Cooperative Sequential Spectrum Sensing Algorithms for OFDM

TL;DR

This paper introduces cooperative sequential spectrum sensing algorithms for OFDM-based cognitive radio networks, demonstrating improved detection performance and robustness against model uncertainties through simulation studies.

Contribution

It develops and compares energy and CP-based sequential detection algorithms tailored for OFDM systems, including modifications to handle various impairments.

Findings

Sequential detection outperforms fixed sample size detectors.

Energy detector performs better than CP-based detector in most scenarios.

Energy detector is robust against model uncertainties such as timing, frequency offset, and noise power.

Abstract

This paper considers the problem of spectrum sensing in cognitive radio networks when the primary user employs Orthogonal Frequency Division Multiplexing (OFDM). We develop cooperative sequential detection algorithms based on energy detectors and the autocorrelation property of cyclic prefix (CP) used in OFDM systems and compare their performances. We show that sequential detection provides much better performance than the traditional fixed sample size (snapshot) based detectors. We also study the effect of model uncertainties such as timing and frequency offset, IQ-imbalance and uncertainty in noise and transmit power on the performance of the detectors. We modify the detectors to mitigate the effects of these impairments. The performance of the proposed algorithms are studied via simulations. It is shown that energy detector performs significantly better than the CP-based detector, except in case of a snapshot detector with noise power uncertainty. Also, unlike for the CP-based detector, most of the above mentioned impairments have no effect on the energy detector.