# Energy Efficiency Maximization Precoding for Quantized Massive MIMO Systems

arXiv: https://arxiv.org/abs/2108.03048 · HTML: https://mlsanity.com/paper/2108.03048/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2021.

## Built on by (most cited first, 5 of 5)

- [Compression of Site-Specific Deep Neural Networks for Massive MIMO Precoding](https://mlsanity.com/paper/2502.08758/) (2025)
- [Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach](https://mlsanity.com/paper/2507.10634/) (2025)
- [Energy-Efficient State Estimation with 1-Bit Sensing: A Bussgang-Kalman Framework for Internet of Things](https://mlsanity.com/paper/2507.17284/) (2025)
- [Quantization-Aware EE Optimization and SE-EE Tradeoff for MiLAC-Aided MU-MISO Beamforming](https://mlsanity.com/paper/2604.24538/) (2026)
- [Data Detection for Massive MIMO Systems with 1-Bit Quantized Dithered Linear Precoding](https://mlsanity.com/paper/2606.05447/) (2026)

## Abstract (arXiv)

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 precoding direction. We cast the derived condition as a functional eigenvalue problem, wherein finding the principal eigenvector attains the best local optimal point. To this end, we propose generalized power iteration based algorithm. Alternating these two methods, our algorithm identifies a joint solution of the active antenna set and the precoding direction and power. In simulations, the proposed methods provide considerable performance gains. Our results suggest that a few-bit DACs are sufficient for achieving high EE in massive MIMO systems.