Throughput Optimized Non-Contiguous Wideband Spectrum Sensing via Online Learning and Sub-Nyquist Sampling

TL;DR

This paper introduces a throughput-optimized wideband spectrum sensing method using online learning and sub-Nyquist sampling, enabling efficient detection of vacant frequency bands in heterogeneous networks.

Contribution

It proposes a novel online learning algorithm for non-contiguous wideband spectrum sensing that maximizes sensed bands without prior spectrum knowledge.

Findings

The algorithm converges to optimal sensing performance.

Simulation results match real radio environment performance.

The method guarantees sensing of maximum possible bands.

Abstract

In this paper, we consider non-contiguous wideband spectrum sensing (WSS) for spectrum characterization and allocation in next generation heterogeneous networks. The proposed WSS consists of sub-Nyquist sampling and digital reconstruction to sense multiple non-contiguous frequency bands. Since the throughput (i.e. the number of vacant bands) increases while the probability of successful reconstruction decreases with increase in the number of sensed bands, we develop an online learning algorithm to characterize and select frequency bands based on their spectrum statistics. We guarantee that the proposed algorithm allows sensing of maximum possible number of frequency bands and hence, it is referred to as throughput optimized WSS. We also provide a lower bound on the number of time slots required to characterize spectrum statistics. Simulation and experimental results in the real radio environment show that the performance of the proposed approach converges to that of Myopic approach which has prior knowledge of spectrum statistics.