Feasibility Study of LTE Middle-Mile Networks in TV White Spaces for Rural India

2016 IEEE 27th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC): Mobile and Wireless Networks

Chaitanya Prasad N†, Soubhik Deb†, Abhay Karandikar, Member, IEEE Department of Electrical Engineering Indian Institute of Technology, Bombay Email: [email protected], [email protected], [email protected]

Abstract—With an intention to provide robust broadband To ensure coexistence of the TV broadcasters with the connection to rural areas, Government of India is laying out secondary devices, geolocation databases have been mandated optical fiber cables across the country connecting the rural by the regulators Federal Communications Commission and offices (named Gram Panchayat) under the initiative BharatNet. Office of Communications, UK [7]. Standards like IEEE This work explores the feasibility of implementing a middle-mile 802.11af, IEEE 802.22 have been designed for enabling network from these Gram Panchayats to the nearby villages, broadband wireless access in TV white spaces [7]. Extensive where wireless clusters will be set up, using LTE-Advanced over TV White Spaces owing to excellent propagation characteristics testbed trials have been pursued [8], [9], [10], [11] and of the frequencies in TV UHF band. A proportionally fair radio devices working on TV White Space have been developed resource allocation over the middle-mile network is proposed that [12]. Technologies are being conceptualized and developed for uses Coordinated Multipoint Technology offered by Long Term operation over TV white spaces in Long Term Evolution (LTE) Evolution - Advanced (LTE-A) which satisfies the broadband [13], [14], [15], [16]. It is estimated that around 40 MHz of requirement. The simulation results have shown that for a cell free UHF TVWS spectrum will be available for commercial radius of Coordination Region below 5 km, a rate of 1 Mbps use. Thus we analyze middle-mile network in this band. The per end-user for rural population density of India is achievable, major contributions of our work are: which is more than the 512 Kbps target rate prescribed by the Telecom Regulatory Authority of India (TRAI). Analyzed the feasibility of a middle-mile network employ- • Keywords—TV White Space, LTE-Advanced, middle-mile net- ing LTE-A technology over TV White Space for supple- works, resource allocation, proportionally fair menting broadband connection in India. This is the first time that estimates for the parameters like transmitter power, I. INTRODUCTION cell radius, etc., are determined based on targets set up by Telecom Regulatory Authority of India for broadband Digital age has brought immense opportunities for people. connections per subscriber. But due to the lack of broadband services, most of the Presented an iterative algorithm that assigns radio resources inhabitants of rural and semi-urban areas in India have been • for transmission between Gram Panchayat and its villages unable to exploit that opportunity. In a bid to assuage this based on individual average rates attained over previous problem, Government of India has been working on providing iterations so as to achieve proportional fairness. broadband access to 638, 619 villages via 250, 000 village offices called Gram Panchayat under the initiative BharatNet. The remainder of this paper is organized as follows. Gram Panchayats will be provided with Point of Presence Section II describes the system architecture. In Section III, (PoP) via optical fiber backhaul. Wireless clusters can be we discuss the problem statement and suggest radio resource formed at the villages for the last-mile access. Note that the allocation scheme for the same. The results of simulation villages are typically few kilometres away from the Gram described are in Section IV and based on that modifications for Panchayats. To solve the problem of backhauling the data from radio resource allocation scheme are suggested. Discussion on these clusters to the PoP, it is envisioned to use the under- the above results are provided in Section V. Finally, we draw utilized TV White Spaces (in the UHF band). out some concluding remarks in Section VI. The usage TV White Space for rural broadband is different in India from other countries primarily because: II. SYSTEM ARCHITECTURE Being located in Region 3 of International Telecommuni- As mentioned earlier, the connectivity between the PoP • cation Union (ITU) terrestrial spectrum allocations, Fixed, at Gram Panchayat and the wireless clusters in the villages Mobile, Broadcasting services is permitted in 470 590 can be provided using middle-mile network in the TV UHF MHz band which is in sharp contrast to services or band band. An UHF Base Station (UHF-BS) will be connected to allowed in Europe and US [1]. the PoP and an UHF Customer Premise Equipment (UHF- [2] shows that in the 470 590 MHz band for four zones of CPE) will be connected to the access point of the wireless • India, at least 12 out of the 15 channels (80%) are available cluster. From here on, UHF-BS and UHF-CPE will be referred in 100% of the areas which is much larger than that in US, to as just BS and CPE, respectively. The BS-CPE link uses UK, Europe [3], [4], [5], [6]. the LTE-A over TVWS spectrum and all theses links are combined to create a middle-mile network. For simplifica- † These authors contributed equally to this work. tion, only the downlink is considered. Antenna of each BS

Fig. 2: Truncated network comprising of the central CoR and its associated Tiers I and II with 6 and 12 CoRs, repectively.

Fig. 1: A simple overview of the two-stage system depicting where, PTx is the power of transmitting antenna on that PRB the connections among BSs, CPEs and end-users. Each CPU in dBm, GT x is the transmitting antenna gain in dBi, Noise handles RRA among a few selected SAs of adjacent BSs. comprises both thermal noise and interference from other BSs using the same PRB. The corresponding Spectral efficiency (SE) is evaluated from SINR using comprises of 3 sectoral antennas (SAs) identified by the set = 1, 2, 3 while the CPEs are directional antennas. Each SE = log10(1 + SINR), (2) S { } SA forms a sectoral angle of 120. Those SAs, belonging Using the Channel Quality Indicator (CQI) Table [18] and the to three adjacent BSs, which are facing each other form a diversity of the antenna being used, rate in a PRB of the Coordination Region (CoR). The CoRs also include the CPEs LOS link in a SubFrame of duration TSF =1ms for the that are associated to the SAs belonging to that CoR. The corresponding SE is obtained. The diversity is 1, 2, 4 for SAs forming a CoR coordinate to perform Radio Resource SISO, 2 2 MIMO and 4 4 MIMO, respectively. Allocation (RRA) of a set of Physical Resource Blocks (PRBs) ⇥ ⇥ = 1, 2, 3,...,K among the CPEs in with that CoR. III. RADIO RESOURCE ALLOCATION (RRA) LTE-AK { facilitates Coordinated} Multipoint (CoMP) Technology, which is employed for the resource allocation that ensures According to Telecom Regulatory Authority of India better performance using joint processing schemes. For its (TRAI), the current definition of broadband is 512 kbps implementation, a central processing unit (CPU) dedicated to connection which will be upgraded to a 2 Mbps connection each CoR is established. This is illustrated in Fig.1. CoRs by 2017. If implementation of LTE-A over UHF TV White serve as a building block for our middle-mile network. We Space is supposed to be a good choice for middle-mile, then assume that the CoRs are arranged in cellular architecture it needs to ensure the above data rates at end-user. We first as shown for a truncated network in Fig. 2. The truncated formulate this objective and then suggest RRA for the same. network is with respect to the central CoR. The 6 adjacent CoRs closest to it form Tier I, next closest set CoRs forms A. Problem Formulation Tier II, and so on. Ideally, one has to analyze an infinite network but for the scope of distances involved in middle- Following notations are used in rest of the work: mile network, analyzing the truncated network is found to max Pb : Maximum transmission power of BS b be sufficient. Fixing a central CoR, the set of CoRs in the • k Pbs : Power of BS b in sector s on PRB k truncated network is = CoR1,...,CoR19 and set of • k M { } r(m,n) : Received rate of CPE(m,n) on PRB k CPEs in some CoRm is represented by m. A CPE in • is depicted by CPE2M . But it is possibleN that during Nm (m,n) The objective is to maximize rm under total power constraint. allocation of PRBs not all CPEs are eligible. The set of legally Mathematically, available CPEs for resource allocation in CoRm is denoted k by ⇤ . Assume that there is always data to be transmitted. max r m : CoR , (3) Nm (m,n) 8 m 2M The Channel State Information (CSI) for each BS-CPE link is n:CPE(m,n) m k assumed to be available at the CPU. X 2N X2K subject to: P k P max, bs  b A large part of India is flat terrain. Hence, BS sectoral s k X2S X2K antenna at height of 12 15 m and CPE antenna at height P k 0. of 5 m is considered for this work. Hata Model [17] is most bs appropriate for this terrain in 470 590 MHz band. The SINR Eq.(3) involves optimization over a solution space of contin- k of each individual PRB on a LOS link is then calculated using uous variable Pbs and discrete PRBs and hence it is difficult to solve. We propose a computationally feasible suboptimal SINR = PTx + GT x PL Noise, (1) algorithm for RRA. The algorithm is inspired by [19]. It is 2016 IEEE 27th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC): Mobile and Wireless Networks

k that a CPE with the lowest r(m,n)(t 1) on a selected PRB k is given a chance to utilize that PRB.

Algorithm 1 SPFA

1: Initialize: Nm⇤ = Nm 2: for each iteration t do 3: for each CoRm do 2M 4: for each PRB k do 2K 5: if CoRm Tier II then 2 6: update Nm⇤ based on the bounds imposed by the Coordinated Policy with Bucketing 7: end if 8: for each CPE(m,n) m do k 2N 9: Compute r(m,n)(t) Fig. 3: Interference Diagram and an outward sector k k r(m,n)(t) 10: Compute SC(m,n)(t)= rk (t 1) assumed that the power of a sectoral antenna is distributed (m,n) 11: end for equally among the PRBs, giving rise to its sub-optimality. 12: Assign PRB k to k CPE(m,n⇤) = arg max SC(m,n)(t) Nm⇤ B. Interference Analysis 13: rk (t) (1 1 )rk (t 1)+ 1 rk (t) (m,n⇤) Tc (m,n⇤) Tc (m,n⇤) The RRA gives rise to both intra-CoR and inter-CoR inter- 14: end for ference. To mitigate the predominant intra-CoR interference, 15: end for an additional constraint is imposed where no PRB is reused 16: end for in a CoR. This leads to only inter-CoR interference (ICI). The ICI may not be zero as same radio resources may be used in adjacent CoRs depending on the kind of frequency reuse D. Fairness Index being implemented. For allocating the PRBs, two different approaches have been employed depending upon location of In order to evaluate the SPFA in terms of fairness, we use the CoRs as described below. the well-known Jain’s fairness index, which is a quantitative fairness measure originally proposed in [21] as Coordinated Policy: This policy is employed in the central 2 CoR and the CoRs in Tier I, where all available PRBs are rk shared between all the CPEs of a CoR. Thus, ⇤ = m. The (m,n) m n:CPE m k advantage of this policy is that each PRB is ﬂexibleN toN choose JFI = ✓ (m,n)2N 2K ◆ . m P P 2 (4) the CPE that utilizes it best. The potential interferences to the k m . r(m,n) CPEs in the central CoR is shown in Fig. 3. |N | n:CPE k  (m,n) m P 2N ⇣ P2K ⌘ Coordinated Policy with Bucketing: In this policy, each Fairness of a scheduler for a CoRm increases with JFIm. CPE has a restriction on the number of PRBs that can be allocated to it. As the name suggests, we introduce bucketing IV. SIMULATIONS AND RESULTS in this policy, where each PRB will be allocated to a CPE in m⇤ . This policy is followed by the CoRs in the outermost tier. Simulating multiple realizations of a two tier topology AbsenceN of bucketing would result in unfairness because the as described in Sec. II gives an estimate on the achievable PRBs would be allocated to the CPEs in the outward sectors transmission characteristics of middle-mile network. As cell (some indicated in Fig. 3) to avoid interference. The size of radius of the CoR increases, number of villages in the CoR the bucket of each CPE is proportional to its demand. will change. In a typical scenario like that of India, it is unfair to assume equal no. of CPEs in each CoR. Due to the paucity of data and intention to include the diversity in our topology, C. Suboptimal Proportional Fair Allocation (SPFA) the no. of CPEs in each COR is chosen randomly from the set The iterative suboptimal approach in [19] has been adapted 3, 4, 5, 6 . To accomplish that, the no. of CPEs in the sector { } to our formulation and is implemented in all CoRs. Transmis- belonging to each BS of a CoR is randomly chosen as follows: sion power in each PRB across the whole network is assumed 2, with probability (w.p.) q to be equal, leading to sub-optimality. Also, it is assumed that no. of CPEs in each sector = s 1, w.p. (1 q ) CPEs in a CoR are identical, thereby giving rise to the same s ⇢ (5) demand. Deﬁne rk (t) as the received rate of CPE on (m,n) (m,n) where q [0, 1]. Realistically, the placement of villages k s 2 PRB k in iteration t and r(m,n)(t) as its moving average with are neither symmetric nor in proximity of each other. The k time constant Tc [20]. Information exchange of SC(m,n)(t) CPEs associated with a village have to be placed such that takes place between each BS of a CoR and associated CPU they are reasonably away from each other. To include this at each iteration t, which chooses the best legally available in our simulations, each CPE is randomly placed in disjoint CPE using SPFA as described in Algorithm 1 while ensuring circular area. Fig. 4 illustrates the case of 1 and 2 CPEs in coarse proportional fairness. An alternate viewpoint will be a sector in their respective circular areas with radii RRP1 2016 IEEE 27th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC): Mobile and Wireless Networks

Fig. 4: An instance of a topology generated from qs =0.4 where the CPEs are placed uniformly at random in each of Fig. 5: Total received rate of the central CoR averaged over the circular regions with radius R [0,R ). For sectors RP max 10 different network initializations for various values of sector with one CPE, R = .R and2 for sectors with two RP1 1 max1 probability (q ) and various radii of random placement (R ) CPEs, R = .R , where , [0, 1) s RP RP2 2 max2 1 2 2 GP, each CoR will have similar data rates, irrespective of the and RRP2 . These radii are scaled to their maximum values of p3 p3 number of CPEs and their location. Rmax1 = 6 Rcell and Rmax2 = 4 Rcell, respectively where Rcell is the cell radius of a CoR. Multiple such randomly initialized topologies are simulated using SPFA and the system V. D ISCUSSIONS parameters in Table I, path loss and LTE models. It is observed that the total average rate of the central CoR is estimated to be A. SPFA With Reservation (SPFAR) in the range of [87, 92] Mbps. For each topology, the average CoR rate is almost constant over time in spite of the dynamic The PRBs of the central CoR are generally observed to resource allocation. This is a consequence of the multiuser be distributed among its CPEs with a fairness index of over diversity obtained from the opportunistic user scheduling [19]. 0.98. In specific cases, because of coarse proportional fairness Further analysis on the cell parameters, transmission powers, of SPFA, the fairness goes below 0.99. With the intention of fairness and CPE demands are described in Section V. getting better fairness and at lower computational cost as com- pared to power control, a new allocation algorithm SPFAR is proposed. The principle behind SPFAR is that during allocation TABLE I: SIMULATION PARAMETERS of PRBs, preference should be given to CPEs having inferior average rates. For a first few iterations, say tf , the system Parameter Value Unit implements SPFA for allocating the PRBs in each CoR. Based CoR Cell Radius (Rcell) 5 Km on the average received rate over these tf iterations, SPFAR is Number of CoRs 19 – employed in the subsequent t0 iterations from iteration (tf +1) Channel Bandwidth (BW) 10 MHz to (tf + t0). The system is allowed to settle during these t0 iterations and SPFAR for the next t0 iterations is based on the Number of PRBs (K) 48 – average received rate over the first tf + t0 iterations and so Tx Power per PRB of SA 14.44 dBm on. Consider the following: Carrier Frequency (Fc) 500 MHz Height of BS 15 m r : Latest avg. rate of the CPE • (m,n) (m,n) rm = r CPE m : Latest avg. rate vector Gain of Tx antenna 10 dBi • { (m,n)| (m,n) 2N } rPRB: Latest avg. rate of any PRB in CoR Height of CPE 5 m • m m Overhead ↵ 0.25 – Mean of rm, rm, is determined by Antenna Diversity 4 –

Time constant Tc 50 ms rm I rm = · , (6) Duration of SubFrame (TSF ) 1 ms |Nm| Simulation duration 0.5 s where, I is an unit column vector of size m 1. Assume Scaling Factors (1,2) (0.45, 0.35) – |N |⇥ m is the set of indices of CPEs whose r(m,n) is less than that of r . For CPE , the magnitude of difference m (m,n) 2 m The contemporary Indian scenario would be diverse in the between r(m,n) and rm is equivalently described in terms of no. of CPEs per CoR and their locations. Hence topologies the number of PRBs as initialized with varying values of qs and RRP are simulated and it can be seen that the average received rate of the central rm r(m,n) CoR as shown in Fig. 5 has little variance. Hence it is safe to K =. , (7) (m,n) ⇣ PRB ⌘ conclude that when the wireless access is provided to every rm 2016 IEEE 27th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC): Mobile and Wireless Networks

where, K(m,n) is rounded off to nearest integer, is a tuning is seen when Rcell is less than 5 km, after which, pathloss parameter and becomes more predominant thus decreasing the average rate. We also observe from Fig. 6 that increasing the T x power has r (m,n) little improvement in the total average rate due to higher ICI. n:CPE m rPRB = (m,n)2N . m P K The set of PRBs , for each CoR is split into two disjoint subsets- and K, where is the set of PRBs which are K1 K2 K1 going to be allocated using Algorithm 2 and 1 2 = . K K K K To construct 1, n m (m,n) no. of PRBs are randomly selected fromK and2 assignedN to it. Rest of theS PRBs are K assigned to 2. AllocateP the PRBs in 2 among all CPEs of the KCoR using SPFA. K |Nm| m Algorithm 2 Reserved PRB Allocation

1: for each CoRm do 2M 2: while m =0 1 =0do | |6 ^|K |6 3: Choose CPE(m,n) m such that r 2 r , CPE (m,n)  (m,n0) 8 m,n0 2 m 4: Allocate the set of PRBs 3 1 to CPE as K ⇢K (m,n) = y ry rk , k K3 2K1| (m,n) (m,n) 8 2K1 K3 n 3 = K(m,n) Fig. 6: Plot of cost-beneﬁt analysis (CBA) comparing the ^|K| 5: m m CPE(m,n) o received rate (beneﬁts) for various SA transmitting powers 6: { } per PRB and sizes of each CoR (costs). Assuming a uniform K1 K1 K3 7: end while population density, a rate of 1 Mbps per end-user for a typical 8: end for district like Palghar grows proportionally to the cell area.

C. Meeting the Demands One of the specific cases where the fairness using SPFA is below 0.97 is simulated using SPFAR for various values of The implications of average throughput obtained from simulations are discussed briefly. Considering the typical district with tf = 125 and t0 = 25. A tradeoff between fairness and the total average rate of the central CoR is observed as of Palghar, in the state of Maharashtra, with over 324, 000 shown in Table II. The reservation of PRBs improves fairness people spanning an area of 1140 sq. km [22], has a density while introducing sub-optimality, thereby decreasing the total of 285 people per sq. km. The contention ratio of home average rate of a CoR. Depending on the priority of fairness users in India is about 1 : 50, as prescribed by the Telecom vs. throughput requirements, is chosen appropriately. Regulatory Authority of India (TRAI). Using this information and earmarking a rate of 1 Mbps to each end-user, the target average received rate for a CoR of different R is shown in TABLE II: For a varying , the tradeoff between Fairness cell Fig 6, which has to be met using the 40 MHz TVWS spectrum. among the CPEs vs. their total Throughput in the central CoR This target rate has been plotted along side the CBA. It can Algorithm Fairness CoR rate (Mbps) be seen that for a cell radius of CoR below 5 km, the target rate of 1 Mbps per user is achievable. SPFA – 0.9689 89.73 SPFAR 1 0.9828 87.31 D. Varying Demands Based on Topology SPFAR 2 0.9862 86.26 It was assumed in Section III that the CPEs in a CoR SPFAR 3 0.9876 85.86 are identical and therefore, on average, demand an equal SPFAR 4 0.9905 85.41 distribution of resources among the CPEs. This is often not the case in a diverse topology as that of India. If the rates B. Transmission T x Power vs. Cell Radius R demanded by the set of CPEs m in CoRm are in the ratio cell N (m,1) : (m,2) : : (m, m ), then line 10 of Algorithm 1 Increasing the T x power of an BS increases both received for SPFA is modified··· to include|N | these rate requirements as power and interference, while increasing the cell radius Rcell rk (t) decreases them both. Utilizing the general attributes of the k (m,n) Compute SC(m,n)(t)= (8) CoMP system estimated in previous sections, a Cost-Benefit rk (t 1) (m,n) Analysis (CBA) for estimating the trade-off between Power (m,n) & Distance is done in Fig. 6 for a bandwidth of 10 MHz. ✓ ◆ We choose the number of villages in each CoR from the set where the average rate of CPE(m,n) is normalized by (m,n), 3, 4, 5, 6 as described in Section IV. As distance between the representative of its demanded rate. The already initialized the{ neighboring} BSs decreases, the interference between them topologies studied in Section IV were simulated again with becomes more prominent which is evident from decreasing different rate requirements covering a wide range of values. average rate of a CoR with decreasing cell radius Rcell. This It is observed that the average fairness among the CPEs 2016 IEEE 27th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC): Mobile and Wireless Networks

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