Sparse Reconstruction-based Detection of Spatial Dimension Holes in Cognitive Radio Networks

TL;DR

This paper proposes a compressive sensing-based spectrum sensing algorithm for MIMO-OFDM systems that effectively detects spatial dimension holes, outperforming traditional methods in identifying transmission opportunities.

Contribution

It introduces a CS-based detection method for spatial holes in MIMO-OFDM systems and demonstrates improved performance when combined with MMSE decoders.

Findings

CS detector outperforms traditional energy detectors in detecting user activity.

CS-aided MMSE decoders provide better decoding performance than separate CS or MMSE decoders.

Simulation results confirm the effectiveness of the proposed approach.

Abstract

In this paper, we investigate a spectrum sensing algorithm for detecting spatial dimension holes in Multiple Inputs Multiple Outputs (MIMO) transmissions for OFDM systems using Compressive Sensing (CS) tools. This extends the energy detector to allow for detecting transmission opportunities even if the band is already energy filled. We show that the task described above is not performed efficiently by regular MIMO decoders (such as MMSE decoder) due to possible sparsity in the transmit signal. Since CS reconstruction tools take into account the sparsity order of the signal, they are more efficient in detecting the activity of the users. Building on successful activity detection by the CS detector, we show that the use of a CS-aided MMSE decoders yields better performance rather than using either CS-based or MMSE decoders separately. Simulations are conducted to verify the gains from using CS detector for Primary user activity detection and the performance gain in using CS-aided MMSE decoders for decoding the PU information for future relaying.