Improved weighted average consensus in distributed cooperative spectrum sensing networks

Abstract

This work proposes a fully distributed improved weighted average consensus (IWAC) technique applied to a cooperative spectrum sensing (CSS) problem in cognitive radio systems. This method allows the secondary users to cooperate based on only local information exchange without a fusion centre. We have compared 4 rules of average consensus (AC) algorithms. The first rule is the simple AC without weights. The AC rule presents performance comparable to the traditional CSS techniques such as the equal gain combining rule, which is a soft combining centralised method. Another technique is the weighted AC (WAC) rule, using the weights based on the SUs' channel condition. This technique results in a performance similar to that of the maximum ratio combining with soft combining (centralised CSS). Two new AC rules are analysed, namely, WAC accuracy exchange (WAC‐AE) and IWAC; the former relates the weights to the channel conditions of the SUs' neighbours, whereas the latter combines the conditions of WAC and WAC‐AE in the same rule. All methods are compared with each other and with the hard combining centralised CSS. The WAC‐AE results in a similar performance of the WAC technique but with fast convergence, whereas the IWAC can deliver suitable performance with small complexity increment. Moreover, the 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 is proven to be very similar. The analyses are based on numerical Monte Carlo simulations, whereas the algorithm's convergence is evaluated for both fixed and dynamic mobile communication scenarios and under additive white Gaussian noise and Rayleigh channels.

Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols

“…However, in the situation when the nodes have access to global information related to the network, such as the size of the network (number of nodes n = |V|) and the sum of the weights n i=1 w i , then any algorithm that solves the standard average consensus can be used to solve the weighted average consensus problem with the initial private values of the nodes changed from c i to nw i c i n i=1 w i . The weighted AC problem is popular in the area of distributed cooperative spectrum sensing networks [33,66,91,92]. In this setting, one of the goals is to develop decentralized protocols for solving the cooperative sensing problem in cognitive radio systems.…”

“…In this setting, one of the goals is to develop decentralized protocols for solving the cooperative sensing problem in cognitive radio systems. The weights in this case represent a ratio related to the channel conditions of each node/agent [33]. The development of methods for solving the weighted AC problem is an active area of research (check [33] for a recent comparison of existing algorithms).…”

“…The weights in this case represent a ratio related to the channel conditions of each node/agent [33]. The development of methods for solving the weighted AC problem is an active area of research (check [33] for a recent comparison of existing algorithms). However, to the best of our knowledge, existing analysis for the proposed algorithms focuses on showing convergence and not on providing convergence rates.…”

Small-gain theorem for stability, cooperative control and distributed observation of infinite networks

“…As a particular application, in distributed cooperative spectrum sensing, the main objective is to develop distributed protocols for solving the cooperative sensing problem in cognitive radio systems, e.g. see [31], [32] and references therein. The weights in this case represent a ratio related to the channel conditions of each agent.…”

Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols

“…In the rest of this section we focus on two special cases of (36): RK with heavy ball momentum (equation (33) with b i = 0) and RBK with heavy ball momentum (equation ( 34) with b C = 0). Algorithm 3 mRK: Randomized Kaczmarz with momentum as a gossip algorithm 1: Parameters: Distribution D from which method samples matrices; stepsize/relaxation parameter ω ∈ R; heavy ball/momentum parameter β.…”