Analog to Digital Cognitive Radio: Sampling, Detection and Hardware

TL;DR

This paper explores sub-Nyquist sampling techniques for cognitive radio spectrum sensing, focusing on signal recovery, noise robustness, collaborative detection, and practical hardware implementation to improve spectrum utilization.

Contribution

It introduces novel methods for sub-Nyquist spectrum sensing, including second-order statistics recovery and joint detection with direction estimation, validated through hardware simulations.

Findings

Sub-Nyquist sampling enables effective spectrum sensing in cognitive radio.

Second-order statistics recovery improves detection in low SNR conditions.

Hardware prototype demonstrates practical feasibility of the proposed methods.

Abstract

The proliferation of wireless communications has recently created a bottleneck in terms of spectrum availability. Motivated by the observation that the root of the spectrum scarcity is not a lack of resources but an inefficient managing that can be solved, dynamic opportunistic exploitation of spectral bands has been considered, under the name of Cognitive Radio (CR). This technology allows secondary users to access currently idle spectral bands by detecting and tracking the spectrum occupancy. The CR application revisits this traditional task with specific and severe requirements in terms of spectrum sensing and detection performance, real-time processing, robustness to noise and more. Unfortunately, conventional methods do not satisfy these demands for typical signals, that often have very high Nyquist rates. Recently, several sampling methods have been proposed that exploit signals' a priori known structure to sample them below the Nyquist rate. Here, we review some of these techniques and tie them to the task of spectrum sensing in the context of CR. We then show how issues related to spectrum sensing can be tackled in the sub-Nyquist regime. First, to cope with low signal to noise ratios, we propose to recover second-order statistics from the low rate samples, rather than the signal itself. In particular, we consider cyclostationary based detection, and investigate CR networks that perform collaborative spectrum sensing to overcome channel effects. To enhance the efficiency of the available spectral bands detection, we present joint spectrum sensing and direction of arrival estimation methods. Throughout this work, we highlight the relation between theoretical algorithms and their practical implementation. We show hardware simulations performed on a prototype we built, demonstrating the feasibility of sub-Nyquist spectrum sensing in the context of CR.