between 30 and 200 m •RX height hm between 1 and 10 m •TX -RX distance r between 1 and 10 km
Slide 8 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
Okumura-Hata model cont.
3 types of prediction area :
•Open area : open space, no tall trees or building in path
• Suburban area : Village Highway scattered with trees and house Some obstacles near the mobile but not very congested
• Urban area : Built up city or large town with large building and houses Village with close houses and tall
Slide 9 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models Okumura-Hata model cont. Definition of parameters : hm mobile station antenna height above local terrain height [m] dm distance between the mobile and the building h0 typically height of a building above local terrain height [m] hb base station antenna height above local terrain height [m] r great circle distance between base station and mobile [m] R=r x 10-3 great circle distance between base station and mobile [km] f carrier frequency [Hz] -6 fc=f x 10 carrier frequency [MHz] λ free space wavelength [m]
Slide 10 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
Okumura-Hata model cont. • Okumura takes urban areas as a reference and applies correction factors
Urban areas : LdB = A + B log10 R – E
Suburban areas : LdB = A + B log10 R – C
Open areas : LdB = A + B log10 R – D
A = 69.55 + 26.16 log10 fc – 13.82 log10 hb
B = 44.9 – 6.55 log10 hb 2 C = 2 ( log10 ( fc / 28 )) + 5.4 2 D = 4.78 ( log10 fc ) + 18.33 log10 fc + 40.94 2 E = 3.2 ( log10 ( 11.7554 hm )) – 4.97 for large cities, fc ≥ 300MHz 2 E = 8.29 ( log10 ( 1.54 hm )) – 1.1 for large cities, fc < 300MHz
E = ( 1.1 log10 fc – 0.7 ) hm – ( 1.56 log10 fc – 0.8 ) for medium to small cities
Slide 11 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
COST 231-Hata model [1][5]
Okumura-Hata model for medium to small cities has been extended to cover 1500 MHz to 2000 MHz (1999)
LdB = F + B log10 R – E + G
F = 46.3 + 33.9 log10 fc –13.82 log10 hb E designed for medium to small cities
0 dB medium sized cities and suburban areas G = 3 dB metropolitan areas
Slide 12 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
COST 231-Hata model cont. Accuracy Extensive measurement in Lithuania [8] at 160, 450, 900 and 1800MHz : • Standard deviation of the error = 5 to 7 dB in urban and suburban environment • Best precision at 900 MHz in urban environment • In rural environment : standard deviation increases up to 15 dB and more
Measurements in Brazil at 800 / 900 MHz : • mean absolute error = 4.42 dB in urban environment • standard deviation of the error = 2.63 dB
path loss prediction could be more accurate but models are not complex and fast calculations are possible precision greatly depends on the city structure
Slide 13 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models 2.2 Semi-empirical models
COST 231-Walfisch-Ikegami [2][5]
Cost 231-WI takes the characteristics of the city structure into account :
• Heights of buildings hRoof • Widths of roads w • Building separation b • Road orientation with respect to the direct radio path Φ
increases accuracy of the propagation estimation more complex
N.B. allows estimation from 20 m (instead of 1 km for Okumura-Hata model )
Output parameter : mean path loss
Slide 14 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
COST 231-Walfisch-Ikegami cont.
Restrictions : •Frequency f between 800 MHz and 2000 Mhz
•TX height hBase between 4 and 50 m •RX height hMobile between 1 and 3 m •TX -RX distance d between 0.02 and 5 km
Slide 15 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
COST 231-Walfisch-Ikegami cont.
2 cases : LOS and NLOS
LOS :
LLOS [dB] = 42.6 + 26 log10 d[km] + 20 log10 f [MHz]
NLOS : ∆ Φ ∆ LNLOS [dB] = LFS + Lrts (wr, f, hMobile , ) + LMSD ( hBase, hBase, d, f, bS )
LFS = free space path loss = 32.4 + 20 log10 d[km] + 20 log10 f [MHz]
Lrts= roof-to-street loss
LMSD= multi-diffraction loss
Slide 16 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
COST 231-Walfisch-Ikegami cont. ∆ Lrts= -8.8 + 10log10 ( f [MHz] ) + 20log10 ( hMobile[m] ) –10 log10 ( w [m] )+ Lori
Lori = street orientation function -10 + 0.35 Φ 0 ≤ Φ < 35° Φ ≤ Φ LORI = 2.5 + 0.075 ( – 35 ) 35° < 55° 4.0 – 0.114 (Φ –55 ) 55° ≤ Φ < 90°
LMSD= Lbsh + ka + kd log10 (d [km] ) + kf log10 ( f [MHz] ) – 9 log10 ( b )
∆ -18 log10 ( 1 + hBase ) hBase > hRoof Where Lbsh = 0 hBase ≤ hRoof
Slide 17 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
COST 231-Walfisch-Ikegami cont.
54 hBase > hRoof ∆ ≥ ka = 54 – 0.8 hBase d 0.5 km, hBase ≤ hRoof ∆ 54 – 0.8 hBase d [km] / 0.5 d < 0.5 km, hBase ≤ hRoof
18 hBase > hRoof kd = ∆ 18 – 15 hBase / hRoof hBase ≤ hRoof
0.7 ( f / 925 – 1 ) medium sized city kf = -4 + 1.5 ( f / 925 – 1 ) metropolitan center
Slide 18 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
Clutter Factor model - Plane earth model [1]
Plane earth model : deterministic model Propagation : direct path + reflection from ground
Plane earth loss : LPEL = 40 log10 r –20 log10 hm –20 log10 hb hm, hb << r Not accurate when taken in isolation
Slide 19 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
Clutter Factor model [1] Measurements in urban and suburban areas : path loss exponent close to 4 ( like in plane earth model ) model : based on plane earth loss + clutter factor
Slide 20 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
2.3 Deterministic models
Based on theory ( propagation mechanisms )
Deterministic models estimate propagation of radio wave analytically
two different approaches : solving electromagnetic formulas and ray tracing
solving electromagnetic formulas : extremely complicated
ray tracing : most widely used (requires a lot of computing power)
Slide 21 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
Ray tracing [6][7]
based on geometrical optics (GO)
used to modelling reflection and refraction of optical rays. if f < 10 GHz : diffraction has to be taken into account different diffraction models are added to GO as extensions
Two methods for ray tracing : ray imaging and ray launching
Slide 22 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
Ikegami model [1]
entirely deterministic prediction of field strengths at specified points
• Using detail map of building heights, shapes and positions trace ray paths Restriction : only single reflection from wall accounted for • Diffraction calculated using single edge approximation • Wall reflection are assumed to be fixed at constant value
two ray (reflected, diffracted) are power summed : 3 L =10log f + 10log (sinφ) + 20log (h − h ) −10log w −10log 1+ − 5.8 E 10 c 10 10 0 m 10 10 2 Lr Φ= angle between the street and the direct line from base to mobile
Lr = reflection loss = 0.25 Slide 23 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
Ikegami model cont.
• model tends to underestimate loss at large distance
•Variation of frequency is underestimated compared with measurement
Slide 24 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models 3. Microcell path loss models 3.1 Empirical model Dual slope empirical model [1] Motivation : simple power law path loss model not accurate enough Dual slop model
Two separate path loss exponents are used to characterize the propagation
breakpoint distance of a few hundred meters
≤ 10n1 log10 r + L1 for r rb Path loss : L =
10n2 log10 ( r / rb ) + 10n1 log10 rb + L1 for r > rb
L1 = reference path loss at r =1 m rb = breakpoint distance n1 = path loss exponent for rr≤ b n2 = path loss exponent for r > rb Slide 25 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
Dual slope empirical model cont. To avoid sharp transition between the two region :
L= L1 + 10n1 log10 r + 10 ( n2 –n1 ) log10 ( 1+ r / rb )
Usually n1 = 2 and n2 = 4 but can vary greatly depending on environment Slide 26 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models 3.2 deterministic model Two-ray model [1] valid for line of sight
at least 1 direct ray and 1 reflected ray
Similar approach as plane earth loss but two path lengths not necessarily equal 2 − − λ 2 jkr1 jkr2 1 e e = + R R = Fresnel reflection coefficient π L 4 r1 r2 Slide 27 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
4. Picocell path loss models
Base station antenna located inside building
Slide 28 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models 4.1 Empirical model Propagation within buildings Wall and floor factor models [1] Characterize indoor path loss by : a fixed exponent of 2 (as in free space) + additional loss factors relating to number of floors nf and walls nw intersected by the straight-line distance r between terminals
L= L1 + 20log r + nf af + nw aw af = attenuation factor per floor aw = attenuation factor per wall
L1 = reference path loss at r =1 m
Slide 29 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
Wall and floor factor models - ITU-R models. [1]
Similar approach except :
• only floor loss is accounted explicitly
• loss between points on same floor included implicitly by changing path loss exponent
LT = 20log10 fc[MHz] + 10n log10 r [m] + Lf ( nf ) –28
Slide 30 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
Wall and floor factor models - ITU-R models cont.
LT = 20log10 fc[MHz] + 10n log10 r [m] + Lf ( nf ) –28
Slide 31 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models 4.2 Semi-empirical model Propagation into buildings
COST231 line-of-sight model [1]
θ 2 Total path loss : LT = LF + Le + Lg (1-cos ) + max(L1 , L2)
LF = free space loss for total path length (ri + re) θ Le = path loss through external wall at normal incidence ( = 0°) θ Lg = additional external wall loss incurred at grazing incidence ( = 90°) α θ 2 L1= nwLi and L2 = (ri –2)(1-cos )
Nw = number of wall crossed by the internal path ri
Li = loss per internal wall α= specific attenuation which applies for unobstructed internal path
Slide 32 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
COST231 line-of-sight model cont.
Slide 33 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
5. Conclusion
Empirical models : • not always accurate enough • can be used only over parameter ranges included in the original measurement set
Deterministic models : • require an enormous amount of data to describe fully the cover area • very important computational effort
Compromise
Slide 34 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models References : [1] S. Saunders, Antennas and Propagation for Wireless Communication Systems, Wiley, 2000, 409 p. [2] R. Vaughan, J. Bach Andersen, Channels, Propagation and Antennas for Mobile Communications, IEE, 2003, 753 p. [3] H. Bertoni, Radio Propagation for Modern Wireless Systems, Prentice Hall, 2000, 258 p. [4] K. Siwiak, Radiowave Propagation and Antennas for Personal Communications, Artech House, 1998, 418 p. [5] COST231, final report, 1999. [6] W. Backman, Error Correction on Predicted Signal levels in Mobile Communications, master thesis, 2003. [7] J. Rissanen, Dynamic resource reallocation in cellular networks, master thesis, 2003. [8] A. Medeisis, A.Kajackas, On the Use of the Universal Okumura-Hata Propagation Prediction Model in Rural Areas, IEEE Vehicular Technology Conference Proceeding, Vol. 3, May 2000, pp. 450-453.
Slide 35 HELSINKI UNIVERSITY OF TECHNOLOGY S-72.333 Physical layer methods in wireless SMARAD Centre of Excellence communication systems Sylvain Ranvier Path loss Models
Homework :
1) What are the advantages and defaults of empirical models, what is the most widely used empirical model ?
2) Using the ITU-R model, calculate the path loss at 0.9 GHz in an office environment, where the distance between Tx and Rx is 10 m, and they are separated by 1 floor.
Slide 36 International Journal of Computer Applications (0975 – 8887) Volume 59– No.11, December 2012 Comparison of Okumura, Hata and COST-231 Models on the Basis of Path Loss and Signal Strength
Yuvraj Singh
ABSTRACT Radio propagation is essential for emerging technologies with appropriate design, deployment and management strategies for any wireless network. It is heavily site specific and can 3 vary significantly depending on terrain, frequency of operation, velocity of mobile terminal, interface sources and other dynamic factor. Accurate characterization of radio channel through key parameters and a mathematical model is important for predicting signal coverage, achievable data 2 rates, BER and Antenna gain. 1 Large scale path loss modeling plays a fundamental role in designing both fixed and mobile radio systems. Predicting the radio coverage area of a system is not done in a standard Transmitter station Mobile unit manner. Wireless systems are expensive systems. Therefore, 1. Reflection 2. Diffraction 3. Scattering before setting up a system one has to choose a proper method depending on the channel’s BTS antenna height gain. By Fig Mechanism of propagation proper selecting the above parameters there is a need to select the particular communication model which show good result 2. OKUMURA MODEL by considering these parameters. This is the most popular model that being used widely The Okumura model for Urban Areas is a Radio propagation 1. INTRODUCTION model that was built using the data collected in the city of Wireless access network has becoming vital tools in Tokyo, Japan. The model is ideal for using in cities with many maintaining communications especially at home and urban structures but not many tall blocking structures. The workplaces due to communication models. Propagation model served as a base for Hata models. Okumura model was models can be classified mainly into two extremes, i.e. fully built into three modes which are urban, suburban and open empirical models and Deterministic models. There are some areas. The model for urban areas was built first and used as models which have the characteristics of both types. Those are the base for others. Clutter and terrain categories for open known as Semi-empirical models. Empirical models are based areas are there are no tall trees or buildings in path, plot of on practically measured data. Since few parameters are used, land cleared for 200-400m. For examples at farmland, rice these models are simple but not very accurate. The models fields and open fields. For suburban area the categories is which are categorized as empirical models for macro cellular village or highway scattered with trees and houses, few environment. These include Hata model, Okumura model, obstacles near the mobile. Urban area categories is built up COST-231 Hata model. On the other hand, deterministic city or large town with large buildings and houses with two or models are very accurate. Some of the examples include Ray more storey or larger villager with close houses and tall, Tracing and Ikegami model. As mentioned earlier, semi- thickly grown trees. empirical models are based on both empirical data and Formula for Okumura Model is expressed below: deterministic aspects. Cost-231 Walfisch-Ikegami model is categorized as a semi empirical model. All these models Lm(dB) = LF(d)+ Amu(f,d) – G(hr) – G(ht) – GAREA estimate the mean path loss based on parameters such as antenna heights of the transmitter and Receiver, distance Where; between them, etc. These models have been extensively Lm = (i.e., median) of path loss validated for mobile networks. Most of these models are based on a systematic interpretation of measurement data LF(d) = free space propagation path loss. obtained in the service area [1][2][3][4][5][6]. Amu(f,d) = median attenuation relative to free space
G(hb) = base station antenna height gain factor
G(hm) = mobile antenna height gain factor
G(hb) = 20log(hb/200) 1000m > hb > 30m
G(hm) = 10log(hm/3) hm<= 3m
G(hm) = 20log(hm/3) 10m > hm > 3m
37 International Journal of Computer Applications (0975 – 8887) Volume 59– No.11, December 2012
GAREA: gain due to type of environment given in suburban, The path loss according to the COST-231-Hata model is urban or open areas Correction factors like terrain related expressed as: parameters can be added using a graphical form to allow for street orientation as well as transmission in suburban and open Lp (dB) = A + B log10 (d) +C areas and over irregular terrain. Irregular terrain is divided Where; into rolling hilly terrain, isolated mountain, general sloping terrain and mixed land-sea path. The terrain related A = 46.3+ 33.9 log10 (fc) – 13.28 log10 (hb) – a (hm) parameters that must be evaluated to determine the various B = 44.9 – 6.55 log 10 (hb) corrections factors [6][7][8][9][10]. C= 0 for medium city and suburban areas 3. HATA MODEL 3 for metropolitan areas Hata established empirical mathematical relationships to describe the graphical information given by Okumura. Hata’s 5. Calculation of Path loss formulation is limited to certain ranges of input parameters The common representation formula of different and is applicable only over quasi-smooth terrain. The communication models is [16][17][18] mathematical expression and their ranges of applicability are as follows [3][5][9] PL (d) = PL (d0) + 10nlog10 (d/d0) where
Carrier Frequency: 150 MHz ≤ fc ≤1500 MHz d= Distance between Transmitter station and Mobile station
Base Station (BS) Antenna Height: 30 m ≤hb ≤200 m do= Reference point
Mobile Station (MS) Antenna Height: 1 m ≤hm ≤10 m n= Path loss exponent Transmission Distance: 1 km ≤d ≤20 km 6. Calculation of signal strength A + B log10 (d) for urban areas The Received Signal Strength Indicator (RSSI) or Signal
Lp (dB) =A + B log10 (d) – C for suburban area Strength is a measure of how strong the most recent signal was when it reached its destination. The RSSI value ranges A + B log10 (d) – D for open area from 0 to 255. Higher RSSI values indicate a stronger signal. Where: Reliable communication can best be achieved with RSSI values greater than 70. If the RSSI is too low the wireless A = 69.55 +26.161log10 (fc) – 13.82 log10 (hb) – a (hm) communications may become intermittent or fail entirely. The received signal strength for Okumura model, Hat model and B = 44.9 – 6.55 log (h ) 10 b COST-231 model can be calculated as 2 C = 5.4 + 2 [log10 (fc / 28)] Pr = Pt + Gt + Gr –PL – A D = 40.94 + 4.78 [log (f )]2 – 18.33 log (f 10 c 10 c) Where
Where, a (hm) = Pr is received signal strength in dBm. [1.1log (f ) –0.7] h – [1.56log (f ) –0.8] 10 c m 10 c P is transmitted power in dB . for medium or small cities t m
2 Gt is transmitted antenna gain in dBm. 8.29[log10 (1.54 hm)] – 1.1 for large city and fc ≤ 200 MHz Gr is received antenna gain in dBm. 2 3.2 [log10 (11.75hm)] – 4.97 PL is total path loss in dBm. for large city and fc ≥ 400 MHz A is connector and cable loss in dBm. 4. COST-231 MODEL In this work, connector and cable loss are not taken into Most future PCS systems are expected to operate in the 1800- consideration [5][18][20][21][22] 2000 MHz frequency band. It has been shown that path loss can be more dramatic at these frequencies than those in the 7. RESULTS 900 MHz range. Some studies have suggested that the path loss experienced at 1845 MHz is approximately 10 dB larger 7.1 Comparison of Okumura, Hata and than those experienced at 955 MHz, all other parameters Cost-231 models based on Path Loss being kept constant. The COST231-Hata model extends Since attenuation is also the main cause of path loss which Hata's model for use in the 1500-2000 MHz frequency range, can be described as: where it is known to underestimate path loss. The model is expressed in terms of the following parameters A=16.5+15log(f/100)-0.12d [9][10][13][17] Where; f=frequency of operation, d=distance travelled.
Carrier Frequency (fc) 1500-2000 MHz BS Antenna Height (hb) 30-200 m
MS Antenna Height (hm) 1-10 m Transmission Distance (d) 1-20 km
38 International Journal of Computer Applications (0975 – 8887) Volume 59– No.11, December 2012
Fig1.Variation of attenuation coefficient with frequency. Fig3.Comparison of path loss of communication models with respect to height of receiver antenna.
Fig2.Comparison of path loss of communication models with respect to height of transmitter antenna. Fig4. Comparison of path loss of communication models with respect to transmission distance.
Table1.Comparison of path loss of communication models with respect to distance Distance(km) Okumura Hata path Cost-231 path loss(db) path loss(db) loss(db) 1 44.03 227.74 275.51 10 64.03 257.57 305.34 20 70.05 266.54 314.32
39 International Journal of Computer Applications (0975 – 8887) Volume 59– No.11, December 2012
7.2 Comparison of Okumura, Hata and Cost-231 models based on signal strength Since the comparison of communication models based on signal strength which considered the correction factor into account is
C=(1.1logf-0.7)hm-(1.56logf-0.8)
Where; hm=height of receiver antenna.
Fig 7. Comparison of signal strength of communication models based on height of receiver antenna
Fig 5.Variation of correction factor with frequency.
Fig 8. Comparison of signal strength of communication models based on transmission distance Table 2. Comparison of signal strength of communication models based on coverage distance Distance(km) Okumura Hata Cost-231 model model model signal signal signal strength strength strength (db) (db) (db) 5 41.98 -148.59 -196.36 10 35.96 -157.57 -205.34
15 32.44 -162.82 -210.59 Fig 6. Comparison of signal strength of communication models based on height of transmitter antenna. 20 29.94 -166.54 -214.32
40 International Journal of Computer Applications (0975 – 8887) Volume 59– No.11, December 2012
8. Conclusion [10] . P. Schneider, F. Lambrecht & A. Baier, “Enhancement of the Okumura-Hata propagation using The path loss of Okumura, Hata and Cost 231 models shows detailed morphological and building data”, IEEE, 1996. decreasing trend with respect to transmitter antenna height and receiver antenna height and increasing trend with respect [11] . A. H. Ali & M. R. A. Razak, “Investigation of outdoor to transmission distance. Among the communication models fading model over indoor environment”, IEEE Okumura model shows the least path loss and Cost-231 model International Conference on Business, Engineering and shows the largest path loss. The signal strength trends are Industrial Applications (ICBEIA), 978-1-4577-1280- opposite to that of path loss as signal strength with respect to 7/11/$26.00 2011. transmitter antenna height and receiver antenna height shows [12] . J. O. Mark, B. B. Samir & M. Naufal , “BER increasing trend and decreasing trend with respect to Performance evaluation for multicarrier CDMA over transmission distance. The signal strength of Okumura model generalized K-µ fading channels”, IEEE symposium on is largest in all the three cases and Cost-231 model shows the computer and informatics, 2011. least signal strength. Among the three models Hata model shows intermediate results both in case of path loss and signal [13] . A. katariya, A. yadav, N. Jain & G. tomar, “BER strength. performance criteria based on standard IEEE802.11a for OFDM in multipath fading environment”, International 9. REFERENCES Conference on Computational Intelligence and Communication Systems, 2011. [1] . T. K. Sarkar, Z. Ji, K. Kim, A. Medour & M. S. Palma, “A Survey of Various Propagation Models for [14] . S. Nasrzadeh & M. Jalali, “Channel Estimation and Mobile Communication”, IEEE Antennas and Symbol Detection in AWGN Channel for New Structure Propagation Magazine, Vol. 45, No. 3, June 2003. of CDMA Signals”, Eighth International Conference on Information Technology, 2011. [2] . N. L. B. M. Nordon, “Interface developing for Hata model using Matlab”, Universiti Teknologi Malaysia, [15] . H. Kaur, B. Jain & A. Verma, “Comparative May 2008. performance analysis of M-ary PSK Modulation Schemes using Simulink”, International journal of ECE, [3] . Z. Nadir & M. Idrees Ahmad, “Path loss Determination IJECT, Vol. 2, Issue 3, September 2011. Using Okumura-Hata Model and Cubic Regression for Missing Data for Oman”, Proceeding of IMECS, Vol. 2, [16] . Spectrum Planning Team, “Investigation of 2010. Modified Hata Propagation Models”, IEEE, April 2001. [4] . H. K. Sharma, S. H. Sahu & S. Sharma, “Enhanced cost [17] . M. A. Masud, M. Samsuzzaman & M. A. Rahman, 231 propagations model in wireless network” “Bit Error Rate Performance Analysis on Modulation International journal of computer application (0975 – Techniques of Wideband Code Division Multiple 8887) Vol. 19, No. 06, April 2011. Access”, Journal Of Telecommunication, Volume 1, Issue 2, PP. 22-29, March 2010. [5] . S. sarooshyari & N. Madaya, “An Introduction to mobile radio propagation and characterization of frequency [18] . A. A. Tahir & F. Zhao, “Performance analysis on bands” wireless comm. Technologies, IEEE, 16:332:559, modulation techniques of W-CDMA in multipath fading 1996. channel “, January 2009. [6] . P. K. Sharma & R. K. Singh, “Comparative Analysis [19] . O. Grigoriadis & H. Srikanth Kamath, “Ber of Propagation Path loss Models with Field Measured Calculation Using Matlab Simulation For OFDM Data”, International Journal of Engineering Science and Transmission”, IMECS, Vol.2, 2008. Technology Vol. 2(6), 2010, pp 2008-2013. [20] . C. Akkash, “Methods for Path loss Prediction”, [7] . V. S. Abhayawardhana, I. J. Wassell, D. Crosby, M. P. Report 09067, ISSN 1650-2647, Oct. 2009. Sellars & M. G. Brown,” Comparison of Empirical [21] . H. Cavdar,” A Statistical Approach to Bertoni - Propagation Path Loss Models for Fixed Wireless Walfisch Propagation Model for Mobile Radio Design in Access Systems”, IEEE , December 2003. Urban area, IEEE, PP. 279-283, 2001. [8] . M. Kumar, V. Kumar & S. Malik, “Performance and [22] . M. Dottling, A. Jahn & W. Wiesbeck, “A comparison analysis of propagation models for predicting RSS for and verification of 2D and 3D ray tracing propagation efficient handoff”, International journal of advanced models for land mobile satellite communications”, IEEE, scientific research & technology, Vol.1, Issue 2, 0-7803-6369, PP. 434-437, 2000. February 2012.
[9] . A. Neskovic, N. Neskovic & G. Paunovic, “Modern approaches in modeling of mobile radio systems propagation environment”, IEEE Communications Surveys· 2000.
41
Pathloss Determination Using Okumura-Hata Model And Cubic Regression For Missing Data For Oman
Zia Nadir, Member, IAENG , Muhammad Idrees Ahmad
Abstract— In this paper, we aim to adapt a propagation This article addresses the comparisons between the model for Salalah (OMAN) as we examine the applicability of theoretical and the empirical propagation models. It was Okumura-Hata model in Oman in GSM frequency band. The achieved that, the most extensively used propagation data for study is in continuation to the work by authors which was mobile communications is Okumura’s measurements and this carried out for urban area due to the data obtained from local is recognized by the International Telecommunication Union operator. We accomplish the modification of model by investigating the variation in pathloss between the measured (ITU) [3]. and predicted values, according to the Okumura-Hata The cellular concept was a major breakthrough in solving propagation model for a cell in Salalah city and then finding the the problem of spectral congestion and user’s capacity. It missing experimental data with cubic regression. We also verify offered high capacity with a limited spectrum allocation our modified model by applying it for other cells. The mean without any major technological change. The cellular square error (MSE) was calculated between measured path loss concept is a system level idea in which a single, high power values and those predicated on basis of Okumura-Hata model for an open area. The MSE is up to 6dB, which is an acceptable transmitter (large cell) is replaced with many low power value for the signal prediction. The model gave a significant transmitters (small cells). The area serviced by a transmitter difference in an open area that allowed necessary changes to be is called a cell. Each small powered transmitter, also called a introduced in the model. That error was minimized by base station provides coverage to only a small portion of the subtracting the calculated MSE from the original equation of service area. The power loss involved in transmission open area for Okumura-Hata model. Modified equation was between the base station (BTS) and the mobile station (MS) also re-verified for another cell in an open area in Oman and gave acceptable results. Theoretical simulation by Okumura is known as the path loss and depends particularly on the Hata Model and the obtained experimental data is compared antenna height, carrier frequency and distance. At higher and analyzed further using a cubic regression on the set of the frequencies the range for a given path loss is reduced, so experimental data. Scatter plot of the experimental data on more cells are required to cover a given area. Base stations path loss verses distance reveals a third order polynomial trend close to one another are assigned different groups of in the experimental data. Therefore the cubic regression model channels. So that all the available channels are assigned to a was fitted using method of least squares which estimates the parameters by minimizing sum of squares of the white noise. relatively small number of neighboring base stations. The coefficient of determination of this regression suggested Neighboring base stations are assigned different groups of that about 90% variation in path loss can be explained. channels so that the interference between base stations or interaction between the cells is minimized. As the demand for Index Terms— Okumura Hata Model, Pathloss, Propagation service increases, the number of base stations may be models, Cubic Regression. increased, thereby providing additional capacity with no increase in radio spectrum. The key idea of modern cellular systems is that it is possible to serve the unlimited number of I. INTRODUCTION subscribers, distributed over an unlimited area, using only a Mobile communications is growing rapidly due to enabling limited number of channels, by efficient channel reuse [2]. technologies, which permit wider deployment. Historically, growth in the mobile communications field has now become II. THEORETICAL PROPAGATION MODELS slow, and has been linked to technological advancements The propagation models are divided into two basic types; [1,2]. The need for high quality and high capacity networks, estimating coverage accurately has become extremely namely: Free space propagation and Plane earth propagation important. Therefore, for more accurate design coverage of model. modern cellular networks, signal strength measurements A. Free Space Propagation Model must be taken into consideration in order to provide an In free space, the wave is not reflected or absorbed. Ideal efficient and reliable coverage area. propagation implies equal radiation in all directions from the
radiating source and propagation to an infinite distance with Manuscript received November 14, 2009. no degradation. Spreading the power over greater areas Z. Nadir is with the ECE dept. of Sultan Qaboos University, Muscat, causes the attenuation. Equation (1) illustrates how the power OMAN (phone: 24142536; e-mail: [email protected] ). M. Idrees Ahmed is with DOMAS, Sultan Qaboos University, Muscat, flux is calculated. OMAN ., (e-mail: [email protected]).
PPdt = /4 π d ² (1) desert, wet ground etc). Path Loss (1) Equation for the plane Earth Model is illustrated in equation (7). 2 Where Pt is known as transmitted power (W/m ) and Pd is the power at a distance d from antenna. If the radiating Lpe = 40log10 (dh )-20log 10 ( 1 )-20log 10 ( h 2 ) (7) element is generating a fixed power and this power is spread over an ever-expanding sphere, the energy will be spread Where d represents the path length in meters and h1 and h2 more thinly as the sphere expands. are the antenna heights at the base station and the mobile, respectively. The plane earth model in not appropriate for By having identified the power flux density at any point of mobile GSM systems as it does not consider the reflections a given distance from the radiator, if a receiver antenna is from buildings, multiple propagation or diffraction effects. placed at this point, the power received by the antenna can be Furthermore, if the mobile height changes (as it will in calculated. The formulas for calculating the effective antenna practice) then the predicted path loss will also be changed. aperture and received power are shown in equations (2) and (3) below. The amount of power ‘captured’ by the antenna at III. EMPIRICAL PROPAGATION MODELS the required distance d, depends upon the ‘effective aperture’ Empirical propagation models will be discussed in this of the antenna and the power flux density at the receiving section; among them are Okumura and Hata models. element. Actual power received by the antenna depends on the following: (a) The aperture of receiving antenna Ao, (b) the wavelength of received signal λ, (c) and the power flux A. Cellular Propagation Models density at receiving antenna Pd. The two basic propagation models (free space loss and plane earth loss) would require detailed knowledge of the Effective area Ae of an isotropic antenna is: location, dimension and constitutive parameters of every tree, building, and terrain feature in the area to be covered. This is Ae = λ²/4 π (2) far too complex to be practical and (2) would yield an unnecessary amount of detail. One appropriate way of While power received is: accounting for these complex effects is via an empirical model. There are various empirical prediction models among them are, Okumura – Hata model, Cost 231 – Hata model, PPArde=× = P t ×λπ²/(4 d )² (3) (3) Cost 231 Walfisch – Ikegami model, Sakagami- Kuboi model. These models depend on location, frequency range While equation (4) illustrates the path loss (Lp): and clutter type such as urban, sub-urban and countryside.
Lptr = Power transmitted ()- P Power received () P (4) When substituting equation (3) in equation (4), it yields B. Okumura’s Measurements equation (5): Okumura carried out extensive drive test measurements with range of clutter type, frequency, transmitter height, and transmitter power. It states that, the signal strength decreases LdBp ( ) =+ 20 log10 (4π ) 20 log 10 ( d ) - 20 log 10 (λ ) at much greater rate with distance than that predicted by free (5) space loss [3-5].
Then substituting (λ (in km) = 0.3 / f (in MHz)) and rationalizing the equation produces the generic free space C. Hata’s Propagation Model path loss formula, which is stated in equation (6): Hata model was based on Okumura’s field test results and predicted various equations for path loss with different types
Lp (dB ) =+ 32.5 20 log10 ( d ) + 20 log 10 ( f ) (6) of clutter. The limitations on Hata Model due to range of test results from carrier frequency 150Mhz to 1500Mhz, the B. Plane Earth Propagation Model distance from the base station ranges from 1Km to 20Km, the The free space propagation model does not consider the height of base station antenna (hb) ranges from 30m to 200m effects of propagation over ground. When a radio wave and the height of mobile antenna (hm) ranges from 1m to propagates over ground, some of the power will be reflected 10m. Hata created a number of representative path loss due to the presence of ground and then received by the mathematical models for each of the urban, suburban and receiver. Determining the effect of the reflected power, the open country environments, as illustrated in equations (8-10), free space propagation model is modified and referred to as respectively. the ‘Plain-Earth’ propagation model. This model better represents the true characteristics of radio wave propagation Path Loss for urban clutter: over ground. The plane earth model computes the received Lp (urban )=+ 69.55 26.16log10 ( f ) -13.82log 10 ( hb ) signal to be the sum of a direct signal and that reflected from -ah(mb )+ (44.9 - 6.55log10 ( h ) )log 10 ( d ) (8) a flat, smooth earth. The relevant input parameters include the antenna heights, the length of the path, the operating frequency and the reflection coefficient of the earth. This ah(mm )= (1.1log10 ( f ) - 0.7) h - (1.56log 10 ( f ) - 0.8) (9) coefficient will vary according to the terrain type (e.g. water,
Path loss for suburban clutter: N: Number of Measured Data Points
Lpp( suburban )= L ( urban ) - 2{log10 ( f / 28)}² -5.4 (10) The MSE was found 113.459dB but the acceptable range is up to 6 dB [6]. Therefore, the MSE is subtracted from the
Hata equation for open area and the modified equation will be Path loss for the open country is : as following:
2 LppModified()()-4.78{log()} open area= L urban10 f Lpp( open country )=+ L ( urban )-4.78{log10 ( f )}² (13) (11) +−18.33log (f ) - 40.94 113.459 18.33log10 (f ) - 40.94 10
Hata model is not suitable for micro-cell planning where The modified result of Hata equation in open area is shown antenna is below roof height and its maximum carrier in Fig. 2 using modified equation and the MSE in this case is frequency is 1500MHz. It is not valid for 1800 MHz and less then 6dB, which is acceptable [6]. 1900 MHz systems.
IV. RESULTS AND DISCUSSIONS To generate measurements of signal strength level for downlink and uplink at coverage areas for a cell in the road of Salalah, OMAN, TEMS tools were used. However, the road of Salalah can be considered as an open area and therefore equations (8), (9) and (11) of Okumura-Hata model were used. After determining the path loss of the practical measurements for each distance, the study was carried on in order to make a comparison between the experimental and theoretical values and the result is shown in Fig-1. Fig. 2: Modified Hata's open area equation path loss versus distance
In order to verify that the modified Hata's open area equation (13) is applicable for other open areas in Oman, another data generated from TEMS tool for another cell in the road of Salalah has been used. Based on that practical data, the propagation path loss and the distance have been re-verified for another cell.
Theoretical simulation and the obtained experimental data is compared and analyzed further using a cubic regression model on the set of the experimental data which is giving acceptable results. After observation of experimental data, it can be predicted that the scatter plot of the experimental data Fig. 1: Theoretical and Experimental path loss versus distance on path loss verses distance reveals a third order polynomial trend. Therefore the Cubic Regression Model was fitted using From the above plot, the results clearly show that the method of least squares which estimates the parameters by measured path loss is less than the predicted path loss by a minimizing sum of squares of the white noise. The estimated difference varying from 4 to 20 dB. However, there are model is given as below: several reasons which may cause those significant differences. First of all, in Japan there are few areas virtually 98.66 6.8 7.0 4.0 satisfying the open area conditions; and if any, they are (14) narrow. Because of that reason Okumura selected the value The coefficient of determination of this regression for urban area as standard for open area [5]. Moreover, the suggested that about 90% variation in path loss can be geographical situation of Japan is different from that in Oman explained by distance using the above equation (14). The due to geographical differences. Then, mean square error correlation between experimental, theoretical model and (MSE) was calculated between measured path loss value and fitted by cubic regression were worked out by Pearson those predicted by Hata model using the following equation Correlation Coefficient. These correlations are presented in [6]: Table 1 below. This table shows that the path loss estimated 2 MSE=−−() P P/( N 1) (12) by the equation (14) has highly significant correlation of (∑ mr ) 0.975 with the experimental data and a correlation of 0.974 with the theoretical model. The correlation between Where; experimental data and theoretical model was 0.948 which is Pm: Measured path loss (dB) also highly significant at p<0.01 value. Pr: Predicted path loss (dB)
Table1. Pearson Correlation Coefficient The measured data that provided by OmanMobile just Experimental Theoretical Cubic covered the open area. Therefore, measurements for other Experimental 1 0.948** 0.974** areas (urban and suburban) should be obtained in order to Theoretical 0.948** 1 0.975** compare all results (urban, suburban and open areas) with the Cubic 0.974** 0.975** 1 predicted data based on Okumura-Hata model for Oman. ** Correlation is significant at 99% level. Also, if more detailed environmental information is included Hata propagation model, Fig.3 shows the theoretical, in the model, better prediction results might be achieved. experimental and cubic regression plots showing good Cubic regression gave us also the missing experimental agreement. points showing a good agreement with Theoretical model.
ACKNOWLEDGMENT The authors would like to express their thanks to Mohammed Dhawyani, Qais Mahrooqi, Faisal Rahbi, Ali Kalbani for their valuable contribution.
REFERENCE: [1] D. Nobel, “The history of land to mobile radio communications,” IEEE Transactions on Vehicular Technology, pp. 1406-1416, May 1962. [2] V. H. MacDonald, “The cellular concept,” The Bell Systems Technical Journal, vol. 58, no. 1, pp. 15-43, January 1979. [3] A. Medeisis and A. Kajackas, “On the Use of the Fig. 3: path loss versus distance for Experimental, Theoretical and Cubic Universal Okumura-Hata Propagation Predication Regression data points Model in Rural Areas”, Vehicular Technology
Conference Proceedings, VTC Tokyo, Vol.3, pp. By calculating the MSE for the second cell, it was found to 1815-1818, May 2000. be (3.2058dB) which is acceptable [6]. Therefore, the [4] S. R. Saunders M. Hata, “Empirical Formula for modified Hata's open area equation (13) was re-verified in Propagation Loss in Land Mobile Radio Services”, IEEE order to be applicable in other open areas in Oman. Transactions on Vehicular Technology, vol VT 29 August 1980. V. CONCLUSION [5] R. D. Wilson and R. A. Scholtz, “Comparison of CDMA This work was focused for predicting the mean signal and Modulation Schemes for UWB Radio in a Multipath strength in different areas. However, most propagation Environment”, Proceedings of IEEE Global models aim to predict the median path loss. But, existing Telecommunications Conference, Vol. 2, Dec 2003. predictions models differ in their applicability over different [6] J. Wu and D. Yuan, “Propagation Measurements and terrain and environmental conditions. Although there are Modeling in Jinan City”, IEEE International Symposium many predictions methods based on deterministic processes on Personal, Indoor and Mobile Radio Communications, through the availability of improved databases, but the Boston, MA, USA, Vol. 3, pp. 1157-1159, 8-11 Okumura-Hata model is still mostly used [7]. That is because September 1998. [7] Y. Okumura et al., Field Strength and Its Variability in of the ITU-R recommendation for its simplicity and its VHF and UHF Land-Mobile Radio Service, Review of proven reliability. the Electrical Communications Laboratory, Vol. 16, no. The effects of terrain situation predicted at 900MHz were 9-10, September-October 1968. analyzed. Results of radio signals propagation measurements [8] Z. Nadir, N. Elfadhil, F. Touati , “Pathloss determination for an open area in Oman were compared to those predicted using Okumura-Hata model and spline interpolation for based on Okumura-Hata model. However, the Okumura-Hata missing data for Oman” World Congress on propagation model might not be fully adapted in Oman Engineering, IAENG-WCE-2008, Imperial College, because there is no rain attenuation impact in Oman London, United Kingdom, 2-4 July,2008. environment due to lack of rain. Therefore, further improvement of Okumura-Hata model in the open area has been suggested. This improvement was achieved by using mean square error (MSE) between measured and predicted path loss values in order to provide sufficient MSE for radio prediction The modified equation (13) was verified for a cell in another open area and the MSE was found to be 3.21dB which is accepted value for signal predication [6].
HATA-OKUMURA MODEL
The Hata-Okumura model is best suited for large cell coverage (distances up to 100 km) and it can extrapolate predictions up to the 2GHz band. This model has been proven to be accurate and is used by computer simulation tools. Here is the analytical approach to the model:
PL = 69.55 + 26.16 log (f) - 13.82 log (ht) - a (hm) + [44.9 - 6.55 log (ht)] log (d) dB
a (hm) = [1.1 log(f) - 0.7] hm - [1.56 log(f) - 0.8] dB for midsize city
where f - operating frequency (MHz)
ht - transmitting station antenna height (m)
hm - mobile unit antenna height (m)
a(hm) - correction factor for mobile unit antenna height (dB)
d - distance from transmitting station
Using the following parameters: f = 1500 MHz, ht = 40 m, hm = 1.5 m, the loss predictions for this model is shown graphically in Figure 1.1.
Figure 1.1 Hata-Okumura model
Reference: 1. Y. Okumura, E. Ohmori, T. Kawano, and K. Fukuda, "Field Strength and Its Variability in VHF and UHF Land-Mobile Radio Service," Review of the Electrical Communication Laboratory, 16, pp. 825-873, September-October, 1968. 2. http://wcrg.engr.ucf.edu
Wireless Applications Corp. 11 1 - 108 th Ave NE, #160 Bellevue, WA 9800 4 www.wirelessapplications.com 1 Okumura and Hata Macroscopic Propagation Models
In this section Okumura and Hata propagation models are discussed. The two models or their modified version are frequently used throughout commercially available RF engineering tools.
1.1 Okumura Propagation Model
Okumura’s model is one of the most frequently used macroscopic propagation models. It was developed during the mid 1960's as the result of large-scale studies conducted in and around Tokyo. The model was designed for use in the frequency range 200 up to 1920 MHz and mostly in an urban propagation environment.
Okumura’s model assumes that the path loss between the TX and RX in the terrestrial propagation environment can be expressed as:
L50 = LFS + Amu + H tu + H ru (1) where:
L50 - Median path loss between the TX and RX expressed in dB
LFS - Path loss of the free space in dB
Amu - “Basic median attenuation” – additional losses due to propagation in urban environment in dB
Htu - TX height gain correction factor in dB
H xu - RX height gain correction factor in dB
The free space loss term can be calculated analytically using:
æ d ö æ f ö ç ÷ ç ÷ LFS = 32.45 + 20logç ÷ + 20logç ÷ -10log (Gt ) -10log (Gr ) (2) è1km ø è1MHz ø where: d - Distance between the TX and RX in km f - Operating frequency in MHz
Gt ,Gr - TX and RX antenna gains (linear)
The remaining terms on the right hand side of (1) are provided in a graphical form as the family of curves.
1 Basic Median Attenuation ( Amu ) This term models additional propagation losses due to the signal propagation in a terrestrial environment. The curves for determining the basic median attenuation are provided in Figure 1. On the horizontal axis of the graph in Figure 1, we find operating frequency expressed in MHz. On the vertical axis we find the additional path loss attenuation expressed in dB. The parameter of the family of the curves is the distance between the transmitter and receiver. The curves in Figure 1 were derived for TX height reference of 200m and RX height of 3m. If the actual heights of the TX and RX differ from those referenced, the appropriate correction needs to be added. For example, at 850MHz frequency and the transmitter-receiver distance of 5km, the attenuation is close to 26dB. This value is read from the leftmost scale in Figure 1 at the point where constant vertical line at 850MHz intersects with the parametric 5km distance curve. The projection of this intersection on the basic median attenuation scale gives the resulting attenuation of approximately 26dB.
Figure 1. Basic median attenuation as a function of frequency and path distance. After Okumura [6].
2 Base station height gain ( Htu ) and mobile height gain factor ( Hru )
The curves used for correction of nonstandard transmitter and receiver heights are presented in Figure 2 and Figure 3. Figure 2 shows the correction factor if the base station antenna is not 200m high. At the effective height of 200m, all curves meet and no correction gain is required (Htu =0dB). Base station antennas above 200m introduce positive gain in the Okumura model given by equation 1 and antennas lower than 200m have negative gain factor. The parameter is again the distance between the transmitter and the receiver, similar to Figure 1. For example, for 100m antennas and 1km distance, the base station antenna gain Htu is approximately –4dB.
Figure 3 is interpreted similarly for the mobile antenna height correction. All curves meet at the referent 3m horizontal coordinate. Higher antennas introduce gain and lower cause loss of referent signal level. The parameter for this family of curves is not the distance between the base and mobile station as in Figure 2, but frequency. For example, a 5m high antenna operating at 800MHz will have approximately Hru =2dB gain relative to the referent 3m antenna in the large city. Mobile height gain factor is also separated according to the size of the city in two clusters in Figure 3: medium and large city. If the same mobile antenna (5m, 800MHz) is deployed in a medium city, the height gain factor is increased from 2dB to 6dB.
Figure 2. Base station height correction gain – after Okumura [4]
3
Figure 3. Mobile station height correction gain – after Okumura [4]
One should notice that the base station correction factor is provided as a function of the effective height of the transmitter antenna. The effective antenna height is calculated as the height of the antenna’s radiation above the average terrain. The terrain is averaged along the direction of radio path over the distances between three and fifteen kilometers. The procedure is easily be explained with the aid of Figure 4. First, a terrain profile is determined from the TX and in the direction of the receiver. The terrain values along the profile that fall between 3 km and 15 km are averaged to determine the height of the average terrain. Finally the effective antenna height is determined as the difference between the height of the BTS antenna and the height of the average terrain.
Height of the Average Terrain Radiation Center Line Effective Antenna Height
distance 0 3 km 15 km
Figure 4. Calculation of the effective antenna height for the Okumura model
4 In his model Okumura provides some additional corrections in graphical form. For example, corrections for street orientation, general slope of the terrain, mixture of land and sea can be used to enhance the model’s accuracy. However, in practice these corrections are seldom used.
Example 1 Let us use Okumura model to determine the received signal level 2.3 miles from the site operating at 870MHz. The following numerical data is given:
Radiation centerline of the BTS transmitter: hbts = 40m
Height of the mobile receive antenna: hm = 3m
Terrain elevation at the location of the BTS: Ebts = 340m
Average height of the terrain in the area: Eterrain = 312m
Power delivered to the BTS antenna: PBTS =19.5W
BTS antenna gain: 10log (Gt ) =10dB
MS antenna gain: 10log (Gm ) = 0dB
The free space loss between the TX and RX can be calculated as:
LFS = 32.45 + 20log (2.3×1.609) + 20log(870) -10 = 92.61dB
The basic median attenuation is determined from Figure 1 as:
Amu = 24dB
The effective height of the BTS transmitter is given as:
hte = 40 + 340 - 312 = 68m
Correction for the base station height gain can be determined from Figure 2 as:
H tu » -9dB
The total path loss between the transmitter and receiver (including the antenna gains) is given as:
L50 = 92.61+ 24 +9 =129.61dB
The received signal level is obtained as:
RSL =10log(19.5×1000)-125.61 = -82.7dBm
5 1.1.1 Use of Okumura’s model in computer predictions
Due to its simplicity and the fact that it is one of the first models developed for the mobile cellular propagation environment, Okumura’s model is one of the most widely used models. However, there are some difficulties associated with its use for computer based predictions. Some difficulties are:
1. For the model to be used in the computer environment, the curves in Figures 1 to 3 need to be digitized and provided in the form of look-up tables. 2. The whole empirical nature of the Okumura model means that its applicability is limited to parameter ranges used in the model development. If the actual values of the parameters are outside the range, the curves need to be extrapolated. Whether this is a reasonable course of action depends on the particular circumstances. 3. Use of the effective antenna height is limited to the cases of large cell radii. If the cell radius is smaller than 3 km, the use of effective antenna height does not seem appropriate. 4. If the average height of the terrain is above the height of the radiation centerline, the effective antenna height may become negative.
The above concerns and difficulties associated with Okumura’s model have led to numerous modifications and adjustments. Almost every RF tool using this model has its own interpretations and adjustments, that address the specifics associated with computer modeling.
1.2 Hata-Okumura propagation model
In an attempt to make the Okumura’s model easier for computer implementation Hata has fit Okumura’s curves with analytical expressions. This makes the computer implementation of the model straightforward. Hata’s formulation is limited to some values of input parameters.
Hata’s model for RSL prediction and the range of parameters for its applicability is given as:
æ h ö ç be ÷ RSL p = Pt +Gt -69.55 - 26.16log ( f ) +13.82logç ÷ +a (hm ) è h0 ø (3) æ æ h öö æ R ö - ç44.9 - 6.55logç be ÷÷logç ÷ + A + DL ç ç h ÷÷ ç R ÷ a è è 0 øø è 0 ø where:
RSLp Received Signal Level in dBm
Pt Transmitted power in dBm
6 G t Transmit antenna gain in the direction of the receiver in dB f Operating frequency MHz hbe Effective base station antenna height in m h0 Reference base station antenna height, selected as 1m. a(hm ) Mobile antenna height correction in dB R Distance between the bin and the transmitter in km
R0 Reference distance. In Hata model it is always set to 0.62 miles (1km) DL Diffraction losses in dB
Aa Area adjustment factor in dB
The mobile antenna height correction factor is computed as:
1. For a small city and medium size city:
a (hm ) = (1.1log ( f ) - 0.7)hm - (1.56log ( f ) -0.8) (4)
2. For a large city
2 a (hm ) = 8.29(log (1.54hm )) -1.1 f £ 200 MHz (5) 2 a (hm ) = 3.2(log(11.75hm )) - 4.97 f ³ 400 MHz (6)
The area correction factor can be computed as:
1. For suburban areas:
2 é æ f öù Aa = 5.4 + 2´ êlogç ÷ú dB (7) ë è 28øû
2. For open areas:
2 Aa = 4.78(log f ) -18.33log f + 40.94 dB (8)
The Hata model was derived for the following values of the system parameters:
150 MHz £ fc £1500 MHz (9)
30 ft £ hbe £ 200 ft
1 m £ hm £10m 1km £ R £ 20 km
7 Hata implementation of the Okumura’s model can be found in almost every RF propagation tool in use today. However, there are some aspects of its application that a user has to be aware of:
1. The Hata model was derived as a numerical fit to the propagation curves published by Okumura. As such, the model is somewhat specific to Japan’s propagation environment. In addition, terms like “small city”, “large city”, “suburban area” are not clearly defined and can be interpreted differently by people with different backgrounds. Therefore, in practice, the area adjustment factor should be obtained from the measurement data in the process of propagation model optimization. 2. In the Okumura’s original model, the effective antenna height of the transmitter is calculated as the height of the TX antenna above the average terrain. Measurements have shown several disadvantages to that approach for effective antenna calculation. In particular, Hata’s model tends to average over extreme variations of the signal level due to sudden changes in terrain elevation. To circumvent the problem, some prediction tools examine alternative methods for calculation of the effective antenna height.
Example 2. Consider the prediction problem described in Example 1. Assume that the propagation is an urban area of a small city.
The mobile antenna height gain can be obtained as:
a (hm ) = (1.1log (870) -0.7)×3 - (1.56log(870) - 0.8) = 3.81dB
Since the predictions are done in an urban area, no area correction is needed. Therefore,
Aa = 0
The effective height of the BTS transmitter is given as:
hte = 40 + 340 - 312 = 68m
Finally, the received signal level is computed as:
RSL = 10 log(19.5 × 1000 ) + 10 - 69.55 - 26 .16 log(870 ) + 3.81 + 13.82 log(68)
- (44 .9 - 6.55 log(68 ))log(2.3× 1.609 ) = -83.01 dBm
Comparing the result with the value obtained in Example 1.1, we see that the difference is negligible.
8 EE359 – Lecture 2 Outline
Announcements 1st HW posted tomorrow, due next Thursday at 5pm. Discussion section starts next week (Monday) Lecture will end today at 10:30: OHs today 5-6 for me Review of Last Lecture Signal Propagation Overview TX and RX Signal Models Complex baseband models Path Loss Models Free-space Path Loss Ray Tracing Models Simplified Path Loss Model Empirical Models Lecture 1 Review
Course Information Wireless Vision Technical Challenges Multimedia Requirements Current Wireless Systems Spectrum Regulation and Standards
Emerging systems will be covered in the last lecture of the quarter Propagation Characteristics
Path Loss (includes average shadowing) Shadowing (due to obstructions) Multipath Fading
Slow Fast P Pr/Pt t P Very slow r v
d=vt d=vt Path Loss Modeling
Maxwell’s equations Complex and impractical Free space path loss model Too simple Ray tracing models Requires site-specific information Simplified power falloff models Main characteristics: good for high-level analysis Empirical Models Don’t always generalize to other environments Free Space (LOS) Model
d=vt
Path loss for unobstructed LOS path Power falls off : Proportional to 1/d2 Proportional to l2 (inversely proportional to f2) Ray Tracing Approximation
Represent wavefronts as simple particles Geometry determines received signal from each signal component Typically includes reflected rays, can also include scattered and defracted rays. Requires site parameters Geometry Dielectric properties Two Path Model
Path loss for one LOS path and 1 ground (or reflected) bounce Ground bounce approximately cancels LOS path above critical distance Power falls off Proportional to d2 (small d) 4 Proportional to d (d>dc) Independent of l (f) General Ray Tracing
Models all signal components Reflections Scattering Diffraction
Requires detailed geometry and dielectric properties of site Similar to Maxwell, but easier math. Computer packages often used Simplified Path Loss Model
Used when path loss dominated by reflections. Most important parameter is the path loss exponent , determined empirically.
d0 Pr Pt K , 2 8 d Empirical Channel Models
Cellular Models: Okumura model and extensions: Empirically based (site/freq specific) Awkward (uses graphs) Hata model: Analytical approximation to Okumura Cost 231 Model: extends Hata to higher freq. (2 GHz) Walfish/Bertoni: extends Cost 231 to include diffraction
WiFi channel models: TGn Empirical model for 802.11n developed within the IEEE tandards committee. Free space loss up to a breakpoint, then slope of 3.5. Breakpoint is empirically-based. Commonly used in cellular and WiFi system simulations Main Points
Path loss models simplify Maxwell’s equations
Models vary in complexity and accuracy
Power falloff with distance is proportional to d2 in free space, d4 in two path model
Main characteristics of path loss captured in simple model Pr=PtK[d0/d]
Empirical models used in simulations Low accuracy (15-20 dB std) Capture phenomena missing from formulas Can be awkward to use in analysis EE359 – Lecture 3 Outline
Announcements HW posted, due Thursday 5pm Discussion section starts tomorrow, 4-5pm, 4-5 pm in 380-380Y TA OHs start this week Log Normal Shadowing Combined Path Loss and Shadowing Cell Coverage Area Model Parameters from Measurements Lecture 2 Review
Signal Propagation Overview Ray Tracing Models Free Space Model Power falloff with distance proportional to d-2 Two Ray Model Power falloff with distance proportional to d-4 g Simplified Model: Pr=PtK[d0/d] , 2g8. Captures main characteristics of path loss Empirical Models Shadowing
Xc Models attenuation from obstructions Random due to random # and type of obstructions Typically follows a log-normal distribution dB value of power is normally distributed m=0 (mean captured in path loss), 4 Decorrelates over decorrelation distance Xc Combined Path Loss and Shadowing Linear Model: y lognormal g P d r K 0 y 10logK Slow Pt d P /P r t Very slow (dB) -10g log d dB Model P d r (dB) 10log10 K 10g log10 y dB, Pt d0 2 y dB ~ N(0,sy ) Outage Probability and Cell Coverage Area P Path loss: circular cells r Path loss+shadowing: amoeba cells Tradeoff between coverage and interference Outage probability Probability received power below given minimum Cell coverage area % of cell locations at desired power Increases as shadowing variance decreases Large % indicates interference to other cells Model Parameters from Empirical Measurements K (dB) 2 sy P (dB) Fit model to data r 10g log(d) log(d ) Path loss (K,g), d0 known: 0 “Best fit” line through dB data K obtained from measurements at d0. Exponent is MMSE estimate based on data Captures mean due to shadowing Shadowing variance Variance of data relative to path loss model (straight line) with MMSE estimate for g Main Points Random attenuation due to shadowing modeled as log-normal (empirical parameters) Shadowing decorrelates over decorrelation distance Combined path loss and shadowing leads to outage and amoeba-like cell shapes Cellular coverage area dictates the percentage of locations within a cell that are not in outage Path loss and shadowing parameters are obtained from empirical measurements Wireless Communication Channels: Large-Scale Pathloss Path Loss Models Path-Loss Models The most general case of signal reception might consist of a direct path, reflected paths, diffracted paths, and scattered paths (which makes mathematical analysis cumbersome) Path-Loss models are empirical models that are based on fitting curves or analytical expressions that recreate a set of measured data Note: A given empirical model might only be valid within the environment where the measurements used to estimate such model have been taken © Tallal Elshabrawy 3 Log-Distance Path-Loss Model Theoretical and Measurement-based Propagation suggest that the average received signal power decreases logarithmically with distance PL (d): Average path-loss for an arbitrary separation n : Path-loss exponent © Tallal Elshabrawy 4 Path-Loss Exponent for Different Environments Environment Path-Loss Exponent n Free-Space 2 Urban area cellular radio 2.7 to 3.5 Shadowed urban cellular radio 3 to 5 In building line-of-sight 1.6 to 1.8 Obstructed in building 4 to 6 Obstructed in factories 2 to 3 © Tallal Elshabrawy 5 Log-normal Shadowing Distance between two nodes alone cannot fully explain the signal strength level at the receiver Shadowing has been introduced as a means to model the variation of signal propagation behavior between two different signal paths assuming the same propagation distance PL (d): Path-loss model for an arbitrary separation d Xσ : Shadowing parameter (zero mean Gaussian distributed random variable in dB with standard deviation σ also in dB) © Tallal Elshabrawy 6 Received Power in Path-Loss Models d d P-PL d 4 3 T d d Position Index 1 2 1 2 3 4 © Tallal Elshabrawy 7 Received Power in Path-Loss Models PdR d d X 1 X 4 P-T PL d X 4 3 X 3 2 d d Position Index 1 2 1 2 3 4 © Tallal Elshabrawy 8 Reception Quality PdR d d X 1 X 4 P-T PL d X 4 3 X 3 2 d d Position Index 1 2 1 2 3 4 γ: Desired received power threshold PrPd γ PrXP PL d γ R σ T © Tallal Elshabrawy 9 Probability of Bad Reception Quality PrPd Pr X P PL d RT x2 1 2 2σ X follows a normal distribution with PrXxσ th e d x σ 2 2πσ xth zero mean and standard deviation σ x Let z= f x σ X z2 1 2 PrXx e d z σ th 2 2π xth σ σ xxth1 th PrXxσ th Q erfc σ 2 2σ x PdT PL γ PrPdγ Q xth R σ z2 - 122 2 Note: Q(x )= e dzx erfc( )= e-u d u 2ππxx © Tallal Elshabrawy 10 Percentage of Coverage Area Due to the random effects of shadowing some locations within the coverage area will be below a particular h desired received signal R’ level R So, its better to compute Pd 0 d R' how the boundary R coverage area relates to PdR 0 d R' the percent of area covered within the R: Radius of Coverage Area boundary required for Transmitter © Tallal Elshabrawy 11 Calculation of Percentage of Coverage Area Assume h (height of antenna) is Negligible, then, U(γ) depicting the percentage of area with received signal strength equal to or exceeding γ may be calculated as follows dA r 1 r U γ PrPr γ dA dθ π R 2 R R 1 2 π R U γ PrPr γ rr d dθ 2 R π R 00 PrT PL PrPr Q R R: Radius of Coverage Area required for Transmitter PrT PL 1 PrPrR erfc 2 2 PdnrdPL 10 log 1 T 00 PrPrR erfc 2 2 © Tallal Elshabrawy 12 Calculation of Percentage of Coverage Area PdnrdPL 10 log 1 T 00 PrPrR erfc 2 2 PdnRdnrRPL 10 log 10 log 1 T 00 PrPrR erfc 2 2 1 2π R U γ PrPr γ rr d dθ 2 R πR 00 12R 2π R U γ PrPr γ rr d dθ Pr Pr γ rr d 22RR πRR000 21R r U γ rabrerfc ln d 2 R 0 2 R γ PdnRdT PL00 10 log10ne log ab 22σσ © Tallal Elshabrawy 13 Calculation of Percentage of Coverage Area It can be shown that 1121ab ab U γ 1erfa exp2 1erf 2 bb By choosing the signal level such that PRR γ i.e.,a 0 Therefore for the case when Boundary Coverage = 50 % 11 1 U γ 1exp2 1erf 2 bb © Tallal Elshabrawy 14 Calculation of Percentage of Coverage Area “Wireless Communications: Principles and Practice 2nd Edition”, T. S. Rappaport, Prentice Hall, 2001 © Tallal Elshabrawy 15 Outdoor Propagation Models Longley-Rice Model (Read) Durkin’s Model (Read) Okumura’s Model Hata Model PCS extension to Hata Model Walfisch and Bertoni (Read) © Tallal Elshabrawy 16 Okumura’s Model Okumura’s model is one of the most widely used models for signal predictions in urban and sub-urban mobile communication areas This model is applicable for frequencies ranging from 150 MHz to 1920 MHz It can cover distances from 1 km to 100 km and it can be used for base station heights starting from 30m to 1000m The model is based on empirical data collected in detailed propagation tests over various situations of an irregular terrain and environmental clutter © Tallal Elshabrawy 17 Okumura’s Model LdBL50 F A mu fdGhGhG, te re AREA th L50 is the median value or 50 percentile value of the propagation path loss LF is the free space propagation path loss Amu is the median attenuation relative to free space GAREA is the gain due to the type of environment G(hte) is the base station antenna height gain factor G(hre) is the mobile antenna height gain factor © Tallal Elshabrawy 18 Okumura’s Model: Amu Curves “Wireless Communications: Principles and Practice 2nd Edition”, T. S. Rappaport, Prentice Hall, 2001 © Tallal Elshabrawy 19 Okumura’s Model: GArea Curves “Wireless Communications: Principles and Practice 2nd Edition”, T. S. Rappaport, Prentice Hall, 2001 © Tallal Elshabrawy 20 Okumura’s Model: G(hte), G(hre) The empirical model of Okumura assumed hte = 200m, hre = 3m h te G hte 20log 30m h te 1000m 200 h re G hre 10log h re 3m 3 h re G hre 20log 3m h re 10m 3 © Tallal Elshabrawy 21 Hata Model L50 urban dB 69.. 55 26 26log f c 13 . 82log h te a h re 44.. 9 6 55log hte log d th L50 is the median value or 50 percentile value of the propagation path loss fc (in MHz) is the frequency (15MHz to 1500MHz) hte is the effective transmitter height in meters (30m to 200 m) hre is the effective transmitter height in meters (1m to 10 m) d is the T-R separation in Km a(hre) is the correction factor for effective mobile (i.e., receiver) antenna height which is a function of the size of the coverage area © Tallal Elshabrawy 22 Hata Model: a(hre) For a Medium sized city, correction factor is given by: ah re 11lo....gf07h156lo c re gf c 0 8 dB For a Large city, correction factor is given by: 2 ahre 829lo..g 1 54h re 1 . 1 dB for f c 300MHz 2 ahre 32lo..g 11 75h re 4 . 97 dB for f c 300MHz © Tallal Elshabrawy 23 Hata Model Path loss in suburban area, the equation is modified as 2 L dB L urban 2 log f2854/. 50 50 c For path loss in open rural areas, the formula is modified as 2 L dB L urban 4... 78 log f 18 33log f 40 94 50 50 c c Hata Model is well-suited for Large cell mobile systems © Tallal Elshabrawy 24 PCS Extension to Hata Model An extended version of the Hata model developed by COST-231 working committee for 2 GHz range L50 urban dB 46.. 3 33 9log f c 13 . 82log h te a h re 44.. 9 6 55log hre log d C M fc is the frequency (1500MHz to 2000 MHz) hte is the effective transmitter height in meters (30m to 200 m) hre is the effective transmitter height in meters (1m to 10 m) d is the T-R separation in Km (1 Km to 20 Km) CM=0 dB for medium sized city and suburban areas, CM=3 dB for metropolitan centers © Tallal Elshabrawy 25 Indoor Propagation Models The indoor radio channel differs from the traditional mobile radio channel in the following aspects Much smaller distances Much greater variability of the environment for a much smaller range of T-R separation distances Difficult to ensure far-field radiation Propagation within buildings is strongly influenced by specific features such as Building layout Construction materials Building type Open/Closed doors Locations of antennas © Tallal Elshabrawy 26 Partition Losses (Same Floor) “Wireless Communications: Principles and Practice 2nd Edition”, T. S. Rappaport, Prentice Hall, 2001 © Tallal Elshabrawy 27 Partition Losses between Floors “Wireless Communications: Principles and Practice 2nd Edition”, T. S. Rappaport, Prentice Hall, 2001 © Tallal Elshabrawy 28 Log-Distance Pathloss Model The lognormal shadowing model has been shown to be applicable in indoor environments © Tallal Elshabrawy 29 Ericsson Multiple Breakpoint Model Upper bound on Lower bound on the path-loss the path-loss “Wireless Communications: Principles and Practice 2nd Edition”, T. S. Rappaport, Prentice Hall, 2001 © Tallal Elshabrawy 30 Attenuation Factor Model This was described by Seidel S.Y. It is an in-building propagation model that includes Effect of building type Variations caused by obstacles nSF represents the path-loss exponent for the same floor measurements FAF represents the floor attenuation factor PAF represents the partition attenuation factor for a specific obstruction encountered by a ray drawn between the transmitter and receiver © Tallal Elshabrawy 31 Attenuation Factor Model FAF may be replaced by an exponent that accounts for the effects of multiple floor separation nMF represents the path-loss exponent based on measurements through multiple floors © Tallal Elshabrawy 32