Compressive Link Acquisition in Multiuser Communications

TL;DR

This paper introduces a sparsity-aware compressive sampling scheme for multiuser link acquisition, balancing complexity and performance by leveraging sub-Nyquist sampling and sparse recovery techniques.

Contribution

It proposes a novel sequential acquisition method combining compressive sampling with a likelihood ratio test, optimizing the trade-off between sampling cost and detection accuracy.

Findings

The proposed scheme outperforms traditional methods like matched filtering in certain scenarios.

It achieves comparable detection performance with fewer samples, reducing computational complexity.

The method effectively maximizes the Kullback-Leibler divergence across hypotheses, enhancing detection robustness.

Abstract

An important receiver operation is to detect the presence specific preamble signals with unknown delays in the presence of scattering, Doppler effects and carrier offsets. This task, referred to as "link acquisition", is typically a sequential search over the transmitted signal space. Recently, many authors have suggested applying sparse recovery algorithms in the context of similar estimation or detection problems. These works typically focus on the benefits of sparse recovery, but not generally on the cost brought by compressive sensing. Thus, our goal is to examine the trade-off in complexity and performance that is possible when using sparse recovery. To do so, we propose a sequential sparsity-aware compressive sampling (C-SA) acquisition scheme, where a compressive multi-channel sampling (CMS) front-end is followed by a sparsity regularized likelihood ratio test (SR-LRT) module. The proposed C-SA acquisition scheme borrows insights from the models studied in the context of sub-Nyquist sampling, where a minimal amount of samples is captured to reconstruct signals with Finite Rate of Innovation (FRI). In particular, we propose an A/D conversion front-end that maximizes a well-known probability divergence measure, the average Kullback-Leibler distance, of all the hypotheses of the SR-LRT performed on the samples. We compare the proposed acquisition scheme vis-\`{a}-vis conventional alternatives with relatively low computational cost, such as the Matched Filter (MF), in terms of performance and complexity.