Spectrum Occupancy Measurements and Analysis in 2.4 Ghz WLAN

electronics

Article Spectrum Occupancy Measurements and Analysis in 2.4 GHz WLAN

Adnan Ahmad Cheema 1 and Sana Salous 2,* 1 School of Engineering, Ulster University, Jordanstown BT37 0QB, UK 2 Department of Engineering, Durham University, Durham DH1 3LE, UK * Correspondence: [email protected]; Tel.: +0044-191-33-42532

Received: 26 July 2019; Accepted: 7 September 2019; Published: 10 September 2019

Abstract: High time resolution spectrum occupancy measurements and analysis are presented for 2.4 GHz WLAN signals. A custom-designed wideband sensing engine records the received power of signals, and its performance is presented to select the decision threshold required to define the channel state (busy/idle). Two sets of measurements are presented where data were collected using an omni-directional and directional antenna in an indoor environment. Statistics of the idle time windows in the 2.4 GHz WLAN are analyzed using a wider set of distributions, which require fewer parameters to compute and are more practical for implementation compared to the widely-used phase type or Gaussian mixture distributions. For the omni-directional antenna, it was found that the lognormal and gamma distributions can be used to model the behavior of the idle time windows under different network traffic loads. In addition, the measurements show that the low time resolution and angle of arrival affect the statistics of the idle time windows.

Keywords: cognitive radio; spectrum occupancy; dynamic spectrum access; time resolution; directional; sensing engine

1. Introduction The vision of 5G network (5GN) encapsulates many application areas, e.g. mobile broadband, connected health, intelligent transportation, and industry automation [1]. To entertain such a wide variety of applications, telecom manufacturers and standardization bodies require the 5GN to support a few Gbps data rates and low latency to a fraction of a millisecond. However, the bottleneck to achieve such requirements will depend on a better understanding of the radio propagation channel in the millimeter wave band to meet such critical constraints [2]. Another aspect of the 5GN is to provide coexistence and improve the spectrum utilization below the 6 GHz band by using concepts of the cognitive radio (CR) network [3]. In this paper, a CR module, which can perform sensing and detection of the spectrum holes, is referred to as “Sensing Engine” (SE), and the time to sense a snapshot of the bandwidth is defined as time resolution. In a CR network, unlicensed users can access the spectrum holes in time, frequency and space or any of their combinations [4–6], provided they cause no interference [4,7]. Opportunistic spectrum access [8] broadly defines the approaches which can enable unlicensed users to find spectrum holes when licensed users are not active. These approaches will improve spectrum utilization and overcome emerging spectrum demands. In future wireless networks, these approaches will be very important to provide spectrum access in networks like Internet of Things [9,10], 5G [11], device-to-device [12], and drones assisted [13]. However, one of the fundamental challenges is to reliably detect spectrum holes and develop models to predict their occurrence along with the idle time windows (ITWs), a continuous fraction of time when the licensed users of the network are not active. These models will be helpful to decide the optimum spectrum allocation based on unlicensed users’ requirements (e.g. data rates) and/or to improve spectrum utilization.

Electronics 2019, 8, 1011; doi:10.3390/electronics8091011 www.mdpi.com/journal/electronics Electronics 2019, 8, 1011 2 of 14

The 2.4 GHz industrial, scientific and medical (ISM) band is widely used to provide wireless communication using technologies like WLAN, Bluetooth and Zigbee. Several studies [14–18] in this band have demonstrated that spectrum utilization is very low and this can be helpful to meet spectrum demands of future wireless networks by using concepts of opportunistic spectrum access. However, considering the 2.4 GHz ISM band specifications where the signal duration can be on the order of 100–200 µsec with the narrowest frequency resolution bandwidth requirement as low as 1 MHz [19], an accurate characterization of ITWs is a fundamental challenge. In this paper, our focus is to model the ITWs in the 2.4 GHz WLAN, which is widely-used technology in public and private networks to connect licensed users, by conducting high-resolution spectrum occupancy measurements according to band specifications. It is expected that these measurement-based models will be useful for users in future wireless networks to access 2.4 GHz WLAN spectrum without causing any interference. For opportunistic spectrum access in the 2.4 GHz WLAN band, a two-state (idle/busy) continuous-time semi-Markov model was proposed in Refs. [6,20,21] where empirical distributions of the ITWs were fitted with the exponential (EX), generalized Pareto (GP) and phase-type distributions (e.g., Hyper Erlang). Although phase type distributions, which are complex to compute due to the high number of parameters, provide an excellent fit, comparatively less complex distributions like the GP can also provide a good fit. In Refs. [6,20], a narrowband vector signal analyzer (VSA) was used to perform high time resolution measurements in a 2.4 GHz WLAN channel, where data packets were generated artificially to stimulate network traffic in an interference controlled environment. This approach facilitates the identification of ITW from a known pattern of transmitted data packets. In Ref. [21], four narrowband sensors were used to monitor the 2.4 GHz WLAN traffic. To sense all 2.4 GHz WLAN channels, the span was divided into 16 channels and each channel was traversed sequentially after 8.192 seconds. Due to this long traverse time, concurrent and continuous network traffic is not possible to monitor in all channels. In Refs. [22,23], a spectrum analyzer (SA) was used to conduct low time resolution (a second or more) measurements in the 2.4 GHz WLAN band where Geometric and GP distributions, respectively, provide an excellent fit for the ITWs. It is important to highlight that due to such low time resolution, 2.4 GHz WLAN signals smaller than the time resolution will not be detected, which will lead to unrealistic longer ITWs and relating models will generate interference to licensed users. In Ref. [24], high time resolution measurements were performed in the 2.4 GHz WLAN band, and Gaussian mixture distribution (based on 4 components) was found to provide an excellent fit for ITWs. However, this distribution required a higher number of parameters for computation. Table1 provides a summary of the measurements in the 2.4 GHz WLAN band and distributions used to model the ITWs for a two-state continuous time semi-Markov model. The order of the distribution is provided from excellent fit (marked in bold) to worst. In this paper, we model the statistics of ITW for 2.4 GHz WLAN signals, recorded in real network traffic at high time resolution using a custom-designed SE [18]. In addition, statistics of the ITWs are modelled using numerically simple distributions (e.g. Weibull (WB), Gamma (GM) and lognormal (LN)) which require fewer parameters and are more practical for implementation compared to phase type or Gaussian mixture distributions. For comparison with existing work, the GP distribution was used, which also required few parameters and was found to provide good fit in most cases. Moreover, the effect of using different time resolutions on the statistics of ITW are investigated. Electronics 2019, 8, 1011 3 of 14

Table 1. Summary of the existing measurements to model ITWs in the 2.4 GHz WLAN band.

Time Spectrum Distribution(s) for Ref. SE and Bandwidth Network Traffic Resolution Sensing ITW [6] High VSA and N Artificial P and F GP [20] High VSA and N Artificial P and F HE, GP, EX [21] Low TMote Sky and N Real P HX, HE, GP, EX Geometric, Bernoulli, [23] Low SA and W Real P LN [22] Low SA and W Real P GP, EX, GM, LN, WB [24] High SDR and N Artificial and Real P G, GP, HE and EX Ref.: reference; N: Narrowband; W: Wideband; P: power; F: Feature-based; HE: Hyper-Erlang; HX: Hyper-Exponential; G: Gaussian mixture; SDR: software defined radio.

Directional antennae were used in Refs. [25–27] to find the effect of the angular dimension on the spectrum occupancy. These measurements were conducted using considerably low time resolution per antenna or angle, ranging from 6 seconds to over a minute, which makes it difficult to capture short duration signals and can lead to unrealistic ITW. To the best of our knowledge, these are the only measurement-based papers, which investigate the effect of angular dimension on the spectrum occupancy. However, no further analysis of the statistics of ITWs was provided. In this paper, we also investigate the effect of the angular dimension on the statistics of the ITW. Although previously different SEs and distributions were used to model the statistics of ITW, they have the following shortcomings:

The empirical distribution of ITW based on the high time resolution measurements was previously • modelled using phase type or Gaussian mixture distributions which require higher parameters for computation and are not feasible for implementation. In addition, low time resolution-based measurements tend not to detect 2.4 GHz WLAN signals, which leads to unrealistic statistics of ITWs. Moreover, the statistics of the ITW were not presented for individual WLAN channels which could have different distributions for ITW. In this paper, in addition to the GP distribution, which was found to provide good fit compared to phase type or Gaussian mixture distributions and required few parameters for computation, similar numerically simple distributions like WB, GM, EX, and LN are also tested and shown to provide appropriate fit under certain traffic load conditions. The analysis is also presented for each of the concurrently measured WLAN channels. This work also investigates the effect of time resolution on the statistics of the ITW, which is vital • to understand the accuracy of the width of the ITW in relation to set time resolution of the SE. This work first investigates the effect of the angular dimension and time resolution per angle • on the presence of 2.4 GHz WLAN signals. Then, using the OR hard combining technique the statistics of the ITW per WLAN channel are also analyzed.

In this paper, Section2 provides a short introduction of the custom-designed SE and related performance. The measurements setup is provided in Section3 followed by the data analysis methodology in Section4. The results of the measurements using an omni-directional antenna are discussed in Section5. In Section6, the e ﬀect of the angular dimension on the ITW is presented with conclusions in Section7.

2. SE and Performance in 2.4–2.5 GHz The SE was developed at Durham University and can operate in two frequency bands: (a) 0.25–1 GHz and (b) 2.2–2.95 GHz with a time resolution as high as 204.8 µsec. The logged in data from the SE can be monitored online or stored for offline processing where further filtering is applied followed by a double Fast Fourier transform (FFT) to compute the received power. The architecture and implementation details of the SE can be found in Ref. [18]. Electronics 2019, 8, x FOR PEER REVIEW 4 of 14 Electronics 2019, 8, 1011 4 of 14 by a double Fast Fourier transform (FFT) to compute the received power. The architecture and implementation details of the SE can be found in Ref. [18]. BeforeBefore performing performing measurements measurements in in the the desired desired band, band, the the SE SE was was calibrated calibrated for for all all the the gains gain ands and losses,losses, which which is is essential essential to getto get the the correct correct value value of the of receivedthe received power. power. To quantify To quantify the sensitivity the sensitivity and instantaneousand instantaneous dynamic dynamic range (IDR)range of (IDR) the SE, of athe continuous SE, a continuous wave (CW) wave signal (CW) was signal fed into was the fed SE into via anthe attenuatorSE via an which attenuator was gradually which increasedwas gradually until the increased received signaluntil couldthe received not be distinguished signal could from not the be noisedistinguished floor. The datafrom were the noise logged floor. in at The 80 MHzdata were with alogged time resolution in at 80 MHz of 204.8 withµ seca time for resolution 100 MHz sensing of 204.8 bandwidthµsec for 100 centered MHz sensing at 2.45 bandwidth GHz. The rawcentered data at were 2.45 filtered GHz. The using raw a high-orderdata were filtered Gaussian using window a high- toorder get 400 Gaussian kHz frequency window to resolution get 400 kHz bandwidth. frequency Figure resolution1 shows bandwidth. the received Figure signal 1 shows power the against received thesignal detected power signal against to noise the ratiodetected (SNR) signal in the to sensed noise bandwidth.ratio (SNR) Tablein the2 summarizessensed bandwidth. the measured Table 2 performancesummarizes parameters the measured of the performance SE where the parameters noise figure of (NF), the theSE diwherefference the between noise figure the theoretical (NF), the anddifference measured between noise floor,the theoretical was found and to be measured about ~11 noise dB. Thefloor, measured was found sensitivity to be about was found~11 dB. to beThe 95 dBm (with at least 12 dB SNR) and an IDR value of 33 dB was achieved. These performance −measured sensitivity was found to be -95 dBm (with at least 12 dB SNR) and an IDR value of 33 dB parameterswas achieved. are helpfulThese performance to find the noise-free parameters region are helpful for spectrum to find holesthe noise detection.-free region More for details spectrum are providedholes detection in Sections. More5 and details6. are provided in Sections 5 and 6.

SNR (dB) 15

2.4 - 2.5 GHz

5 -95 -90 -85 -80 -75 -70 -65 -60 -55 received power (dBm) Figure 1. Received Power versus SNR in 2.4–2.5 GHz. Figure 1. Received Power versus SNR in 2.4–2.5 GHz. Table 2. Performance parameters of SE in 2.4–2.5 GHz. Table 2. Performance parameters of SE in 2.4–2.5 GHz. Centre Frequency Bandwidth Noise Floor Sensitivity NF dB IDR dB CentreMHz Frequency MHzBandwidth dBmNoise Floor NFdBm Sensitivity SNR (dB) IDR MHz MHz dBm dB dBm SNR (dB) dB 2450 100 107.03 10.95 95 12.03 33.32 2450 100 − −107.03 10.95− −95 12.03 33.32

3. Measurement Setup 3. Measurement Setup To analyze the occupancy of the 2.4 GHz WLAN signal, the bandwidth of the SE was conﬁgured To analyze the occupancy of the 2.4 GHz WLAN signal, the bandwidth of the SE was configured to 100 MHz centered at 2.45 GHz with a time resolution of 204.8 µsec. A custom-designed wideband to 100 MHz centered at 2.45 GHz with a time resolution of 204.8 µsec. A custom-designed wideband omni-directional discone antenna, placed at 1.5 m above ground, as shown in Figure2a, was used omni-directional discone antenna, placed at 1.5 m above ground, as shown in Figure 2a, was used in in the measurements. The measurements were taken during working hours from 01:15 pm to 01:35 the measurements. The measurements were taken during working hours from 01:15 pm to 01:35 pm pm in an indoor environment. For the directional measurements, three commercial log periodic in an indoor environment. For the directional measurements, three commercial log periodic vertically-polarized antennae (see Figure2b) were used with beam widths of 55 degrees. The antennae vertically-polarized antennae (see Figure 2b) were used with beam widths of 55 degrees. The were placed at 1.5 m above ground with an angular separation of 90 degrees. Due to having three antennae were placed at 1.5 m above ground with an angular separation of 90 degrees. Due to having antennae, switching at 204.8 µsec, the time resolution per antenna or angle was 819.2 µsec, which three antennae, switching at 204.8 µsec, the time resolution per antenna or angle was 819.2 µsec, corresponds to the switching time between three antennae, and an additional reference sweep was taken which corresponds to the switching time between three antennae, and an additional reference sweep to get the correct antenna switching sequence in each data ﬁle. The measurements were performed in was taken to get the correct antenna switching sequence in each data file. The measurements were the same environment from 05:20 pm to 05:40 pm. performed in the same environment from 05:20 pm to 05:40 pm.

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Discone antenna

Workstation

(a) (b) Figure 2. Measurement setup: (a) discone antenna, (b) log periodic antenna. Figure 2. Measurement setup: (a) discone antenna, (b) log periodic antenna.

At theAt beginning the beginning of each measurement, of each measurement, the radio frequency the radio (RF) frequency attenuator and(RF) signal attenuator conditioning and signal (SC) gainsconditioning were calculated (SC) gains based were on calculated the sampled based data on and the recorded sampled to data calibrate and therecorded received to power.calibrate the In bothreceived sets of measurements,power. In both sets the rawof measurements, data were acquired the raw with data an were 80 MHz acquired sampling with rate an 80 in MHz multiple sampling files whererate ineach multiple file containsfiles where 2 secondseach file contains of data. 2 Over seconds 976,000 of data. snapshots Over 976,000 were snapshots collected forwere both collected omni-directionalfor both omni and-directional directional setupsand directional and processed setups with and 400 processed kHz frequency with 400 resolution kHz frequency by applying resolution a high-orderby applying Gaussian a high window-order Gaussia which isn window sufficient which to detect is sufficient 2.4 GHz to WLAN detect 2.4 signals GHz or WLAN any other signals or availableany shortother duration available signals short duration in the 2.4 signals GHz ISM in the band. 2.4 GHz ISM band.

4. Data4. AnalysisData Analysis Methodology Methodology To estimateTo estimate the detection the detection of signals of signals in the in sensed the sensed bandwidth, bandwidth, the energy the energy detection detection [28] approach [28] approach was usedwas whereused where the received the received power power was compared was compared with a with predefined a predefined threshold threshold to define to define the state the of state of the channel.the channel. A channel A channel was considered was considered in a busy in a statebusy if state the receivedif the received power power was above was above the predefined the predefined thresholdthreshold and otherwise and otherwise considered considered in the in idle the state. idle Basedstate. Based on this, on a this, binary a binary time series time series was created was created in whichin which ‘1’ represents ‘1’ represents the busy the statebusy (presence state (presence of signal) of signal) and ‘0’ and represents ‘0’ represents the idle the state idle (absence state (absence of of signal).signal). To find To the find spectrum the spectrum utilization, utilization the duty, the cycle duty (DC)cycle was(DC) calculated was calculated based based on the on binary the binary time time seriesseries using using Equation Equation (1): (1): DC = Total time occupied by busy states / Total time occupied by both states (1) DC = Total time occupied by busy states/Total time occupied by both states (1) The DC is an important parameter and its lower values indicate the availability of the ITWs. The DCThe is duration an important of consecutive parameter 0’s and i.e. its, ITW, lower in values the binary indicate time the series availability was computed of the ITWs. along with its Theempirical duration cumulative of consecutive distribution 0’s i.e., function ITW, in (CDF). the binary The empirical time series distribution was computed was fitted along with with GP, WB, its empiricalEX, GM cumulative and LN distributions. distribution functionThe associated (CDF). parameters The empirical (shape: distribution k, scale: was δ and fitted location: with GP, µ) were WB, EX,found GM based andLN on the distributions. maximum likelihood The associated techniques parameters and used (shape: to compute k, scale: theδ meanand location: (M) for theµ) fitted were founddistribution based. onTable the maximum3 summarizes likelihood the distribution techniques functions and used toalong compute with the meanmean (M)formula for the for the fitted distribution.respective distribution. Table3 summarizes the distribution functions along with the mean formula for the respective distribution.To measure the goodness of fit between the empirical and used distributions, Kolmogorov Smirnov (KS) test was used, and the distribution with minimum KS distance was chosen as the best representative of the empirical distribution.

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Electronics 2019, 8, 1011 Table 3. Distribution functions. 6 of 14

Distribution Function and Mean Distribution Function and Mean Table 3. Distribution functions. t Distribution Function and Mean1 Distribution Function and Mean t F(t ) = 1 1 + k − k F(t ) = 1 exp GP w 1 EX tw k tw w w F(tw) = 1 1 + k − F(tw) w= 1 exp GP − � − � ��δ EX −M−= − δ�− � δ δ M = δ δ M = M = where1 k where k < k1 < 1 1 k − k tw 1 tw F(δtw) = 1 exp δ F(tw) = γ k,δ WB − t − GM Γ(k) δ F(t ) = 1M exp= δ Γ 1 + 1 M = kδ1 t − k k F(t ) = k, w ( ) WB 1 ln tw θ GM k w wF(tw) = 2 1 +1erf − LN − �− �√2δ --w M = 1 + 2 M = kγ � � δ δ Γ δ M = expkθ + 2 1 ln t tw denotes idle time windows (twδΓ0),� exp( )� is exponential function, Γ( ) is Gamma function, γ( ) is lower incompleteδ F(t ) = 1 +≥ erf gamma function and erf( )2 is the error function.2 LN w − θ - - w � � �� M = exp + √ δ To measure the goodness of ﬁt between22 the empirical and used distributions, Kolmogorov Smirnov (KS) test was used, and the distributionδ with minimum KS distance was chosen as the best tw denotes idle time windows� (θtw ≥ 0),� exp( ) is exponential function, Γ( ) is Gamma function, γ( ) is representativelower incomplete of the empirical gamma function distribution. and erf( ) is the error function.

5. Omni-Directional Antenna Measurements 5. Omni-Directional Antenna Measurements This section provides analysis of the measurements taken with the omni-directional antenna. This section provides analysis of the measurements taken with the omni-directional antenna. Figure3 displays the time-frequency map of the WLAN tra ﬃc over the duration of 1 second, where Figure 3 displays the time-frequency map of the WLAN traffic over the duration of 1 second, where 97 dBm is chosen as the decision threshold which is 10 dB above the measured noise ﬂoor and found −−97 dBm is chosen as the decision threshold which is 10 dB above the measured noise floor and found to be the noise-free region. to be the noise-free region.

0 Channel 1 Channel 12 -70

100

200 -75

300 -80 400

500

time (ms) -85 600

700 -90 800

900 -95 1000 2.4 2.41 2.42 2.43 2.44 2.45 2.46 2.47 2.48 2.49 2.5 frequency (GHz)

FigureFigure 3. 3.Time-frequency Time-frequency map map from from 2.4 2.4 GHz GHz to to 2.5 2.5 GHz. GHz. Figure3 shows that most of the users’ activity was present in the WLAN channels ‘1’ and ‘12’. Figure 3 shows that most of the users’ activity was present in the WLAN channels ‘1’ and ‘12’. There are different WLAN packets with different durations. For example, channel ‘1’ did not remain There are different WLAN packets with different durations. For example, channel ‘1’ did not remain in the busy state all the time. It remains in the idle state for various durations as represented by the in the busy state all the time. It remains in the idle state for various durations as represented by the ‘white’ color in the time-frequency map. Thus, by exploiting the idle state of the channel it can be ‘white’ color in the time-frequency map. Thus, by exploiting the idle state of the channel it can be accessed in the time domain by CR users. To find the statistics of the ITW, both channels were further accessed in the time domain by CR users. To find the statistics of the ITW, both channels were further processed to get the binary time series as shown in Figures4 and5. Based on the binary time series, processed to get the binary time series as shown in Figures 4 and 5. Based on the binary time series, the DC values were found to be 7.4013% in channel ‘1‘, 11.5071% in channel ‘12‘ and 4.6449% over the DC values were found to be 7.4013% in channel ‘1‘, 11.5071% in channel ‘12‘ and 4.6449% full-sensed bandwidth. These lower DC values indicate that both channels are highly underutilized and can be used for opportunistic spectrum access.

Electronics 2019, 8, x FOR PEER REVIEW 7 of 14 Electronics 2019, 8, x FOR PEER REVIEW 7 of 14 over Electronicsfull-sensed2019, 8bandwidth., 1011 These lower DC values indicate that both channels are highly7 of 14 underutilized and can be used for opportunistic spectrum access. 1.5 1.5

1 1

0.5 0.5 Binary Time Series Time Binary Binary Time Series Time Binary 0 00 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 time500 (ms) 600 700 800 900 1000 time (ms) -70 -70

-80 -80

-90 -90 Threshold -100 -100 received power (dBm) (dBm) power received received power (dBm) (dBm) power received 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 time500 (ms) 600 700 800 900 1000 time (ms) FigureFigure 4. Mapping 4. Mapping from received from received power power to Binary to Binary Time Series Time Series in channel in channel ‘1’. ‘1’.

1.5 1.5

1 1

0.5 0.5 Binary Time Series Time Binary Binary Time Series Time Binary 0 00 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 time500 (ms) 600 700 800 900 1000 time (ms) -70 -70

-80 -80

-90 -90 Threshold -100 -100 received power (dBm) power received received power (dBm) power received 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 time500 (ms) 600 700 800 900 1000 time (ms) FigureFigure 5. Mapping 5. Mapping from received from received power power to Binary to Binary Time Series Time Series in channel in channel ‘12’. ‘12’. Figure6 shows the empirical CDF of the ITW found in channel ‘1’. The gamma distribution Figure 6 shows the empirical CDF of the ITW found in channel ‘1’. The gamma distribution provides the best ﬁt with minimum KS distance. The estimated parameters are k = 0.4898, δ = 73.8318 provides the best fit with minimum KS distance. The estimated parameters are k = 0.4898, δ = 73.8318 and the mean M = 36.1628 ms. Apart from this, the log normal distribution also provides the second and the mean M = 36.1628 ms. Apart from this, the log normal distribution also provides the second best ﬁt. It is also important to observe that the empirical CDF tends to increase rapidly in the interval best fit. It is also important to observe that the empirical CDF tends to increase rapidly in the interval (95 ms to 100 ms). The reason for such long windows is because the channel was in the idle state most (95 ms to 100 ms). The reason for such long windows is because the channel was in the idle state most of the time and the occupancy state was changing due to the arrival of the beacon packet which was of the time and the occupancy state was changing due to the arrival of the beacon packet which was observed every 100 ms. Figure4 also shows that the beacon packets were detected at t = 0, 100, 200 and observed every 100 ms. Figure 4 also shows that the beacon packets were detected at t = 0, 100, 200 800 ms. and 800 ms.

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ElectronicsElectronics 20192019,, 88,, xx FORFOR PEERPEER REVIEWREVIEW 88 ofof 1414

0.90.9

0.80.8

0.70.7

0.60.6

0.50.5 CDF CDF

0.40.4

0.3 0.3 ChannelChannel 1 1 GP D=0.1413 0.2 GP D=0.1413 0.2 WBWB D=0.1328 D=0.1328 EX D=0.2693 0.1 EX D=0.2693 0.1 GMGM D=0.1282 D=0.1282 LNLN D=0.1290 D=0.1290 00 00 1010 2020 3030 4040 5050 6060 7070 8080 9090 100100 timetime (ms) (ms) FigureFigure 6.6. EmpiricalEmpiricalEmpirical CDFCDF versusversus fittedfitted fitted distributiondistribution ofof channelchannel ‘1’‘1’ ‘1’... Figure7 shows the empirical CDF of the ITW in channel ‘12’ where the GP distribution provides FigFigureure 77 showsshows thethe empiricalempirical CDFCDF ofof thethe ITWITW inin channelchannel ‘12’‘12’ wherewhere thethe GPGP distributiondistribution providesprovides the best fit with estimated parameters k = 1.1620 and δ = 3.285. Although it provides the best fit, since thethe bestbest fitfit withwith estimatedestimated parametersparameters kk == 1.16201.1620 andand δδ == 3.285.3.285. AlthoughAlthough itit providesprovides thethe bestbest fit,fit, sincsincee k > 1, the mean of the distribution is not finite. To calculate the mean value, the WB distribution kk >> 1,1, thethe meanmean ofof thethe distributiondistribution isis notnot finite.finite. ToTo calculatecalculate thethe meanmean value,value, thethe WBWB distributiondistribution parameters are used, which provide the second best fit to the empirical CDF. The estimated parameters parametersparameters areare used,used, whichwhich provideprovide thethe secondsecond bestbest fitfit toto thethe empiricalempirical CDF.CDF. TheThe estimatedestimated for this distribution are k = 0.6255 and δ = 9.0156 and the mean M = 12.8766 ms. By comparing both parametersparameters forfor thisthis distributiondistribution areare kk == 0.62550.6255 andand δδ = = 9.01569.0156 andand thethe meanmean MM == 12.876612.8766 ms.ms. ByBy empirical CDFs, it is observed that channel ‘1’ has less traffic load which provides longer ITW, so it is comparingcomparing bothboth empiricalempirical CDFs,CDFs, itit isis observedobserved thatthat channelchannel ‘1’‘1’ hashas lessless traffictraffic loadload whichwhich providesprovides most suitable for CR users. Another key observation is that, it is not possible to have ITW longer than longerlonger ITW,ITW, soso itit isis mostmost suitablesuitable forfor CRCR users.users. AnotherAnother keykey observationobservation isis that,that, itit isis notnot possiblepossible toto 100 ms since the WLAN access point was periodically transmitting a beacon packet approximately havehave ITWITW longerlonger thanthan 100100 msms sincesince thethe WLANWLAN accessaccess pointpoint waswas periodicallyperiodically transmittingtransmitting aa beaconbeacon every 100 ms. packetpacket approximatelyapproximately everyevery 100100 ms.ms.

0.90.9

0.80.8

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0.3 0.3 ChannelChannel 12 12 GPGP D=0.1277 D=0.1277 0.2 0.2 WBWB D=0.1342 D=0.1342 EXEX D=0.3009 D=0.3009 0.1 0.1 GMGM D=0.1465 D=0.1465 LNLN D=0.1349 D=0.1349 00 00 1010 2020 3030 4040 5050 6060 7070 8080 9090 100100 timetime (ms) (ms) FigureFigure 7.7. EmpiricalEmpiricalEmpirical CDFCDF versusversus fittedfitted ﬁtted distributiondistribution ofof channelchannel ‘12’‘12’ ‘12’...

SinceSince thethe SE SE uses uses a a frequency frequency sweep sweep,sweep,, each each frequency frequency point point was was traversed traversed afteafte afterrr the the configured configuredconfigured timetime resolution.resolution. resolution. ToTo To investigateinvestigate investigate thethe the effecteffect effect ofof lowlow of low timetime time resolutionresolution resolution onon thethe on statisticsstatistics the statistics ofof ITW,ITW, of thethe ITW, timetime the seriesseries time ofofseries bothboth of channelschannels both channels werewere resampledresampled were resampled individuallyindividually individually inin thethe timetime in the domaindomain time domainwithwith 1.63841.6384 with msms 1.6384 andand 3.27683.2768 ms and msms 3.2768 timetime resolutions.resolutions.ms time resolutions. ThisThis illustratesillustrates This illustrates thethe effecteffect theonon thethe effect ITWITW on whenwhen the ITW thethe whennetworknetwork the traffictraffic network isis sensedsensed traffic using isusing sensed aa lowlow using timetime a resolutionresolutionlow time resolution SE.SE. FiguresFigures SE. 88 and Figuresand 99 showshow8 and thethe9 show effecteffect the ofof different edifferentffect of timetime different resolutionsresolutions time resolutions onon channelschannels on ‘1’‘1’ channels andand ‘12’,‘12’, ‘1’ wherewhere reducingreducing thethe timetime resolutionresolution tendstends toto increaseincrease thethe durationduration ofof thethe ITW.ITW. ThisThis apparentapparent increaseincrease isis duedue toto thethe SESE inabilityinability toto detectdetect packetspackets withwith aa durationduration lessless thanthan thethe timetime resolution.resolution. Thus,Thus,

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and ‘12’, where reducing the time resolution tends to increase the duration of the ITW. This apparent Electronics 2019, 8, x FOR PEER REVIEW 9 of 14 Electronicsincrease 2019 is due, 8, x to FOR the PEER SE inability REVIEW to detect packets with a duration less than the time resolution.9 Thus,of 14 measurementsmeasurements performed performed using using low low time time resolution resolution SE SE do do not not provide provide reliable data for modelling measurements performed using low time resolution SE do not provide reliable data for modelling timetime-based-based opportunistic spectrum access. time-based opportunistic spectrum access. 1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5

CDF 0.5 CDF 0.4 0.4 0.3 0.3 0.2 0.2 204.80 µs 204.80 µs 0.1 1.6384 ms 0.1 1.6384 ms 3.2768 ms 0 3.2768 ms 00 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 time50 (ms) 60 70 80 90 100 time (ms) Figure 8. Effect of time resolution on empirical CDF of channel ‘1’. FigureFigure 8. EffectEﬀect of of time time resolution resolution on empirical CDF of channel ‘1’ ‘1’..

1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5

CDF 0.5 CDF 0.4 0.4 0.3 0.3 0.2 0.2 204.80 µs 204.80 µs 0.1 1.6384 ms 0.1 1.6384 ms 3.2768 ms 0 3.2768 ms 00 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 time50 (ms) 60 70 80 90 100 time (ms) FigureFigure 9. 9. EffectEﬀect of of time time resolution resolution on on empirical empirical CDF CDF of of channel channel ‘12’ ‘12’.. Figure 9. Effect of time resolution on empirical CDF of channel ‘12’.

TheThe empirical empirical distributions distributions can can be be further further utilized utilized to to study study how how the the variability variability of ofthe the load load in ina The empirical distributions can be further utilized to study how the variability of the load in a channela channel affects affects the the fitted fitted distributions distributions and and their their parameters. parameters. Table Table 4 summarizes the KSKS distancedistance channel affects the fitted distributions and their parameters. Table 4 summarizes the KS distance betweenbetween thethe empirical empirical and and fitted fitted distributions distributions (best distributions(best distrib areution markeds are bold).marked The bold) GP distribution. The GP between the empirical and fitted distributions (best distributions are marked bold). The GP distributionis found to have is found the bestto have fit for the channel best fit ‘1’for whilechannel the ‘1’ LN while distribution the LN distribution provides the provides best fit forthe channel best fit distribution is found to have the best fit for channel ‘1’ while the LN distribution provides the best fit ‘12’for channel for time ‘12’ resolutions for time ofresolutions 1.6384 ms of and 1.6384 3.2768 ms ms, and respectively. 3.2768 ms, respectively. for channel ‘12’ for time resolutions of 1.6384 ms and 3.2768 ms, respectively. For both channels, the mean ITW were calculated for comparison. Table 5 summarizes the For both channels, the mean ITW were calculated for comparison. Table 5 summarizes the parameters and the mean value for the distributions which provided the best fit. The mean value parameters and the mean value for the distributions which provided the best fit. The mean value tends to increase with the drop in traffic load as the channels remain in the idle state for longer time tends to increase with the drop in traffic load as the channels remain in the idle state for longer time windows. These results show that in previous work [20,24], where the GP distribution was shown to windows. These results show that in previous work [20,24], where the GP distribution was shown to provide the second best fit, the LN and GM distributions can also be used to model the behavior of provide the second best fit, the LN and GM distributions can also be used to model the behavior of the ITW for different network traffic loads. Moreover, by having wideband detection capability, a CR the ITW for different network traffic loads. Moreover, by having wideband detection capability, a CR user can monitor the traffic load on particular or multiple channels and can concurrently exploit the user can monitor the traffic load on particular or multiple channels and can concurrently exploit the idle states in multiple channels or radio technologies (e.g., 2.4 GHz WLAN and Bluetooth). idle states in multiple channels or radio technologies (e.g., 2.4 GHz WLAN and Bluetooth).

Electronics 2019, 8, 1011 10 of 14

Table 4. KS distance for ﬁtted distribution for diﬀerent time resolutions.

Channel 1 Channel 12 CDF. 204.8 µs 1.6384 ms 3.2768 ms 204.8 µs 1.6384 ms 3.2768 ms GP 0.1413 0.2412 0.2741 0.1277 0.1054 0.1507 WB 0.1328 0.2578 0.2879 0.1342 0.1321 0.1560 EX 0.2693 0.2492 0.2969 0.3009 0.1067 0.1522 GM 0.1282 0.2537 0.2900 0.1465 0.1408 0.1724 LN 0.1290 0.2449 0.2903 0.1349 0.0796 0.1438

For both channels, the mean ITW were calculated for comparison. Table5 summarizes the parameters and the mean value for the distributions which provided the best fit. The mean value tends to increase with the drop in traffic load as the channels remain in the idle state for longer time windows. These results show that in previous work [20,24], where the GP distribution was shown to provide the second best fit, the LN and GM distributions can also be used to model the behavior of the ITW for different network traffic loads. Moreover, by having wideband detection capability, a CR user can monitor the traffic load on particular or multiple channels and can concurrently exploit the idle states in multiple channels or radio technologies (e.g., 2.4 GHz WLAN and Bluetooth).

Table 5. Parameters for best ﬁtted distribution.

Channel 1 Channel 12 Parameters 204.8 µs 1.6384 ms 3.2768 ms 204.8 µs 1.6384 ms 3.2768 ms k 0.4898 0.4276 0.4968 0.6255 - - − − δ 73.8318 87.5879 102.667 9.0156 0.9796 0.9142 µ - - - - 2.9397 3.2385 M 36.1628 61.3533 68.5910 12.8766 30.5544 38.7210

6. Directional Measurements The influence of the angle of arrival on the statistics of the ITW is studied from directional measurements. Three antennae (A1, A2 and A3) were used for this indoor measurement. Figure 10 shows the effect of different directions on the received power in channel ‘1’ where it can be observed that antenna A3 has a higher received power compared to the other two antennae. Similarly, antenna A1 has a higher received power for channel ‘12’ as shown in Figure 11. While it is expected that the signal power varies with the angle of arrival, if at a certain direction the received power is lower than the decision threshold due to propagation losses, the channel may be considered in the idle state. Further to this, the time resolution per antenna also affects the state of the channel. For example, consider channel ‘12’ on antenna A3, where the received power dropped by more than 10 dB and it could no longer detect short duration signals. Electronics 2019, 8, x FOR PEER REVIEW 10 of 14

Table 4. KS distance for fitted distribution for different time resolutions.

Channel 1 Channel 12 CDF 204.8 µs 1.6384 ms 3.2768 ms 204.8 µs 1.6384 ms 3.2768 ms GP 0.1413 0.2412 0.2741 0.1277 0.1054 0.1507 WB 0.1328 0.2578 0.2879 0.1342 0.1321 0.1560 EX 0.2693 0.2492 0.2969 0.3009 0.1067 0.1522 GM 0.1282 0.2537 0.2900 0.1465 0.1408 0.1724 LN 0.1290 0.2449 0.2903 0.1349 0.0796 0.1438 Table 5. Parameters for best fitted distribution Channel 1 Channel 12 Parameters 204.8 µs 1.6384 ms 3.2768 ms 204.8 µs 1.6384 ms 3.2768 ms k 0.4898 −0.4276 −0.4968 0.6255 - - δ 73.8318 87.5879 102.667 9.0156 0.9796 0.9142 µ - - - - 2.9397 3.2385 M 36.1628 61.3533 68.5910 12.8766 30.5544 38.7210

6. Directional Measurements The influence of the angle of arrival on the statistics of the ITW is studied from directional measurements. Three antennae (A1, A2 and A3) were used for this indoor measurement. Figure 10 shows the effect of different directions on the received power in channel ‘1’ where it can be observed that antenna A3 has a higher received power compared to the other two antennae. Similarly, antenna A1 has a higher received power for channel ‘12’ as shown in Figure 11. While it is expected that the signal power varies with the angle of arrival, if at a certain direction the received power is lower than the decision threshold due to propagation losses, the channel may be considered in the idle state. Further to this, the time resolution per antenna also affects the state of the channel. For example,

Electronicsconsider2019 channe, 8, 1011l ‘12’ on antenna A3, where the received power dropped by more than 10 dB 11and of 14it could no longer detect short duration signals.

-70 A1 -80 -90 -100

received power (dBm) 0 100 200 300 400 500 600 700 800 900 1000 time (ms) -70 A2 -80 -90 -100

received power (dBm) 0 100 200 300 400 500 600 700 800 900 1000 time (ms) -70 A3 -80 -90 -100

received power (dBm) 0 100 200 300 400 500 600 700 800 900 1000 time (ms) ElectronicsFigure 2019 10., 8, Exﬀ FORect ofPEER directional REVIEW antenna on received power and short duration packets in channel ‘1’.11 of 14 Figure 10. Effect of directional antenna on received power and short duration packets in channel ‘1’.

FigureFigure 11. 11.Eff Effectect of of directional directional antenna antenna on on received received power power and and short short duration duration packets packets in channelin channel ‘12’. ‘12’. A decision threshold of 97 dBm, 10 dB above the measured noise floor, was used per direction to − find the respective binary time series. Table6 compares the DC values in relation to di fferent directional A decision threshold of −97 dBm, 10 dB above the measured noise floor, was used per direction antennas and gives a summary of the DC values based on the omni-directional antenna. To remove to find the respective binary time series. Table 6 compares the DC values in relation to different the uncertainty in the occupancy state due to the variation in the received power or the effect of time directional antennas and gives a summary of the DC values based on the omni-directional antenna. resolution per antenna, the binary time series were combined using the OR hard combining data fusion To remove the uncertainty in the occupancy state due to the variation in the received power or the technique, where a channel is considered in the busy state if it is detected in any direction. This series effect of time resolution per antenna, the binary time series were combined using the OR hard is used to compute the empirical CDF of the ITW. Figure 12 shows the empirical CDF of both channels combining data fusion technique, where a channel is considered in the busy state if it is detected in and Table7 summarizes the KS distance for fitted distributions (best distributions are marked in bold). any direction. This series is used to compute the empirical CDF of the ITW. Figure 12 shows the The Weibull distribution provides the best fit for channel ‘1’ with parameters k = 1.1070, δ = 51.7498 empirical CDF of both channels and Table 7 summarizes the KS distance for fitted distributions (best and M = 49.8307 ms. Channel ‘12’ empirical CDF is fitted best with the lognormal distribution with distributions are marked in bold). The Weibull distribution provides the best fit for channel ‘1’ with parameters δ = 1.4980, µ = 2.6644 and M = 44.0975 ms. Thus, the analysis shows that the angular parameters k = 1.1070, δ = 51.7498 and M = 49.8307 ms. Channel ‘12’ empirical CDF is fitted best with dimension can influence the statistics of the idle window due to propagation losses and the time the lognormal distribution with parameters δ = 1.4980, µ = 2.6644 and M = 44.0975 ms. Thus, the resolution per antenna used to acquire the data. To compensate for these effects, decisions can be analysis shows that the angular dimension can influence the statistics of the idle window due to propagation losses and the time resolution per antenna used to acquire the data. To compensate for these effects, decisions can be made using the OR-combining technique. However, the time domain- based directional opportunistic spectrum access can be vital in cases where communication links of licensed users are highly directional.

Table 6. summary of DC percentages in relation to omni and directional antennae.

DC Percentage

Channel ‘1‘ Channel ‘12‘ 2.4–2.5 GHz Omni-directional - 7.4013 11.5071 4.6449 A1 3.7515 12.9776 3.9191 Antenna Type Directional A2 4.3158 12.6841 4.1967 A3 5.1859 8.2525 3.1760

Electronics 2019, 8, 1011 12 of 14 made using the OR-combining technique. However, the time domain-based directional opportunistic spectrum access can be vital in cases where communication links of licensed users are highly directional.

Table 6. Summary of DC percentages in relation to omni and directional antennae.

DC Percentage Channel ‘1‘ Channel ‘12‘ 2.4–2.5 GHz Omni-directional - 7.4013 11.5071 4.6449 A1 3.7515 12.9776 3.9191 Antenna Type Directional A2 4.3158 12.6841 4.1967 A3 5.1859 8.2525 3.1760 Electronics 2019, 8, x FOR PEER REVIEW 12 of 14

0.9

0.8

0.7

0.6

0.5 CDF

0.4

0.3

0.2

0.1 channel 1 channel 12 0 0 10 20 30 40 50 60 70 80 90 100 time (ms) Figure 12. Empirical CDF of channels using OR combining technique.technique.

Table 7.7. KS distance forfor ﬁttedfitted distribution.distribution.

CDF Channel Channel 1 1Channel Channel 12 12 GP 0.1610 0.1610 0.1771 0.1771 WB 0.14590.1459 0.16540.1654 EX 0.1480 0.1480 0.1801 0.1801 GM 0.1477 0.1651 GM 0.1477 0.1651 LN 0.1751 0.1473 LN 0.1751 0.1473 The opportunistic spectrum access will play an important role in future wireless networks like InternetThe of opportunistic Things, device spectrum-to-device access, 5G will, and play drones an important assisted, to role improve in future spectrum wireless utilization networks and like Internetovercome of the Things, spectrum device-to-device, demands from 5G, emerging and drones applications assisted, toin improvesmart cities, spectrum connected utilization health and overcomeretail sector, the to spectrum count a few. demands Due to fromthe wide emerging spread applications availability infor smart 2.4 GHz cities, WLAN connected signals, health our work and retailwill be sector, a step to countforward a few. for Dueunlicensed to the wide users spread from availability emerging forapplications 2.4 GHz WLAN to access signals, the spectrum our work willwithout be a stepcausing forward interference. for unlicensed The usersreported from emergingset of measurement applicationss to and access the the numerical spectrum withoutsimple causingdistributions interference. to model The the reportedoccupancy set will of measurements allow users to and access the the numerical spectrum simple without distributions performing to modelperiodic the spectr occupancyum sensing. will allow In addition, users to our access findings the spectrum relating the without impact performing of time resolutions periodic and spectrum angle sensing.of arrival Inwill addition, be useful our to ﬁndings properly relating plan spectrum the impact occupancy of time resolutionsmeasurements and and angle build of arrival models will in beother useful wireless to properly communication plan spectrum technologies occupancy. measurements and build models in other wireless communication technologies. 7. Conclusions Both omni-directional and directional high time resolution occupancy measurements were performed to model the distribution of the idle time window in the 2.4 GHz WLAN. It is found that distributions such as Gamma and lognormal can be used to model the idle state of a 2.4 GHz WLAN channel along with the generalized Pareto distribution. Moreover, previous reported measurements provide statistics of the idle time windows over the full span of a radio technology which does not provide the statistics per channel. Here, analysis is performed per channel, which shows that different concurrently measured channels in a radio technology may have different ITW statistics and their idle states can be modelled using different distributions. Both the time resolution of the SE and directional antennae can influence the state (idle/busy) of the signal and if not selected appropriately can produce longer idle time windows with the inability to detect short duration signals.

Electronics 2019, 8, 1011 13 of 14

7. Conclusions Both omni-directional and directional high time resolution occupancy measurements were performed to model the distribution of the idle time window in the 2.4 GHz WLAN. It is found that distributions such as Gamma and lognormal can be used to model the idle state of a 2.4 GHz WLAN channel along with the generalized Pareto distribution. Moreover, previous reported measurements provide statistics of the idle time windows over the full span of a radio technology which does not provide the statistics per channel. Here, analysis is performed per channel, which shows that different concurrently measured channels in a radio technology may have different ITW statistics and their idle states can be modelled using different distributions. Both the time resolution of the SE and directional antennae can influence the state (idle/busy) of the signal and if not selected appropriately can produce longer idle time windows with the inability to detect short duration signals.

Author Contributions: This research was part of the A.A.C.’s PhD work at Durham University, under the supervision of S.S. A.A.C has conducted the experiments, data analysis and drafting of this manuscript under the guidelines of S.S. Funding: This research was funded by an EPSRC grant PATRICIAN EP/I00923X/1 and the EU CREW project. The Department of Engineering at Durham University and HMGCC funded the studentship of A.A.C. Acknowledgments: The authors would like to thank the late S.M. Feeney for the design of the analogue circuits in the SE. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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