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In heterogeneous networks (HetNets), the load between macrocell base stations (MBSs) and small-cell base stations (SBSs) is imbalanced due to their different transmission powers and locations. This load imbalance significantly impacts system performance and affects the experience of mobile users (MUs) with different priorities. In this paper, we aim to distributively optimize the user association in HetNets with various user priorities to solve the load balancing problem. Since the user association is a binary matching problem, which is NP-hard, we propose a distributed belief propagation (BP) algorithm to approach the optimal solution. We first develop a factor graph model, using the network topology, to represent this user association problem. With this factor graph, we propose a novel distributed BP algorithm by adopting the proportional fairness as the objective. Next, we theoretically prove the existence of the fixed point in our BP algorithm. To be more practical, we develop an approximation method to significantly reduce the computational and communication complexity of the BP algorithm. Furthermore, we analyze some properties of the factor graph relevant to the performance of the BP algorithm using the stochastic geometry. Simulation results show that 1) the proposed BP algorithm well approaches the optimal system performance and achieves a much better performance compared with other association schemes and that 2) the analytical results on the average degree distribution and sparsity of the factor graph match with those obtained from the Monte Carlo simulations. [ABSTRACT FROM PUBLISHER]

Ultradense heterogeneous networks (HetNets) are emerging as an inevitable approach to tackle the capacity crunch in cellular networks. However, imbalanced load among small and macrocells and poor resource utilization as a consequence in HetNets remains a long-standing problem. This paper addresses this problem by presenting a solution for maximization of coverage and capacity while minimizing load imbalance among macro and small cells. Most recent studies on the topic focus on either optimization of coverage, capacity or load, or a combination of two of these three intertwined objectives. We formulate the optimization problem as a function of two hard parameters namely antenna tilt and transmit power, and a soft parameter, cell individual offset, that affect the coverage, capacity, and load directly. The resulting solution is a combination of the otherwise conflicting coverage and capacity optimization (CCO) and load balancing (LB) self-organizing network (SON) functions. In the presented joint CCO-LB solution, a conflict free operation of CCO and LB is ensured by designing a novel load aware user association methodology and resolving the effects of shadowing on coverage probability using stochastic approximation. The problem is proven to be nonconvex and is solved using genetic algorithm, sequential quadratic programming, and pattern search algorithms. The proposed CCO-LB solution is compared against two recently proposed CCO and CCO-LB solutions in the literature. Results show that the proposed solution can yield significant gain in terms of throughput, spectral efficiency, and load distribution. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of relay assignment in cooperative wireless networks having multiple sources, multiple relays, and a single destination. With the objective of maximizing the minimum capacity among all sources in the network, a mixed-integer linear programming (MILP) problem is formulated, which can be solved by standard branch-and-bound algorithms. To reduce computational complexity, a greedy solution in the form of a lexicographic bottleneck assignment algorithm is proposed. Simulation results obtained for the IEEE 802.16j uplink scenarios show that the greedy algorithm performs very close to the optimal solution but at a much lower computational cost. The proposed greedy solution can also be tailored to provide further improvements on other network performance criteria. [ABSTRACT FROM PUBLISHER]

Fifth generation new radio aims to facilitate new use cases in wireless communications. Some of these new use cases have highly demanding latency requirements; many of the powerful forward error correction codes deployed in current systems, such as the turbo and low-density parity-check codes, do not perform well when the low-latency requirement does not allow iterative decoding. As such, there is a rejuvenated interest in noniterative/one-shot decoding algorithms. Motivated by this, we propose a signal-to-noise ratio-adaptive convolutionally coded system with optimized constellations designed specifically for a particular set of convolutional code parameters. Numerical results show that significant performance improvements in terms of bit-error-rate and spectral efficiency can be obtained compared to the traditional adaptive modulation and coding systems in low-latency communications. [ABSTRACT FROM AUTHOR]