Energy efficient downlink massive MIMO: Is 1-bit quantization a solution ?

Abstract

Massive MIMO aims to build wireless base stations with hundreds of coherently operating antennas serving tens of single antenna users in order to improve both the transmission capacity by a factor 10-50 and the energy-efficiency trade-off by up to a thousand times. Precoding at the base station has been proposed to efficiently implement digital beamforming. It implies a high signal dynamic range and therefore a power backoff resulting in less energy-efficiency. One-bit quantized Zero-Forcing precoding has been proposed to efficiently handle the RF front-end when the array is implemented with so many antennas. In this paper, we analyze the energy-efficiency of the quantized Zero-Forcing precoded systems for a large number of users and a massive MIMO base station.

Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering

“…Therefore, to further reduce the memory energy consumption, one option is to properly optimize the quantization to reduce the memory requirements of the implementation. Significant energy gains from optimized quantization have been demonstrated in [ 28 , 29 , 30 ] for signal processing and digital communications applications and in [ 31 , 32 , 33 ] for neural networks. The effects of quantization on the Kalman filter were first studied in [ 34 , 35 ] to understand the convergence of filters with reduced precision.…”