Impact of Channel Correlation on Subspace-Based Activity Detection in Grant-Free NOMA

TL;DR

This paper examines how channel correlation affects activity detection in grant-free NOMA, proposing masking sequences and pilot reallocation strategies to mitigate performance degradation.

Contribution

It introduces methods to counteract the negative effects of channel correlation on subspace-based activity detection in grant-free NOMA systems.

Findings

High channel correlation deteriorates detection performance.

User-specific masking sequences improve robustness.

Pilot reallocation strategies can mitigate channel selectivity effects.

Abstract

In this paper, we consider the problem of activity detection in grant-free code-domain non-orthogonal multiple access (NOMA). We focus on performing activity detection via subspace methods under a setup where the data and pilot spreading signatures are of different lengths, and consider a realistic frame-structure similar to existing mobile networks. We investigate the impact of channel correlation on the activity detection performance; first, we consider the case where the channel exhibits high correlation in time and frequency and show how it can heavily deteriorate the performance. To tackle that, we propose to apply user-specific masking sequences overlaid on top of the pilot signatures. Second, we consider the other extreme with the channel being highly selective, and show that it can also negatively impact the performance. We investigate possible pilots' reallocation strategies that can help reduce its impact.