In cognitive wireless network, throughput scheduling optimization under interference temperature constraints has attracted more attentions in recent years. A lot of works have been investigated on it with different scenarios. However, these solutions have either high computational complexity or relatively poor performance. Throughput scheduling is a constraint optimization problem with NP(Non-deterministic Polynomial) hard features. In this paper, we proposed an immune-clone based suboptimal algorithm to solve the problem. Suitable immune clone operators are designed such as encoding, clone, mutation and selection. The simulation results show that our proposed algorithm obtains near-optimal performance and operates with much lower computational complexity. It is suitable for slowly varying spectral environments. 
Throughput optimization in cognitive wireless network based on clone selection algorithm