Spectrum Sensing in Wideband OFDM Cognitive Radios

TL;DR

This paper proposes three Neyman-Pearson based algorithms for detecting primary user signals in wideband OFDM cognitive radio systems, addressing different levels of prior knowledge about the PU signal.

Contribution

It introduces novel detection algorithms tailored for wideband OFDM cognitive radios, considering various prior knowledge scenarios of the PU signal.

Findings

Algorithms effectively detect PU signals with different prior knowledge levels.

Detection performance improves by joint analysis of interfered sub-carriers.

The detector can identify abrupt power changes along the frequency spectrum.

Abstract

In this paper, detection of the primary user (PU) signal in an orthogonal frequency division multiplexing (OFDM) based cognitive radio (CR) system is addressed. According to the prior knowledge of the PU signal known to the detector, three detection algorithms based on the Neyman-Pearson philosophy are proposed. In the first case, a Gaussian PU signal with completely known probability density function (PDF) except for its received power is considered. The frequency band that the PU signal resides is also assumed known. Detection is performed individually at each OFDM sub-carrier possibly interfered by the PU signal, and the results are then combined to form a final decision. In the second case, the sub-carriers that the PU signal resides are known. Observations from all possibly interfered sub-carriers are considered jointly to exploit the fact that the presence of a PU signal interferers all of them simultaneously. In the last case, it is assumed no PU signal prior knowledge is available. The detection is involved with a search of the interfered band. The proposed detector is able to detect an abrupt power change when tracing along the frequency axis.