Relay Deployment in Cellular Networks: Planning and Optimization

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 30, NO. 7, SEPTEMBER 2012

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Relay Deployment in Cellular Networks: Planning and Optimization Weisi Guo, Tim O’Farrell Department of Electrical and Electronic Engineering University of Sheffield, United Kingdom Email: {w.guo, t.ofarrell}@sheffield.ac.uk

Abstract— This paper presents closed-form capacity expressions for interfere-limited relay channels. Existing theoretical analysis has primarily focused on Gaussian relay channels, and the analysis of interference-limited relay deployment has been confined to simulation based approaches. The novel contribution of this paper is to consolidate on these approaches by proposing a theoretical analysis that includes the effects of interference and capacity saturation of realistic transmission schemes. The performance and optimization results are reinforced by matching simulation results. The benefit of this approach is that given a small set of network parameters, the researcher can use the closed-form expressions to determine the capacity of the network, as well as the deployment parameters that maximize capacity without committing to protracted system simulation studies. The deployment parameters considered in this paper include the optimal location and number of relays, and resource sharing between relay and base-stations. The paper shows that the optimal deployment parameters are pre-dominantly a function of the saturation capacity, pathloss exponent and transmit powers. Furthermore, to demonstrate the wider applicability of the theoretical framework, the analysis is extended to a multiroom indoor building. The capacity improvements demonstrated in this paper show that deployment optimization can improve capacity by up to 60% for outdoor and 38% for indoor users. The proposed closed-form expressions on interference-limited relay capacity are useful as a framework to examine how key propagation and network parameters affect relay performance and can yield insight into future research directions.

I. I NTRODUCTION Relays have been proposed as a solution to solving the challenge of improving local capacity in Long-Term-Evolution Advanced (LTE-A) and 802.16 j/m standards. Its primary purpose is to either increase the capacity of an existing area or to extend the coverage area of the parent cell-site. The Qualityof-Service (QoS) provided by an operator is not necessarily determined by the average performance, but by that achieved by a certain bottom percentage of customers. This is generally customers operating either on the interference limited celledge or indoors. Statistically, over 70% of the mobile traffic is carried to indoor users, therefore there is an urgency in addressing how to enhance capacity for users in both of these scenarios. This paper presents a novel closed-form capacity expression for an interference-limited relay-assisted cellular network, with consideration to the capacity saturation of realistic transmission schemes, as well as both outdoor and indoor users. The benefit of this approach is that given a small set of network parameters, the researcher can determine the capacity

of the network, as well as the deployment parameters that maximize capacity without committing to protracted system level simulation studies. A. Review The topic of relays in cellular networks has been well studied in the past [1] [2]. In terms of analysis, existing theory has largely focused on extending the original work on Gaussian relay channels. Closed-form expressions on optimal relay deployment in Gaussian channels was proven in [3] for location and in [4] for resource allocation. In a realistic cellular system, the effects of inter-cell interference [5] and capacity saturation of realistic modulation schemes have a significant impact on both the capacity of the system and the optimal solutions, as shown in [6]. For relay deployment in a multi-cell interference-limited network, the characterization of capacity and outage performance has been limited to simulation based studies [5] [7]. Interference-limited stochastic geometric theoretical methods have not yet been extended to relay channels [8]. The optimization of relay deployment location [9], resource allocation [1] [5], and cost efficiency [10] is conducted using iterative numerical approaches on simulation results. From a system designer perspective, there is a dichotomy in the theoretical and simulation approaches. The lack of tractable interference-limited relay capacity expressions means that one either has to rely on closed-form Gaussian channel expressions or extensive multi-cell simulation results. This has restricted the insight into how and why key network and channel parameters affect the relay performance and optimal deployment solutions. B. Contribution The novelty of this paper is to consolidate the theoretical and simulation based approaches by proposing an interferelimited theoretical framework that considers capacity saturation. The paper presents closed-form capacity expressions for a relay-assisted base-station and maximizes the capacity with respect to deployment parameters. The benefit of this approach is that, for any set of network parameters, the system designer is able to characterize and optimize the multi-cell network performance without resorting to extensive multicell simulations. The proposed analysis is also validated by a multi-cell simulator. Furthermore, to demonstrate the wider

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(λ), AWGN power (n) and antenna gain (A): |hi |2 λi 10

γs,i = n+

Fig. 1. Example system setup for a particular relay-node (RN) configuration (3-sector BS with 5-relays per sector): a) 19-BS RAN model; b) BS with RNs; c) Mean received SINR plot.

applicability of the theoretical framework, the analysis is extended to a multi-room indoor building. The theoretical analysis in this paper are validated by multi-cell simulation results from our own simulator and existing work. II. S YSTEM M ODEL The system considers a Multi-Cell-Multi-User (MCMU) Radio-Access-Network (RAN), where the base-stations (BSs) are homogeneously deployed, and are assisted by relay-nodes (RNs). The homogeneous deployment offers an upper-bound to the RAN performance, in comparison to other irregular cell distribution methods such as spatial poisson-point-process (SPPP) distribution [8]. The RN-assisted BS establishes a high quality BS-RN channel by providing a LOS BS-RN channel, as recommended by 802.16j/m specifications [11]. The paper considers non-cooperative Decode-and-Forward (DF) relaying protocol due to its low CSI estimation complexity and relatively strong system level performance compared to AF relaying [12]. The relays in this paper operate in the transparent mode, whereby the parent BS is responsible for scheduling the packet transmission of each user with knowledge of the relay deployment. The traffic model assumes that it is full-buffer and given that relays can yield a higher overall capacity, the authors expect that the relays can in fact reduce congestion delay. The system layer simulation results are derived from our own proprietary VCESIM LTE Dynamic System Simulator [13], which is bench-marked against 3GPP tests and has been verified by our sponsors Fujitsu and Nokia Siemens Networks. Each BS’s throughput considers 2-tiers of inter-BS interference, which is sufficiently accurate [5]. The simulation system model is shown in Fig. 1a, where a 19 BS network is created with wrap-around [5]. The relays are deployed near the cell-edge of each BS, where the cell-edge is defined as the region where the interference power from other cells is similar or stronger than the signal power from the serving BS. An illustration of one form of relay deployment is shown in Fig. 1b, and the corresponding average received SINR is shown in Fig. 1c. In simulations, the instantaneous received signal to interference plus noise ratio (SINR) of a single sub-carrier of a single user is a function of: the transmit power of BS (P ), pathloss

Si +A(θi ) 10

PNcell

j=1,j6=i |hj

|2 λ

j 10

Ps,i

Sj +A(θj ) 10

,

(1)

Ps,j

where the values of each parameter is given in Table. I in the Appendix. The parameters h and S are the multi-path and log-normal shadow fading components, defined in [14]. The pathloss component can be expressed as a function of the distance x: λ = Kx−α ; where K is the frequency dependent pathloss constant and α is the pathloss exponent. The downlink throughput employs an adaptive-modulation-coding results are taken from a physical link layer simulator [15]. III. A NALYTICAL M ODEL A. Interference-Limited Capacity The analytical model considers a simplified and tractable SINR expression of (1), based on a serving cell (i) and dominant interference cell (j), as shown in Fig. 2. This is similar to the analytical models in [16], whereby it has been shown that the effects of fading on capacity, when averaged over time, are small compared to the effects of pathloss. The complete SINR expression in (1) can be approximated to: x−α Pi i , by not assuming that the interference power is γi ≈ x−α Pj j greater than AWGN power. The downlink throughput is found using the Shannon expression, with consideration to mutual information saturation:  Cs for: 0 < xi < ds Ci = (2) alog2 (1 + bγi ) for: ds < xi < r where at the distance ds or less away from the serving cell, the the maximum achievable capacity in LTE is Cs = 4.3 bit/s/Hz, for a modulation and coding scheme of 64QAM and 6/7 Turbo Code. The factors a = 0.8 and b = 0.6 are the Shannon adjustment values to compensate for coding losses in mutual information [17]. It has been shown that if the capacity saturation of channels is not considered, the optimization solution can often be skewed towards awarding high capacity links with more resources, when in reality these channels have already been saturated [6]. The value of the distance at which capacity saturation occurs (ds ) can be found as a function of the inter-BS distance (2r), pathloss exponent (α) and the saturation capacity (Cs ): 2r

ds ≤ 1+(

Cs (2 a

−1)Pj 1 )α bPi

,

(3)

which is proven in Lemma 1 in the Appendix. The paper’s simulation results and analytical model considers both the mean capacity and the edge capacity, as defined by: • Mean Capacity: the mean capacity achieved is dominated by BS centre users that achieve a large received SINR. By stating that SINR (γ) of each position is large enough for the approximation log(1 + γ) ≈ log(γ) to hold, the margin of error averaged across the BS is small (0.1%). This is proven in Lemma 2 of Appendix.

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Fig. 2. Analytical (top) System Model and Simulated (bottom) Capacity for: a) Homogeneous BS; and b) Heterogeneous Outdoor-Outdoor Network with BS and Relays.

(x = 0): 1 C¯ = r

Z

where ds =

r

0

ds 1 Cdx ≈ {Σ + a | ds log2 [( )−α ] r 2r − ds 2r − ds ) |}, −2αrlog2 ( r

2r 1

1+γsα

(5)

is given by expression (3) and Σ = Cs ds .

The full proof is given in Lemma 3. The edge capacity achieved is between 2 closest BSs deployed, as illustrated in Fig. 2 at location 1. The minimum capacity (edge) occurs when (x = r): Cedge = alog2 (1 + b

Fig. 3. Baseline downlink throughput results for simulation (symbols) and theory (lines) with BS coverage sizes.

Edge Capacity: the capacity achieved by cell-edge users that has the lowest received SINR, which determine the Quality-of-Service (QoS). The paper will now consider the cellular capacity for a baseline outdoor system in Section III. •

B. No Relay In the baseline outdoor capacity analysis, the paper considers an omni-directional 1-sector BS, as shown in Fig. 2. The capacity of a single sector BS with co-frequency interference from other BSs can be expressed as:  for: 0 < x < 2r 1  Cs 1+γsα C= (4) bx−α 2r  alog2 (1 + (2r−x) ) for: < x ds1 and holds true if the BS-RN channel is always stronger than the RN-UE channel, as proven in Lemma 5. The values for the break-point distances ds1 , ds2 and ds3 can be found via Lemma 1 and Lemma 4 and is presented in Lemma 6. Figure 4 shows the downlink capacity (CCF-OR ) variation with BS-RN distance dr . In Fig. 4, the classical Gaussian relay channel is considered, whereby no capacity saturation and interference are modelled. The results show that the optimal RN location is generally less than halfway (dr /r < 0.5), similar to the results obtained in for a cooperative DF relaying [3] [4]. By introducing the co-channel interference and capacity saturation, the optimization solution shifts to deploying the RN away from the BS to dr /r ' a. This will be more closely explained in the next section. The mean capacity results show a good match between simulation and theoretical capacity. The detailed proof on optimization for mean and edge capacity is given below.

The mean capacity achieved is the average capacity achieved from edge of BS (x = r) to the base of the BS (x = 0): Z 1 0 1 ¯ (9) CCF-OR = CCF-OR dx ≈ {ΣCF-OR + | aFΣ |}, r r r where ΣCF-OR contains the BS and RN saturation capacity terms, and FΣ is a composite logarithm term that contains the non-saturated terms, as explained in Lemma 6. In order to maximize the mean capacity with respect to the location of the RN, expression (9) is differentiated with respect to dr . The optimal BS-RN distance that maximizes mean capacity is: d∗r,CF-OR,mean-opt. ≈ 0.4FΣ [1 + (γs

Pr − 1 ) α ], Pc

(10)

where FΣ is a constant and the full optimization proof is shown in Lemma 6 of the Appendix. The conclusion is that the optimal RN location is almost entirely dependent on the power ratio between the RN and the BS ( PPrc ), the pathloss exponent (α), and the saturation SINR threshold (γs ): • A lower γs (worse transmission technique) means the RN should be placed further to the parent-BS, because most of the coverage area near the BS is saturated. Therefore the RN is only beneficial at the cell-edge and the effectiveness of RN deployment is also reduced. • A lower RN to BS transmit power ratio means the RN should be placed further from the parent-BS, because the stronger the BS transmit power, the stronger the effect of the BS’s coverage. • A lower α (more LOS based propagation) means the RN should be placed further from the BS, because the weaker the pathloss effect, the stronger the effect of the serving BS’s coverage. The results in Fig. 4 show that the optimal RN location for Gaussian channels is generally less than half-way (dr /r
Pc Kdr

∴

NRN

where:

drr ∼

2Pr − 1 < 2π( ) α, Pc

2πdr NRN

(27)

which is only accurate when the number of RNs is above 2 and high.

D ' D + L. For indoor users being served by a RN on the inside, the mean capacity (IR,i) is: C¯IR,i = alog2 (1 + b(

D )−α ). 2r − D

(29)

I. System Modeling Parameters The parent-BSs and RNs are assumed to be on rooftops and have Line-of-Sight (LOS) propagation, whereas the interference from adjacent BSs and RNs are assumed to be Non-Line-of-Sight (NLOS) based [14]. The parameter |h| is the magnitude of the complex fading coefficient h, which Rayleigh distributed and generated from an auto-regressive AR(n) process, where by the value of n is dependent on the delay spread [14] [19]. The BS-UE and the RN-UE channels are assumed to be based on a probabilistic model, whereby the probability of being in LOS: x x 18 )(1 − e− 36 ) + e− 36 , (30) x where x is the distance from the serving BS. The serving BS-RN channel is assumed to be in LOS [11] [20].

℘LOS = min(1,

H. Lemma 8: Indoor Mean Capacity (Relays) For indoor users being served by a RN on the outside, the mean capacity (IR,o) is: Z bPr x−α 1 0 C¯IR,o = alog2 (1 + )dx L L Pc (D + x)−α (28) Pr L −α ' alog2 (b ( ) ), for: D  L, Pc D which holds true for when the building size (L) is significantly smaller than the distance from the serving BS (D), so that

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 30, NO. 7, SEPTEMBER 2012

[3] B. Lin, P. Ho, L. Xie, and X. Shen, “Optimal relay station placement in IEEE 802.16j networks,” in Proc. of ACM Wireless Communications and Mobile Computing, Aug. 2007. [4] N. Zhou, X. Zhu, and Y. Huang, “Optimal Asymmetric Resource Allocation and Analysis for OFDM-Based Multidestination Relay Systems in the Downlink,” in Vehicular Technology, IEEE Transactions on, vol. 60, Mar. 2011, pp. 1307–1312. [5] A. Dinnis and J. Thompson, “The effects of including wraparound when simulating cellular wireless systems with relaying,” in Vehicular Technology Conference, IEEE, Apr. 2007, pp. 914–918. [6] A. Lozano, A. M. Tulino, and S. Verdu, “Optimal power allocation for parallel gaussian channels with arbitrary input distributions,” in Information Theory, IEEE Transactions on, vol. 52, Jul. 2006, pp. 3033– 3051. [7] N. Esseling, B. Walke, and R. Pabst, “Performance evaluation of a fixed relay concept for next generation wireless systems,” in Personal, Indoor and Mobile Radio Communications, IEEE International Symposium on, Sep. 2004. [8] M. Haenggi, J. G. Andrews, F. Baccelli, O. Dousse, and M. Franceschetti, “Stochastic geometry and random graphs for the analysis and design of wireless networks,” in Selected Areas in Communications (JSAC), IEEE Journal on, vol. 28, Sep. 2009, pp. 1029–1046. [9] E. Lang, S. Redana, and B. Raaf, “Business Impact of Relay Deployment for Coverage Extension in 3GPP LTE-Advanced,” in Communications Workshops, 2009. ICC Workshops 2009. IEEE International Conference on, Jun. 2009, pp. 1–5. [10] A. Furuskar, M. Almgren, and K. Johansson, “An infrastructure cost evaluation of single- and multi-access networks with heterogeneous traffic density,” in Vehicular Technology Conference, IEEE, May 2005. [11] R. Srinivasan and S. Hamiti, “IEEE 802.16m-09/0034r4: System Description Document,” IEEE, Technical Report, May 2007. [12] C. S. Patel and G. L. Stuber, “Channel estimation for amplify and forward relay based cooperation diversity,” in Wireless Communications, IEEE Transactions on, vol. 6, Jun. 2007, pp. 2348–2356. [13] W. Guo and T. O’Farrell, “Green cellular network: Deployment solutions, sensitivity and tradeoffs,” in Wireless Advanced (WiAd), IEEE Proc., London, UK, Jun. 2011. [14] 3GPP, “TR36.814 V9.0.0: Further Advancements for E-UTRA Physical Layer Aspects (Release 9),” 3GPP, Technical Report, Mar. 2010. [15] C. Mehlfuhrer, M. Wrulich, J. Ikuno, D. Bosanska, and M. Rupp, “Simulating the long term evolution physical layer,” in European Signal Processing Conference, EURASIP, Aug. 2009, pp. 1471–1478. [16] M.-S. Alouini and A. Goldsmith, “Area spectral efficiency of cellular mobile radio systems,” in Vehicular Technology, IEEE Transactions on, vol. 48, no. 4, Jul. 1999, pp. 1047–1066. [17] P. Mogensen, W. Na, I. Kovacs, F. Frederiksen, A. Pokhariyal, K. Pedersen, T. Kolding, K. Hugl, and M. Kuusela, “LTE Capacity Compared to the Shannon Bound,” in Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE, Apr. 2007, pp. 1234–1238. [18] J. Xu, J. Zhang, and J. Andrews, “On the accuracy of wyner model in cellular networks,” in Wireless Communications, IEEE Transactions on, vol. 10, Sep. 2011, pp. 3098–3109. [19] P. Kyosti, J. Meinila, L. Hentila, and X. Zhao, “WINNER II Channel Models: Part I Channel Models version 1.2,” WINNER and Information Society Technologies, Technical Report, 2007. [20] I. Fu, W. Sheen, and F. Ren, “Deployment and radio resource reuse in IEEE 802.16j multi-hop relay network in Manhattan-like environment,” in Information, Communications and Signal Processing, IEEE International Conference on, Dec. 2007.

Weisi Guo received his B.A., M.Eng., M.A. and Ph.D. degrees from the University of Cambridge. He is currently an Assistant Professor at the University of Warwick and is the author of the VCESIM LTE System Simulator. His research interests are in the areas of self-organization, energy-efficiency, and

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multi-user cooperative wireless networks. Tim O’Farrell holds a Chair in Wireless Communication at the University of Sheffield, UK. He is the Academic Coordinator of the MVCE Green Radio Project. His research encompass resource management and physical layer techniques for wireless communication systems. He has led over 18 research projects and published over 200 technical papers including 8 granted patents.