Improved Weighted Average Consensus in Distributed Cooperative Spectrum Sensing Networks

TL;DR

This paper introduces an improved distributed consensus algorithm for cooperative spectrum sensing in cognitive radio networks, achieving high performance with low complexity and fast convergence without centralized fusion.

Contribution

It proposes a novel IWAC algorithm that combines channel condition-based weights for efficient, fully distributed spectrum sensing with comparable performance to centralized methods.

Findings

IWAC achieves similar detection performance to centralized CSS.

IWAC converges faster than traditional AC methods.

All proposed algorithms have similar computational complexity.

Abstract

This work proposes a fully distributed improved weighted average consensus (IWAC and WAC-AE) technique applied to cooperative spectrum sensing problem in cognitive radio systems. This method allows the secondary users cooperate based on only local information exchange without a fusion centre (FC). We have compared four rules of average consensus (AC) algorithms. The first rule is the simple AC without weights. The AC rule presents {performance comparable to the traditional cooperative spectrum sensing} (CSS) techniques, such as the equal gain combining (EGC) rule, which is a soft combining centralised method. Another technique is the weighted average consensus (WAC) rule using the weights based on the SUs channel condition. This technique results in a performance similar to the maximum ratio combining (MRC) with soft combining (centralised CSS). Two new AC rules are analysed, namely weighted average consensus accuracy exchange (WAC-AE), and improved weighted average consensus (IWAC); the former relates the weights to the channel conditions of the SUs neighbours, while the latter combines the conditions of WAC and WAC-AE in the same rule. All methods are compared each other and with the hard combining centralised CSS. The WAC-AE results in a similar performance of WAC technique but with fast convergence, while the IWAC can deliver suitable performance with small complexity increment{. Moreover, IWAC method results in a similar convergence rate than the WAC-AE method but slightly higher than the AC and WAC methods}. Hence, the computational complexity of IWAC, WAC-AE, and WAC are proven to be very similar. The analyses are based on the numerical Monte-Carlo simulations (MCS), while algorithm's convergence is evaluated for both fixed and dynamic-mobile communication scenarios, and under AWGN and Rayleigh channels.