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.
Multi-beam concurrent transmission is one of promising solutions for a millimeter-wave (mmWave) network to provide seamless handover, robustness to blockage, and continuous connectivity. Nevertheless, one of the major obstacles in multi-beam concurrent transmissions is the optimization of beam pair selection, which is essential to improve the mmWave network performance. Therefore, in this paper, we propose a novel heterogeneous multi-beam cloud radio access network (HMBCRAN) architecture which provides seamless mobility and coverage for mmWave networks. We also design a novel acquirement method for candidate beam pair links (BPLs) in HMBCRANs architecture, which reduces user power consumption, signaling overhead, and overall time consumption. Based on HMBCRANs architecture and the resulted candidate BPLs for each user equipment, a beam pair selection optimization problem aiming at maximizing network sum rate is formulated. To find the solution efficiently, the considered problem is reformulated as a non-operative game with local interaction, which only needs local information exchanging among players. A decentralized algorithm based on HMBCRANs architecture and binary log-linear learning is proposed to obtain the optimal pure strategy Nash equilibrium of the proposed game, in which a concurrent multi-player selection scheme and an information exchanging protocol among players are developed to reduce the complexity and signal overheads. The stability, optimality, and complexity of the proposed algorithm are analyzed via theoretical and simulation method. The results prove that the proposed scheme has better convergence speed and sum rate against the state-of-the-art schemes. 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. The approaching 5G era of cellular communications is posing stringent performance requirements. New groundbreaking applications can be enabled only by means of multi-Gbps data rates and ultra-low latencies. The spectrum scarcity at frequencies below 6 GHz stimulated a new wave of wireless research that focuses on higher bands, namely mmWave. Directionality and high penetration loss represent the key challenges when operating with such carriers. The resulting intermittent connectivity makes the user association problem even more complex and critical than in previous generations of cellular systems, where the channel was better behaved. In this paper, we aim at deriving an optimal and fair cell selection policy that encapsulates the reallocation cost of potential handovers, and captures the erratic nature of the mmWave channel. An important conclusion is that (i) if there is no, or minimal, reallocation cost, each user associates with a single BS, while (ii) for higher handover cost values, users tend to connect to multiple base stations simultaneously.