User association is necessary in dense millimeter wave (mmWave) networks to determine which base station a user connects to in order to balance base station loads and maximize throughput. Given that mmWave connections are highly directional and vulnerable to small channel variations, user association changes these connections and hence significantly affects the user's instantaneous rate as well as network interference. In this paper, we introduce a new load balancing user association scheme for mmWave MIMO networks which considers this dependency on user association of user's transmission rates and network interference. We formulate the user association problem as mixed integer nonlinear programming and design a polynomial-time algorithm, called Worst Connection Swapping (WCS), to find a near-optimal solution. Simulation results confirm that the proposed user association scheme improves network performance significantly by moving the traffic of congested base stations to lightly-loaded ones and adjusting the interference accordingly. Further, the proposed WCS algorithm outperforms other generic algorithms for combinatorial programming such as the genetic algorithm in both accuracy and speed at several orders of magnitude faster, and for small networks where exhaustive search is possible it reaches the optimal solution.
In a similar work to @cite_37 , the problem of user association in HetNets with massive MIMO BSs and single-antenna UEs is studied in @cite_7 . Because of the channel hardening effect in massive MIMO, the considered association approach is independent of the user instantaneous rate or instantaneous channel state information (CSI), but only depends on converging deterministic rate values as a function of the system parameters and large-scale CSI. Even though unique association is considered, the association coefficients are not necessarily integers, but represent the limit of the fraction of time slots in which each UE is served by one BS. Using this interpretation of fractional user association as long-term time average, the researchers then formulated user association as a convex network utility maximization problem and solved it using Lagrangian duality.
@cite_37 @cite_7
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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. With increasing cell density and the heterogeneity in the network, optimal user-cell association which is a well known open problem, will become an even more challenging issue. Contrary to the current studies that address user-cell association problem for convectional HetNets with massive MIMO deployments in HF (high frequencies) ranges, in this paper we investigate user-cell association problem for dense two-tier networks with massive MIMO deployment both at macro and femto-tier operating in HF and mmWave spectrum, respectively. We evaluate the performance of four user-cell association algorithms for massive MIMO deployment in a two-tier network under two different deployment scenarios: 1) HF-HF (both tiers operating in HF band); 2) HF-mmWave (MBSs operating in HF while FBSs in mmWave bands. To this end, we model the association problem in form of a convex network utility maximization problem as a function of the downlink user throughput. Contrary to the existing load aware association schemes that preclude the effect of bandwidth disparity in HF and mmWave bands, we propose a modified utility function that takes into account the effect of large bandwidth at mmWave bands. The problem is solvable through centralized as well as distributed or user centric load aware user association schemes. 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. We consider the interference management problem in a multicell MIMO heterogeneous network. Within each cell there is a large number of distributed micro pico base stations (BSs) that can be potentially coordinated for joint transmission. To reduce coordination overhead, we consider user-centric BS clustering so that each user is served by only a small number of (potentially overlapping) BSs. Thus, given the channel state information, our objective is to jointly design the BS clustering and the linear beamformers for all BSs in the network. In this paper, we formulate this problem from a sparse optimization perspective, and propose an efficient algorithm that is based on iteratively solving a sequence of group LASSO problems. A novel feature of the proposed algorithm is that it performs BS clustering and beamformer design jointly rather than separately as is done in the existing approaches for partial coordinated transmission. Moreover, the cluster size can be controlled by adjusting a single penalty parameter in the nonsmooth regularized utility function. The convergence of the proposed algorithm (to a stationary solution) is guaranteed, and its effectiveness is demonstrated via extensive simulation.