A genetic algorithm-based beamforming approach for delay-constrained networks

Abstract

In this paper, we study the performance of initial access beamforming schemes in the cases with large but finite number of transmit antennas and users. Particularly, we develop an efficient beamforming scheme using genetic algorithms. Moreover, taking the millimeter wave communication characteristics and different metrics into account, we investigate the effect of various parameters such as number of antennas/receivers, beamforming resolution as well as hardware impairments on the system performance. As shown, our proposed algorithm is generic in the sense that it can be effectively applied with different channel models, metrics and beamforming methods. Also, our results indicate that the proposed scheme can reach (almost) the same end-to-end throughput as the exhaustive search-based optimal approach with considerably less implementation complexity.

A comparison of beam refinement algorithms for millimeter wave initial access

“…1). This is an extension of our work [15] with single receive antennas, and allows for beamforming at the receiver side. We assume that each user has perfect receiver CSI.…”

“…In [15], we introduce a genetic algorithm-based initial beamforming approach and evaluate the effect of the algorithm running delay on the network performance. There are also previous works using the GA-based selection approach in different communication networks.…”

“…We use this model because most cases in MMW systems have the LOS channel. Note that there are additional simulation results with different settings of k in [15] assuming single antenna users.…”

“…The machine learning based schemes of [4]- [7] and [15] are generic, in the sense that they can be implemented for different metrics. For the simulations, however, we consider the service outage-constrained end-to-end throughput, the complexity and the average number of required iterations as the system performance metric.…”

“…Extended GA-based Search [15]: The algorithm starts by making L possible beam selection sets at both transmitter and receiver, i.e., submatrices of each codebook. During each iteration, we choose the best set, named as the Queen, based on the performance metrics (for example, (5)).…”