Energy Efficiency Maximization Precoding for Quantized Massive MIMO Systems
Jinseok Choi, Jeonghun Park, and Namyoon Lee

TL;DR
This paper develops an energy-efficient precoding and antenna selection method for quantized massive MIMO systems, using a decomposed optimization approach and iterative algorithms to improve performance with low-resolution converters.
Contribution
It introduces a novel joint precoding and antenna selection optimization framework tailored for quantized massive MIMO, employing a functional eigenvalue problem and power iteration algorithms.
Findings
Significant energy efficiency gains demonstrated in simulations.
Few-bit DACs are sufficient for high EE in massive MIMO.
Proposed algorithms outperform existing methods in efficiency and performance.
Abstract
The use of low-resolution digital-to-analog and analog-to-digital converters (DACs and ADCs) significantly benefits energy efficiency (EE) at the cost of high quantization noise in implementing massive multiple-input multiple-output (MIMO) systems. For maximizing EE in quantized downlink massive MIMO systems, this paper formulates a precoding optimization problem with antenna selection; yet acquiring the optimal joint precoding and antenna selection solution is challenging due to the intricate EE characterization. To resolve this challenge, we decompose the problem into precoding direction and power optimization problems. For precoding direction, we characterize the first-order optimality condition, which entails the effects of quantization distortion and antenna selection. For precoding power, we obtain the optimum solution using a gradient descent algorithm to maximize EE for given…
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Taxonomy
TopicsAdvanced MIMO Systems Optimization · Energy Harvesting in Wireless Networks · Millimeter-Wave Propagation and Modeling
