In this paper, we investigate the problem of optimal base station (BS) ON-OFF switching and user association in a heterogeneous network (HetNet) with massive MIMO, with the objective to maximize the system energy efficiency (EE). The joint BS ON-OFF switching and user association problem is formulated as an integer programming problem. We first develop a centralized scheme, in which we relax the integer constraints and employ a series of Lagrangian dual methods that transform the original problem into a standard linear programming (LP) problem. Due to the special structure of the LP, we prove that the optimal solution to the relaxed LP is also feasible and optimal to the original problem. We then propose a distributed scheme by formulating a repeated bidding game for users and BS's, and prove that the game converges to a Nash Equilibrium (NE). Simulation studies demonstrate that the proposed schemes can achieve considerable gains in EE over several benchmark schemes in all the scenarios considered.
As key technologies for 5G network, massive MIMO and small cells have been extensively studied in prior works. The fundamental PHY layer techniques of massive MIMO were introduced in @cite_31 @cite_20 . Beyond the PHY, upper layer technologies in a wireless network with massive MIMO are also considered, such as @cite_14 @cite_16 @cite_27 . @cite_16 , user association and resource allocation in a massive MIMO HetNet were investigated with the objectives of rate maximization and rate maximization with proportional fairness. @cite_27 , a time-shift frame structure was proposed to mitigate inter-cell interference caused by pilot contamination in a multi-cell massive MIMO system. Since neighboring cells transmit pilots at different time instants, the inter-cell interference can be well mitigated.
@cite_14 @cite_27 @cite_31 @cite_16 @cite_20
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Massive multiple-input–multiple-output (MIMO) and small cell are both recognized as key technologies for the future fifth-generation wireless systems. In this paper, we investigate the problem of user association in a heterogeneous network (HetNet) with massive MIMO and small cells, where the macro base station (BS) is equipped with a massive MIMO, and the picocell BSs are equipped with regular MIMOs. We first develop centralized user association algorithms with proven optimality, considering various objectives such as rate maximization, proportional fairness, and joint user association and resource allocation. We then develop a repeated game model, which leads to distributed user association algorithms with proven convergence to the Nash equilibrium. We demonstrate the efficacy of these optimal schemes by comparing with several greedy algorithms through simulations. We consider a heterogeneous cellular network (HetNet) where a macrocell tier with a large antenna array base station (BS) is overlaid with a dense tier of small cells (SCs). We investigate the potential benefits of incorporating a massive MIMO BS in a TDD-based HetNet and we provide analytical expressions for the coverage probability and the area spectral efficiency using stochastic geometry. The duplexing mode in which SCs should operate during uplink macrocell transmissions is optimized. Furthermore, we consider a reverse TDD scheme, in which the massive MIMO BS can estimate the SC interference covariance matrix. Our results suggest that significant throughput improvement can be achieved by exploiting interference nulling and implicit coordination across the tiers due to flexible and asymmetric TDD operation. Massive MIMO is one of the most promising approaches for coping with the predicted wireless data traffic explosion. Future deployment scenarios will involve dense heterogeneous networks, comprised of massive MIMO base stations with different powers, numbers of antennas and multiplexing gain capabilities, and possibly highly nonhomogeneous user density (hot-spots). In such dense irregularly deployed networks, it will be important to have mechanisms for associating users to base stations so that the available wireless infrastructure is efficiently used. In this paper, we consider the optimal user-cell association problem for massive MIMO heterogeneous networks and illustrate how massive MIMO can also provide nontrivial advantages at the system level. Unlike previous treatments that rely on integer program problem formulations and their convex relaxations, the user-cell association problem is formulated directly as a convex network utility maximization and solved efficiently by a centralized subgradient algorithm. As we show, the globally optimal solution is physically realizable , in that there exists a sequence of integer-valued associations approaching arbitrarily closely the optimal fractional association. We also consider simple decentralized user-centric association schemes, where each user individually and selfishly connects to the base station with the highest promised throughput. Such user-centric schemes where users make local association decisions in a probabilistic manner can be viewed as games and are known to converge to Nash equilibria. Surprisingly, as we show, under certain conditions, the globally optimal solution is close to these Nash equilibria. Such decentralized approaches are, therefore, attractive not only for their simplicity, but also because they operate near the system social optimum. Our theoretical results are confirmed by extensive simulations with realistic LTE-like network parameters. Large multiple-input multiple-output (MIMO) networks promise high energy efficiency, i.e., much less power is required to achieve the same capacity compared to the conventional MIMO networks if perfect channel state information (CSI) is available at the transmitter. However, in such networks, huge overhead is required to obtain full CSI especially for Frequency-Division Duplex (FDD) systems. To reduce overhead, we propose a downlink antenna selection scheme, which selects S antennas from M > S transmit antennas based on the large scale fading to serve K ≤ S users in large distributed MIMO networks employing regularized zero-forcing (RZF) precoding. In particular, we study the joint optimization of antenna selection, regularization factor, and power allocation to maximize the average weighted sum-rate. This is a mixed combinatorial and non-convex problem whose objective and constraints have no closed-form expressions. We apply random matrix theory to derive asymptotically accurate expressions for the objective and constraints. As such, the joint optimization problem is decomposed into subproblems, each of which is solved by an efficient algorithm. In addition, we derive structural solutions for some special cases and show that the capacity of very large distributed MIMO networks scales as O(KlogM) when M→∞ with K, S fixed. Simulations show that the proposed scheme achieves significant performance gain over various baselines.