Interference coordination in fullduplex HetNet with largescale antenna arrays
 176 Downloads
 1 Citations
Abstract
Massive multipleinput multipleoutput (MIMO), small cell, and fullduplex are promising techniques for future 5G communication systems, where interference has become the most challenging issue to be addressed. In this paper, we provide an interference coordination framework for a twotier heterogeneous network (HetNet) that consists of a massiveMIMO enabled macrocell base station (MBS) and a number of fullduplex smallcell base stations (SBSs). To suppress the interferences and maximize the throughput, the fullduplex mode of each SBS at the wireless backhaul link (i.e., inband or outofband), which has a different impact on the interference pattern, should be carefully selected. To address this problem, we propose two centralized algorithms, a genetic algorithm (GEA) and a greedy algorithm (GRA). To sufficiently reduce the computational overhead of the MBS, a distributed graph coloring algorithm (DGCA) based on price is further proposed. Numerical results demonstrate that the proposed algorithms significantly improve the system throughput.
Key words
Massive MIMO Fullduplex Small cell Wireless backhaul Distributed algorithmCLC number
TN929.51 Introduction
The 5thgeneration (5G) communication system has drawn more and more research interest recently. Compared with the 4thgeneration (4G) communication systems, i.e., the 3rd Generation Partnership Project (3GPP) Long Term Evolution Advanced (LTEA) standard (3GPP, 2012a), the most challenging problem in 5G is to achieve substantially higher throughput. As such, a smallcell heterogeneous network (HetNet), where small cells provide local capacity enhancements (e.g., hotspots in urban areas) and macro cells support highly mobile terminals, has been proposed as a promising technique for 5G due to its high spatial reuse gains (Hoydis et al., 2011; Boccardi et al., 2014). However, the inherent cotier and crosstier interferences in HetNets result in serious degradation to the overall throughput performance. Thus, suppressing these interferences in smallcell HetNets is an emerging issue to be addressed.
To address this issue, two main approaches have been proposed in previous papers. On the one hand, massive multipleinput multipleoutput (MIMO) techniques were proposed to reduce the interference. As demonstrated in Marzetta (2010), Rusek et al. (2013), and Larsson et al. (2014), massive MIMO scales up the conventional MIMO by orders of magnitude and exploits the additional spatial degrees of freedom (DoFs), thus providing aggressive spatial multiplexing capabilities and significant array gains. On these bases, Hoydis et al. (2013) proposed a network framework where macrocell base stations (MBSs) with largescale antenna arrays provide services for highly mobile user equipment (UE) and dense small cells support UE with low mobility. Simulation results showed that the smallcell HetNet with massive MIMO could achieve very high throughput. A similar framework was invoked in Hosseini et al. (2013), in which a covariancebased precoding scheme was employed for interference cancellation at the price of sacrificing excess antennas.
On the other hand, fullduplex could be used for interference coordination. By transmitting and receiving signals on the same frequency at the same time (Choi et al., 2010; Sabharwal et al., 2014; Liu et al., 2015; Thilina et al., 2015; Bharadia and Katti, 2016), fullduplex techniques could help small cells efficiently reuse the radio access network (RAN) spectrum (Goyal et al., 2013). In Goyal et al. (2014), an uplink/downlink user scheduling scheme that maximizes the overall utility of smallcell users was developed to investigate the feasibility conditions of fullduplex operations. In Li et al. (2015), three strategies of smallcell inband wireless backhaul with massive MIMO were provided and analyzed, and simulation results demonstrated that applying fullduplex techniques at small cells could provide larger gain. Tabassum et al. (2016) considered the backhaul links in both in and outofband fullduplex modes, and derived the optimal mode selection proportion of smallcell base stations (SBSs) for special cases to maximize the outage capacity.
Although the feasibility of combining massive MIMO, small cell, and fullduplex has been studied in the above works, comprehensive analyses of how to effectively suppress the cotier interference and crosstier interference in such scenarios are still absent, and we attempt to fill that gap in this study. In this paper, the downlink of a twotier HetNet with a massive MIMOenabled MBS and a number of fullduplex SBSs working in inband or outofband wireless backhaul, respectively, is considered. With full consideration of the different impacts of the fullduplex modes, the cotier and crosstier interferences are thoroughly analyzed. To maximize the overall system throughput, we propose centralized algorithms and a distributed algorithm to decide the fullduplex mode selection strategies of SBSs.
2 System model
Massive MIMO is invoked by the MBS, which refers to the case where N ≫ K and N ≫ S. Linear zeroforcing beamforming (LZFBF) with equal power per stream is employed in the MBS access links to MUE and wireless backhauls to SBSs. Moreover, we assume the wireless channels are subject to pathloss, shadowing, and Rayleigh flat fading throughout the paper. For analytical convenience, we further assume that the transmissions across the tiers are synchronized perfectly, and the perfect channel state information (CSI) can be obtained by the MBS from uplink training. The MBS can then use it to design the downlink precoder, which is facilitated by considering time division duplexing (TDD) at the MBS such that the channel reciprocity is guaranteed. Note that although we restrict our attention to the downlink case, the proposed algorithms are applicable to the uplink scenarios with some minor modifications.
2.1 Fullduplex modes of SBSs
Before analyzing the macro and smallcell transmissions, let us look into the fullduplex modes of each SBS. As stated in Tabassum et al. (2016), each SBS can operate in two modes for wireless backhaul transmission: (1) outofband fullduplex mode (OBFD), in which the access link and backhaul link are conducted in orthogonal channels; (2) inband fullduplex mode (IBFD), in which the access link and backhaul link are conducted at the same frequency band simultaneously.
2.2 Macrocell transmission model
2.3 Smallcell transmission model
Recalling that different fullduplex modes induce distinct interference patterns, we need to model them separately.
2.3.1 SBS in OBFD mode
 1.Backhaul link: As shown in Fig. 2a, the ith SBS operates in OBFD mode and suffers from cotier interference from SBSs in \({{\mathcal S}_{\rm{I}}}\). As such, the received signal at the ith SBS can be expressed aswhere \({h_{{\rm{b2s,}}i}} \in {{\mathbb C}^{1 \times N}}\) denotes the channel matrix from the MBS to the ith SBS, G is the precoding matrix, \({x_{{\rm{b2s}}}}(t) \in {\mathcal C}{\mathcal N}\left( {0,\left( {{P_{\rm{M}}}/S} \right){I_S}} \right)\) is the S × 1 symbol vector from the MBS to SBSs. h_{s2s,il} represents the channel between the ith SBS and the lth SBS, which incurs the cotier interference. Thereby, the ith SBS in OBFD mode has the SINR$$\begin{array}{*{20}c} {y_{{\rm{SBS,}}i}^{\rm{O}}(t) = {h_{{\rm{b2s,}}i}}G{x_{{\rm{b2s}}}}(t)\quad \quad \quad \quad \quad \quad \quad \quad } \\ { + \sum\limits_{l \in {{\mathcal S}_{\rm{I}}}} {{h_{{\rm{s2s,}}il}}{x_{{\rm{s2su,}}l}}(t) + n(t),} } \\ \end{array} $$(3)$$\gamma _{{\rm{SBS,}}i}^{\rm{O}} = {{{{{P_{\rm{M}}}} \over S}{{\left\ {{h_{{\rm{b2s,}}i}}G} \right\}^2}} \over {{P_{\rm{S}}}\sum\nolimits_{l \in {{\mathcal S}_{\rm{I}}}} {h_{{\rm{s2s,}}il}^2 + {\sigma ^2}} }}.$$(4)
 2.Access link: As illustrated in Fig. 2a, the ith SUE suffers from interference from the MBS and the other SBSs in OBFD mode, and thus it suffers from both cotier interference and crosstier interference. The received signal can be expressed aswhere h_{s2su,ii} denotes the channel from the ith OBFD SBS to its associated SUE, and h_{s2su,ip} denotes the channel between the pth SBS and the ith SUE, which incurs the cotier interference. h_{b2su,i} ∈ ℂ^{1×N} is the channel matrix from the MBS to the ith SUE, which incurs the crosstier interference.$$\begin{array}{*{20}c} {y_{{\rm{SUE,}}i}^{\rm{O}}(t) = {h_{{\rm{s2su,}}ii}}{x_{{\rm{s2su,}}i}}(t) + \sum\limits_{p \in {{\mathcal S}_{\rm{O}}}\backslash i} {{h_{{\rm{s2su,}}ip}}{x_{{\rm{s2su,}}p}}(t)} } \\ { + {h_{{\rm{b2su,}}i}}W{x_{{\rm{b2mu}}}}(t) + n(t),} \\ \end{array} $$(5)
2.3.2 SBS in IBFD mode
 1.Backhaul link: For IBFD SBS j, the cotier interference and selfinterference should be considered (Fig. 2b). Specifically, the received signal at the jth SBS is given bywhere \(\sqrt {{I_{{\rm{SI}}}}} w(t)\) denotes the residual selfinterference and E[∥w(t)∥^{2}] = 1. Based on Tabassum et al. (2016), I_{SI} can be modeled as I_{SI} = P_{S}/ρ, where ρ represents the selfinterference cancellation capability. Therefore, the SINR at the jth SBS in IBFD mode is obtained by$$\begin{array}{*{20}c} {y_{{\rm{SBS,}}j}^{\rm{I}}(t) = {h_{{\rm{b2s,}}j}}G{x_{{\rm{b2s}}}}(t) + \sum\limits_{q \in {{\mathcal S}_{\rm{I}}}\backslash j} {{h_{{\rm{s2s,}}jq}}{x_{{\rm{s2su,}}q}}(t)} } \\ { + \sqrt {{I_{{\rm{SI}}}}} w(t) + n(t),\quad \quad } \\ \end{array} $$(8)$$\gamma _{{\rm{SUS}},j}^{\rm{I}} = {{{P_{\rm{M}}}{{\left\ {{h_{{\rm{b2s,}}j}}G} \right\}^2}/S} \over {{P_{\rm{S}}}\sum\nolimits_{q \in {{\mathcal S}_{\rm{I}}}\backslash j} {h_{{\rm{s2s,}}jq}^2 + {I_{{\rm{SI}}}} + {\sigma ^2}} }}.$$(9)
 2.Access link: As illustrated in Fig. 2b, the jth SUE suffers from crosstier interference from the MBS and cotier interference from other SBSs in IBFD mode, thus inducing the received signal:$$\begin{array}{*{20}c} {y_{{\rm{SUE,}}j}^{\rm{I}}(t) = {h_{{\rm{s2su,}}jj}}{x_{{\rm{s2su,}}j}}(t) + \sum\limits_{q \in {{\mathcal S}_{\rm{I}}}\backslash j} {{h_{{\rm{s2su,}}jq}}{x_{{\rm{s2su,}}q}}(t)} } \\ { + {h_{{\rm{b2su}},j}}G{x_{{\rm{b2s}}}}(t) + n(t).\quad } \\ \end{array} $$(10)
3 Problem formulation
Due to the different interference patterns of these two fullduplex modes, we should reasonably assign the fullduplex modes to each SBS to maximize the overall throughput of this twotier HetNet. In this section, we formulate the fullduplex mode selection problem as an optimization problem.
For notational convenience, we have replaced g_{i,j} and h_{i} in the third term with c_{i,j} and d_{i}, respectively. This is practical because c_{i,j} = g_{i,j} and d_{i} = h_{i}.
4 Interference coordination with centralized algorithms
Obviously, problem (16) is still a combinatorial optimization problem. Also, since the objective function is nonconvex, the optimal vector x can hardly be obtained even if we relax the constraint x_{i} ∈{0, 1}. As such, the proper nearoptimal solutions are desired. In this section, we propose two centralized algorithms, i.e., a genetic algorithm and a greedy algorithm, so that the MBS collects the global CSI and acts as a central scheduler.
4.1 Genetic algorithm
The genetic algorithm (GEA) is an adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics; it represents an intelligent exploitation of a random search to solve optimization problems. Additionally, in searching a large statespace, GEA may offer significant benefits over other typical optimization techniques. As such, in this subsection, we apply GEA to our optimization problem.
Because GEA is a mature algorithm, the theory will not be elaborated specifically in this paper; instead, we directly list the procedure in Algorithm
. In the settings of key parameters (e.g., the maximum number of iterations, the probability of crossover and mutation), we adopt some general values based on experience. Notably, it is necessary to give serious consideration to the setting of population size, because if we set the value as a constant, the performance of GEA will significantly degrade when the value of S is much larger than the population size; thus, in this study, we set it as S +10, a variable depending on S.As shown in Algorithm \({\mathcal O}({S^2})\), and thus step 4 needs complexity of \({\mathcal O}({S^3})\). Moreover, because the maximum number of iterations we set is a constant, the overall complexity of GEA is still \({\mathcal O}({S^3})\).
, the complexity of GEA depends mainly on step 5. We can easily verify that the complexity of calculating the objective function in problem (16) for one individual is4.2 Greedy algorithm
The greedy algorithm (GRA) is an algorithmic paradigm that follows the problemsolving heuristic of making the locally optimal choice at each stage. In many combinatorial optimization problems, e.g., traveling salesman problem (TSP), a greedy strategy is often used as an effective method to find an available solution. In this subsection, a greedy algorithm is proposed to solve our problem.
As shown in Algorithm \({\mathcal O}(S)\) and the complexity of step 7 is \({\mathcal O}({S^2})\), and thus the complexity of one iteration is \({\mathcal O}({S^3})\), but its rate of convergence cannot be promised because it depends on the quality of the initial random solutions. We can determine only that the overall complexity of GRA will be no less than \({\mathcal O}({S^3})\). However, we can evaluate the convergence rate of GRA by comparing its computation time with that of GEA, because they share the same iteration complexity, i.e., \({\mathcal O}({S^3})\). The comparison is shown in Section 6.
, the basic idea of GRA is that at each iteration we switch the mode of an SBS when this switch can bring the largest throughput gain; the iteration will terminate if the largest throughput gain becomes negative. Obviously, GRA can make the best choice at each iteration; however, it may fail to produce the optimal solution because it is shortsighted. We also note that the solutions obtained by GRA will be influenced by the random initial solutions; thus, to obtain the optimal solution as often as possible, we should generate a lot of initial solutions and choose the one that has the best performance. As for the complexity of GRA, we can see that in Algorithm , the complexity of step 4 isFor the iterative process in the proposed GRA, we prove its convergence in the following proposition:
Proposition 1 For any initial solution, GRA can converge.
Because C_{t} is monotonously increasing with t and has an upper bound U, Proposition 1 holds based on the monotone convergence theorem.
5 Interference coordination with distributed algorithm
As analyzed in Section 2, we recognize that the cotier interference is incurred only when SBSs are in the same fullduplex modes. Thus, we can determine that there is an intuitive relationship between our problem and the graph coloring problem if we regard these two fullduplex modes as two distinct colors. Based on this idea, in this section, we propose a distributed graph coloring algorithm (DGCA) based on price, which has each SBS individually choose the fullduplex mode, to find a nearoptimal solution. As compared with centralized algorithms, DGCA could significantly reduce the computational overhead of MBS.
5.1 Adjacent graph
Considering the largescale fading, the ith SUE associated with the ith SBS suffers from only severe cotier interference incurred by nearby SBSs that operate in the same mode. To describe this phenomenon, we make the following definition:
Remark 1 Intuitively, c_{i,j} denotes the ratio between the interference from SBS j to SUE i and the useful signal transmitted by SBS i to SUE i. As such, when saying that two SBSs are adjacent, we mean that one of them may cause significant cotier interference to the other’s associated SUE. The cotier interference between nonadjacent SBSs is comparatively small and can be ignored.
Applying this definition to all SBS pairs, an adjacent graph is generated. Inspired by this adjacent graph, if we want to minimize the cotier interference in the considered HetNet, we should assign adjacent SBSs with different modes as much as possible. If we further regard these two fullduplex modes as two distinct colors, then we need only to color the adjacent graph according to some kind of graph coloring algorithm.
5.2 Price
Some graph coloring algorithms have been proposed in previous studies (Brélaz, 1979; Kim and Cho, 2013). However, the incurred crosstier interference in the macrocell tier when an SBS is colored with OBFD mode makes our contribution different from the conventional algorithms.
Now that the crosstier interference is taken into account, it is necessary to determine which type of interference is predominant for each SBS. For this reason, we consider an extreme case and make the following definition:
Remark 2 When all SBSs are in IBFD mode, only cotier interference exists. Under this condition, if the ith SBS changes into OBFD mode, then the original cotier interference incurred by SBS i disappears, but a new crosstier counterpart is created. As such, ξ_{i} represents the balance between cotier interference and crosstier interference induced by the ith SBS. If ξ_{i} is negative and low, it means that SBS i induces severe crosstier interference and tends to choose the IBFD mode. Conversely, a positive and high ξ_{i} shows that SBS i results in serious cotier interference, and it is better to operate in a different mode than its adjacent SBSs.
5.3 Distributed graph coloring algorithm
Capitalizing on the adjacent graph and price defined above, we are now ready to present our DGCA method. Without loss of generality, we restrict our attention to the ith SBS.
Remark 3 When both SBS i and SBS j operate in IBFD mode, it is trivial that their combined price is the summation of the individual prices. However, if they are in different modes, cotier interference no longer exists but one of them is bound to create crosstier interference. Without loss of generality, we employ a generalized function that decreases with respect to ξ to describe this combined price. ω is used as a weighting factor to represent how much crosstier interference will impact the considered system; e.g., ω = 0 means that the influence of crosstier interference can be ignored. Finally, if both SBSs work in OBFD mode, then the individual prices as well as the extra price induced by crosstier interference need to be considered.
Now we have the sum price of SBS i to the whole HetNet. To minimize the sum price, Proposition 3 can be easily obtained, if we compare the sum prices when SBS i chooses a different fullduplex mode.

Case 1 If ξ_{I} =0, then x_{i} = 1.
 Case 2 If ξ_{I} ≠ 0, ξ_{O} ≠ 0, then$${x_i} = \left\{ {\begin{array}{*{20}c} {1,} & {{\xi _{\rm{I}}} < {\xi _{\rm{O}}} + \omega \cdot {2^{  {\xi _i}}},} \\ {0,} & {{\rm{otherwise}}{\rm{.}}\quad \quad } \\ \end{array} } \right.$$(22)
 Case 3 If ξ_{I} ≠ 0, ξ_{O} ≠ 0, then$${x_i} = \left\{ {\begin{array}{*{20}c} {1,} & {{\xi _{\rm{I}}} + {\xi _i} < \omega \cdot {2^{  {\xi _i}}},} \\ {0,} & {{\rm{otherwise}}{\rm{.}}\quad \quad } \\ \end{array} } \right.$$(23)
Based on the results above, the specific DGCA is shown in Algorithm
. We can see that the computational overhead of MBS could be significantly reduced with SBSs affording plenty of computation in DGCA. Notably, we sort the SBSs by price in ascending order in step 2. This is because DGCA tends to choose IBFD mode for the first few SBSs due to small ξ_{I} and ξ_{O}, and in the meantime, the network benefits from assigning low price SBSs with IBFD mode. Moreover, there exist two parameters Γ_{th} and ω in DGCA, which play an important role in our algorithm. They may have different influences on the performance of DGCA in different scenarios, i.e., dense or sparse smallcell networks; thus, in our algorithm we assess a range of Γ_{th} and ω to choose the optimal Γ_{th} and ω.As for the analysis of complexity, we can see that in the phases of obtaining adjacent graphs and distributed coloring, i.e., step 1 and steps 3–6, the complexity is \({\mathcal O}({S^2})\); however, in the phase of obtaining the price of each SBS, i.e., step 2, the complexity is \({\mathcal O}({S^3})\), which makes the overall complexity of DGCA grow. Fortunately, step 2 needs only to be executed once in DGCA, and thus the computational complexity of DGCA will be less than those of GEA and GRA, which execute highcomplexity steps repeatedly.
6 Performance evaluation
List of simulation parameters
Parameter  Value 

Macrocell radius, R_{M}  1000 m 
Smallcell radius, R_{S}  40 m 
Carrier frequency, f_{c}  2 GHz 
System bandwidth, B  20 MHz 
Number of MUEs, K  20 
Transmit power of MBS, P_{M}  46 dBm 
Transmit power of SBS, P_{S}  24 dBm 
Noise power, σ^{2}  −174 dBm/Hz 
7 Conclusions
In this paper, focusing on the downlink of a twotier HetNet that incorporates massive MIMO employed in MBS and fullduplex employed in SBSs, an interference coordination framework is described and analyzed. To suppress the network interference and maximize the downlink throughput, two centralized algorithms, i.e., a genetic algorithm and a greedy algorithm, are proposed to decide the fullduplex mode selection strategies of SBSs. In particular, a distributed graph coloring algorithm, in which each SBS individually chooses the fullduplex mode, is further proposed to reduce the computation overhead of the MBS. Numerical results show that the proposed algorithms have significant performance gain compared with the benchmark, and there exists a tradeoff among the throughput performance, computation time, and computation overhead of the MBS when we estimate these three algorithms.
References
 3GPP, 2012a. Evolved universal terrestrial radio access (EUTRA); LTE physical layer; general description. Technical Specification No. 36.201 (v11.1.0), 3rd Generation Partnership Project.Google Scholar
 3GPP, 2012b. Evolved universal terrestrial radio access (EUTRA); further enhancements to LTE time division duplex (TDD) for downlinkuplink (DLUL) interference management and traffic adaptation. Technical Report No. 36.828 (v11.0.0), 3rd Generation Partnership Project.Google Scholar
 Bharadia, D., Katti, S., 2016. Fullduplex radios. In: Vannithamby, R., Talwar, S. (Eds.), Towards 5G: Applications, Requirements and Candidate Technologies. John Wiley & Sons, p.365–394. http://dx.doi.org/10.1002/9781118979846.ch16CrossRefGoogle Scholar
 Boccardi, F., Heath, R., Lozano, A., et al., 2014. Five disruptive technology directions for 5G. IEEE Commun. Mag., 52(2): 74–80. http://dx.doi.org/10.1109/mcom.2014.6736746CrossRefGoogle Scholar
 Brélaz, D., 1979. New methods to color the vertices of a graph. Commun. ACM, 22(4): 251–256. http://dx.doi.org/10.1145/359094.359101MathSciNetCrossRefGoogle Scholar
 Choi, J.I., Jain, M., Srinivasan, K., et al., 2010. Achieving single channel, full duplex wireless communication. 16th Annual Int. Conf. on Mobile Computing and Networking, p.1–12. http://dx.doi.org/10.1145/1859995.1859997Google Scholar
 Goyal, S., Liu, P., Hua, S., et al., 2013. Analyzing a fullduplex cellular system. 47th Annual Conf. on Information Sciences and Systems, p.1–6. http://dx.doi.org/10.1109/ciss.2013.6552310Google Scholar
 Goyal, S., Liu, P., Panwar, S., et al., 2014. Improving small cell capacity with commoncarrier full duplex radios. IEEE Int. Conf. on Communications, p.4987–4993. http://dx.doi.org/10.1109/icc.2014.6884111Google Scholar
 Hosseini, K., Hoydis, J., ten Brink, S., et al., 2013. Massive MIMO and small cells: how to densify heterogeneous networks. IEEE Int. Conf. on Communications, p.5442–5447. http://dx.doi.org/10.1109/icc.2013.6655455Google Scholar
 Hoydis, J., Kobayashi, M., Debbah, M., 2011. Green smallcell networks. IEEE Veh. Technol. Mag., 6(1): 37–43. http://dx.doi.org/10.1109/mvt.2010.939904CrossRefGoogle Scholar
 Hoydis, J., Hosseini, K., ten Brink, S., et al., 2013. Making smart use of excess antennas: massive MIMO, small cells, and TDD. Bell Labs Techn. J., 18(2): 5–21. http://dx.doi.org/10.1002/bltj.21602CrossRefGoogle Scholar
 Jain, M., Choi, J.I., Kim, T., et al., 2011. Practical, realtime, full duplex wireless. 17th Annual Int. Conf. on Mobile Computing and Networking, p.301–312. http://dx.doi.org/10.1145/2030613.2030647Google Scholar
 Kim, S., Cho, I., 2013. Graphbased dynamic channel assignment scheme for femtocell networks. IEEE Commun. Lett., 17(9): 1718–1721. http://dx.doi.org/10.1109/lcomm.2013.071013.130585CrossRefGoogle Scholar
 Larsson, E., Edfors, O., Tufvesson, F., et al., 2014. Massive MIMO for next generation wireless systems. IEEE Commun. Mag., 52(2): 186–195. http://dx.doi.org/10.1109/mcom.2014.6736761CrossRefGoogle Scholar
 Li, B., Zhu, D., Liang, P., 2015. Small cell inband wireless backhaul in massive MIMO systems: a cooperation of nextgeneration techniques. IEEE Trans. Wirel. Commun., 14(12): 7057–7069. http://dx.doi.org/10.1109/twc.2015.2464299CrossRefGoogle Scholar
 Liu, G., Yu, F.R., Ji, H., et al., 2015. Inband fullduplex relaying: a survey, research issues and challenges. IEEE Commun. Surv. Tutor., 17(2): 500–524. http://dx.doi.org/10.1109/comst.2015.2394324MathSciNetCrossRefGoogle Scholar
 Marzetta, T.L., 2010. Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans. Wirel. Commun., 9(11): 3590–3600. http://dx.doi.org/10.1109/twc.2010.092810.091092CrossRefGoogle Scholar
 Rusek, F., Persson, D., Lau, B.K., et al., 2013. Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Process. Mag., 30(1): 40–60. http://dx.doi.org/10.1109/msp.2011.2178495CrossRefGoogle Scholar
 Sabharwal, A., Schniter, P., Guo, D., et al., 2014. Inband fullduplex wireless: challenges and opportunities. IEEE J. Sel. Areas Commun., 32(9): 1637–1652. http://dx.doi.org/10.1109/jsac.2014.2330193CrossRefGoogle Scholar
 Tabassum, H., Sakr, A.H., Hossain, E., 2016. Analysis of massive MIMOenabled downlink wireless backhauling for fullduplex small cells. IEEE Trans. Commun., 64(6): 2354–2369. http://dx.doi.org/10.1109/tcomm.2016.2555908CrossRefGoogle Scholar
 Thilina, K.M., Tabassum, H., Hossain, E., et al., 2015. Medium access control design for full duplex wireless systems: challenges and approaches. IEEE Commun. Mag., 53(5): 112–120. http://dx.doi.org/10.1109/mcom.2015.7105649CrossRefGoogle Scholar