Katzis, Konstantinos

Aiming at the problem of downlink power allocation in cognitive high-altitude platform wireless networks exploiting TV white space spectrum, it is mathematically formulated as a constrained optimization problem, and then an improved immune clonal optimization algorithm is proposed. The mathematical optimization model, algorithm realization process, and key technologies for power allocation are given, and coding, cloning, and mutation suitable operator for algorithm solving are designed. The findings of the simulation experiment indicate that, under the constraints of total transmit power, bit error rate, and interference tolerable to the main user, the algorithm can obtain a greater total data throughput, faster convergence speed, and better power allocation can be obtained. Finally, the proposed algorithm outperforms the Particle Swarm Optimization algorithm.

The issue of spectrum allocation in cognitive high altitude platform wireless networks using TV White Space (TVWS) spectrum is presented in this paper. The mathematical model of spectrum allocation is given, and this model is converted into a constrained optimization problem with the goal of maximizing network benefits. Then, a spectrum allocation optimization approach is proposed that is based on improved immune clonal selection algorithm. According to the characteristics of the problem, the design, coding, cloning, mutation, hypermutation and selection operators suitable for the algorithm's solution are presented. Finally, a simulation experiment was carried out on this algorithm. The proposed algorithm was validated and proved that it converges better than Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Moreover, experiments demonstrate that the suggested approach maximizes network benefits or rewards more effectively when compared with GA and PSO algorithms and results proved the effectiveness of the algorithm.