A metaheuristic-based downlink power allocation for LTE/LTE-A cellular deployments

Abstract

The explosive growth of cellular networks makes their deployment and maintenance more and more complex, time consuming, and expensive. Self-Organizing Networks have been recognized as a promising way to alleviate this problem by minimizing human intervention in such processes. This paper introduces a novel multiobjective framework, based on evolutionary optimization, aiming at improving network performance and users Quality of Service. By tuning the transmitted power at each cell, average intercell interference levels are minimized. The design of the proposed scheme is feasible for distributed implementations in Long Term Evolution (LTE) and LTE-Advanced networks and its operation is compatible with current specifications. The framework is able to provide effective network-specific optimization and obtained results show that gains in terms of network capacity and cell edge performance are 5 and 10 %, respectively. Energy savings always accompanied such enhancements with reductions up to 35 %.Peer ReviewedPostprint (published version

Achieving power and energy efficiency in self-organizing networks

“…Power allocation has been widely used to maximize UE capacity and to minimize inter-cell interference. In [9], the proposed meta-heuristic-based downlink power allocation for LTE/LTE-A provides the required QoS by tuning the transmit power at each cell and minimizing the average inter-cell interference level. In [10], a semi-distributed neighboring gradient information based algorithm and a fully distributed heuristic based algorithm were proposed to automatically create soft FFR patterns in OFDMA based systems.…”

The automatic configuration of transmit power in LTE networks based on throughput estimation

“…Our goal is to find the highest possible throughput achievable in particular network deployment by tuning the transmitted power. In [5] authors solved mentioned problem by the multiobjective optimization. We improve this sollution by giving a higher resolution of the SINR heatmap.…”

Performance analysis and optimization of next generation wireless networks

“…The Street Specific Handover for Vehicular Terminals (SSHVeT) [180] algorithm considers street aware parameters to evaluate the necessity of performing a handover. Parameters such as vehicle velocity, road direction, Reference Signals Received Power (RSRP) [181] and radio propagation characteristics are considered for the HO initiation. The algorithm adapts the corresponding handover thresholds considering the obtained parameter values for each street, in order the ping-pong effect to be minimized.…”