Precoder Implementation and Optimization in 5G NR Massive MIMO Radio

TL;DR

This paper presents the implementation and optimization of linear precoders in a 5G NR Massive MIMO system, demonstrating efficient hardware utilization and high data throughput in a real testbed.

Contribution

It introduces a hardware implementation of 16, 32, and 64 channel precoders with channel matrix storage and matrix multiplication modules optimized for 5G NR Massive MIMO.

Findings

DSP utilization up to 39% for 64x8 precoders

Block RAM usage within 7.16% for large configurations

Achieved throughput of 1.2 Gbps with four active layers

Abstract

The evolution of 5G New Radio (NR) has brought significant improvements in signal strength and service quality for users. By integrating Multiple Input Multiple Output (MIMO) systems into communications, multiple data streams can be transmitted simultaneously across multiple antennas. Additionally, the incorporation of precoding in MIMO systems enables enhanced data rates and spectral efficiency. In wireless networks, precoders are used to steer high-gain beams intended for specific users. This paper focuses on the implementation of 16, 32, and 64 channel linear precoders in the Remote Radio Head (RRH) of the indigenously developed 5G testbed at IIT Madras. These precoders include a memory module to store channel matrices and a multiplier module to perform matrix multiplications between the channel matrices and user data within a slot duration of 500 microseconds. The system demonstrates DSP utilization levels of 9.75%, 19.5%, and 39% for (16 x 8), (32 x 8), and (64 x 8) antenna-layer configurations, respectively, while maintaining Block RAM (BRAM) usage within 2.28%, 3.91%, and 7.16%. Additionally, a throughput of 1.2 Gbps with four active layers highlights the system's optimized performance under hardware constraints.