Genetic Algorithm‐Based Beam Refinement for Initial Access in Millimeter Wave Mobile Networks

Abstract

Initial access (IA) is identified as a key challenge for the upcoming 5G mobile communication system operating at high carrier frequencies, and several techniques are currently being proposed. In this paper, we extend our previously proposed efficient genetic algorithm-(GA-) based beam refinement scheme to include beamforming at both the transmitter and the receiver and compare the performance with alternative approaches in the millimeter wave multiuser multiple-input-multiple-output (MU-MIMO) networks. Taking the millimeter wave communications characteristics and various metrics into account, we investigate the effect of different parameters such as the number of transmit antennas/users/per-user receive antennas, beamforming resolutions, and hardware impairments on the system performance employing different beam refinement algorithms. As shown, our proposed GA-based approach performs well in delay-constrained networks with multiantenna users. Compared to the considered state-of-the-art schemes, our method reaches the highest service outage-constrained end-to-end throughput with considerably less implementation complexity. Moreover, taking the users' mobility into account, our GA-based approach can remarkably reduce the beam refinement delay at low/moderate speeds when the spatial correlation is taken into account. Finally, we compare the cases of collaborative users and noncollaborative users and evaluate their difference in system performance.

On Topology Optimization and Routing in Integrated Access and Backhaul Networks: A Genetic Algorithm-Based Approach

“…In Fig. 16, we compare the coverage probability of the proposed GA-based scheme with those obtained by different state-of-the-art algorithms including exhaustive search, Tabu algorithm [68] and Greedy algorithm [69]. Note that Tabu is an evolutionary algorithm with a specific method of producing the generations (see [70] for details) while with Greedy algorithm, e.g., the non-IAB backhaul links locations are determined oneby-one [69].…”

Development Challenges of Millimeter‐Wave 5G Beamformers

“…This formulation is then solved by schemes like interference award beam selection e.g. (Guo et al, 2018). Best possible design of hybrid beamforming structure is generally evaluated based on the number of a receiver (denoted as N) and the number of active antenna elements per receiver (denoted as M).…”

“…For a small number of RF chains, algorithm shown in (Pal, Srinivas and Chaitanya, 2018) is expected to work well with the evaluated channel state. Along similar lines, the impact of different parameters like number of users, antennas and number of antenna per user on beamforming resolution and hardware impairments is evaluated in (Guo, Makki and Svensson, 2018).…”