DVB-T2 Energy and Spectral Efficiency Trade-off Optimization based on Genetic Algorithm

The performance of a DVBT2 system is affected by a set of parameters, and choosing these parameters is very difficult for anyone, even an expert. In this paper, a Genetic algorithm is used to find the optimal set of parameters: constellation, No. Of forward error correction FEC blocks, code rate, samples per frame, and signal-to-noise ratio SNR for different fading channels. The number of Generations and individuals is 100. The goal of this work is to enhance the system performance by achieving a trade between BER and throughput with the lowest possible SNR and iterations. An online supercomputer platform was used to run the MATLAB simulation of the suggested system, which was conducted using MATLAB R2021a. The results show a clear improvement of the system performance and a BER of less than 10-5 is obtained. The influence of population size on the GA performance is also investigated, population sizes between 50 and 200 are tested. The results show that reaching the optimal solution with the least possible number of iterations is achieved when the population size is 200 while poor results are obtained when the population size is 50. Thus, we have a system capable of finding the optimal set of parameters that are most affected by the genetic algorithm GA and the population size of 200 with the least possible number of iterations.