Low‐complexity heuristics to beam selection and rate adaptation in sparse massive MIMO systems

Abstract

In this work, we propose a novel formulation for a precoder design considering practical rate assignment based on the modulation and coding scheme of the long‐term evolution table, beam selection, and power optimization, which exploits the geometric sparsity of the multiuser massive multiple‐input–multiple‐output channel. We consider two different channel models and provide an optimal solution for the joint beam selection and power optimization as well as a heuristic using Lagrangian relaxation. Assuming knowledge of the beamspace channel, the beamspace precoder consists in selecting and optimizing the power of the beams steered to the user equipments (UEs) in order to maximize the signal‐to‐interference‐plus‐noise ratio at the UE. Furthermore, we propose three additional simple heuristics with low complexity. For these three heuristics, we solve the problem in two steps: (1) selection of beams based on the maximal ratio transmission principle and (2) power allocation per UE. Simulation results show that our optimal solution can achieve a better performance than the zero‐forcing beamforming scheme in the high‐sparsity scenarios. Besides, compared to the linear maximal ratio transmission precoder, the proposed low‐complexity heuristics improve the performance under a scenario with channel sparsity.

Maximizing Signal to Interference Noise Ratio for Massive MIMO: A Stochastic Neurodynamic Approach

“…We aim to maximize the worst user SINR subject to some limits on the power assigned to each user. The SINR i for user i can be expressed as follows [18]…”

A stochastic geometric programming approach for power allocation in wireless networks

“…In (M 1 ), the objective function ( 5) denotes the worst user SINR [38,39]. Whilst the constraints (6) ensure that each power variable is greater than or equal to P min and less than or equal to P max .…”