Channel Estimation for Massive MIMO systems using Tensor Cores in GPU

TL;DR

This paper presents a novel approach for massive MIMO channel estimation that combines a pilot transmission scheme with tensor core acceleration on GPUs, significantly reducing latency and pilot overhead.

Contribution

It introduces a GPU-accelerated channel estimation method using Tensor Cores and a pilot scheme to improve efficiency in massive MIMO systems.

Findings

Reduced processing latency with Tensor Core implementation.

Lower pilot overhead due to the new transmission scheme.

Maintained estimation accuracy across various MIMO configurations.

Abstract

For efficient use of Massive MIMO systems, fast and accurate channel estimation is very important. But the Large-scale antenna array presence requires high pilot overhead for high accuracy of estimation. Also, when used with software-based processing systems like CPUs and GPUs, high processing latency becomes a major issue. To reduce Pilot overhead, a Pilot transmission scheme in combination with PN Sequence correlation based channel estimation scheme is implemented. Then, to deal with the issue of high processing latency, Tensor Cores in Nvidia GPUs are used for computing the channel estimation. Experiments are performed by using Nvidia V100 GPU in the ORBIT Testbed to show the performance of the Pilot transmission scheme. By varying factors like PN sequence length, Channel Impulse Response length, number of multiplexed transmitters, and scale of MIMO, the accuracy and processing latency of Tensor Core implementation of the Channel Estimation is evaluated.