GPU-accelerated partially linear multiuser detection for 5G and beyond URLLC systems

TL;DR

This paper demonstrates GPU acceleration of a partially linear multiuser detection algorithm in RKHSs, enabling real-time, low-latency detection for 5G URLLC systems by parallelizing inner product computations.

Contribution

It introduces a GPU-based parallelization approach for inner product operations in RKHSs, achieving sub-millisecond latency for multiuser detection in OFDM systems.

Findings

GPU acceleration significantly reduces detection latency.

Real-time detection meets 5G URLLC latency requirements.

Parallelization techniques are applicable to other Hilbert space algorithms.

Abstract

In this feasibility study, we have implemented a recently proposed partially linear multiuser detection algorithm in reproducing kernel Hilbert spaces (RKHSs) on a GPU-accelerated platform. Partially linear multiuser detection, which combines the robustness of linear detection with the power of nonlinear methods, has been proposed for a massive connectivity scenario with the non-orthogonal multiple access (NOMA). This is a promising approach, but detecting payloads within a received orthogonal frequency division multiplexing (OFDM) radio frame requires the execution of a large number of inner product operations, which are the main computational burden of the algorithm. Although inner-product operations consist of simple kernel evaluations, their vast number poses a challenge in ultra-low latency (ULL) applications, because the time needed for computing the inner products might exceed the sub-millisecond latency requirement. To address this problem, this study demonstrates the acceleration of the inner-product operations through massive parallelization. The result is a GPU-accelerated real-time OFDM receiver that enables sub-millisecond latency detection to meet the requirements of 5th generation (5G) and beyond ultra-reliable and low latency communications (URLLC) systems. Moreover, the parallelization and acceleration techniques explored and demonstrated in this study can be extended to many other signal processing algorithms in Hilbert spaces, such as those based on projection onto convex sets (POCS) and adaptive projected subgradient method (APSM) algorithms. Experimental results and comparisons with the state-of-art confirm the effectiveness of our techniques.