Algorithms for Joint Spectrum Allocation and Cooperation Set Partition in Cognitive Radio Networks

Abstract

The coexistence of multi-primary users and multi-secondary users in cooperative cognitive radio networks motivate the study to propose a joint spectrum allocation and cooperation set partition problem, which so far has not been addressed before. The problem is formulated as a 0-1 integer non-linear programming model. Due to its NP-hardness, the study proposes a suboptimal Centralized Genetic Algorithm (CGA) to show its convergence by modeling it as a homogeneous finite Markov chain. The study then extends CGA to a fully Distributed Genetic Algorithm (DGA) that consists of two phases. The core techniques include a minimum dominate set based cluster partition, a spectrum pre-allocation algorithm in phase 1, and an inter-cluster cooperation set negotiation and cluster fitness refinement algorithm in phase 2. A Fast-Convergent DGA (FDGA) is also devised to reduce the system configuration time. Extensive experiments by simulations demonstrate that in terms of the fitness that reflects the performance of the proposed algorithms: (1) CGA is shown to perform as well as 92% of the optimal solution by brutal search under small network sizes; (2) As the network size increases, due to the massive search space CGA has to deal, DGA and FDGA instead outperform CGA with 20% on average when achieving the same algorithm termination condition; (3) FDGA delivers similar results as DGA while reducing the configuration time significantly, which is more suitable for large-scale networks.