Multibit-Quantization-Based Collaborative Spectrum Sensing Scheme for Cognitive Sensor Networks

Abstract

In order to overcome the bandwidth limitations of common control channels, we introduce a multibit quantization scheme to preprocess raw measurements in a collaborative spectrum sensing (CoSS) scheme. After quantifying the raw measurement, the cognitive sensors (CSs) send the multibit sensing information to a sink node (SN), which makes a final decision by using a soft decision fusion rule. In order to better understand the effects of quantization parameters on collaborative spectrum sensing in a cognitive sensor network (CSN), we discuss the following two problems: the average error probability minimum problem (AEPMP) and throughput maximum problem. By identifying the number of quantization bits, number of CSs, and global decision threshold, we can discuss some design considerations for the multibit-quantization-based CoSS scheme. A closed-form expression for the optimal global decision threshold is derived in the process of analyzing the AEPMP. Furthermore, an iterative algorithm with low complexity is proposed to find the optimal parameters for maximizing the throughput of a CSN under a target detection probability constraint. Finally, we investigate the impact of optimized parameters on the system performance of a CSN. Our theoretical analysis is verified through numerical simulations.

Keywords

• Multibit-quantization
• collaborative spectrum sensing
• soft decision fusion
• average error probability
• throughput