Enhanced spectrum sensing in MIMO-OFDM cognitive radio networks using multi-user detection and square-law combining techniques

Abstract

Spectrum sensing (SS) is essential for cognitive radio (CR) networks to enable secondary users to opportunistically access unused spectrum without interfering with primary users. This article proposes a novel multi-user detection (MUD) and square-law combining (SLC) framework for SS in multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) CR networks. Traditional SS methods, especially energy detection (ED), often underperform in low signal-to-noise ratio (SNR) conditions, resulting in high false alarm rates due to noise uncertainty and multi-user interference. The multi-user detection-square-law combining (MUD-SLC) framework addresses these limitations by using MUD to separate user signals and SLC to combine energy from multiple antennas, significantly improving probability of detection (PD) while maintaining a low false alarm probability (Pfa). Simulation results show that the proposed approach achieves a PD of 0.81 at Pfa=0.15 and SNR=15 dB, outperforming conventional and advanced SS methods. Moreover, MUD-SLC demonstrates a considerable boost in detection performance, even in the presence of severe interference and noise uncertainty, leading to more reliable spectrum utilization in systems. The framework also maintains a lower Pfa, especially in dynamic wireless environments. This research work contributes to improving the efficiency and reliability of SS in CR networks.