Adaptive communication using softcomputing techniques

Abstract

Adaptive communication has gained attention of almost every recent communication system because of its rate enhancement and context aware features. In this concept, different transmission parameters like transmit power, forward error correcting (FEC) code rate and modulation scheme are adaptively chosen according to the channel state information. Consequently, that set of transmission parameters is chosen that maximizes the channel capacity as well as fulfills the power and bit error rate constraints. Finding the optimum value of the said parameters is a highly non-linear problem with huge search space for solution. In this paper, we have investigated Ant Colony Optimization (ACO) in conjunction with a fuzzy rule base system (SA-FRBS) for adaptive coding, modulation and power in an orthogonal frequency division multiplexing environment. Proposed scheme is compared with Simulated Annealing and FRBS (SA-FRBS) assisted adaptive coding modulation and power scheme as well as with the fixed power scheme. Superiority of proposed scheme is shown by the simulations.

An Overview of Self-Adaptive Differential Evolution Algorithms with Mutation Strategy

“…As a result, they are more suited for handling unimodal situations. When solving multimodal problems, (DE/rand/1 and DE/rand/2) have a better global search capability [20]. Some DE variants focus on incorporating different mutation methods [21], such as a selfadaptive mutation DE [22], a composite DE [23], an ensemble of mutation strategies and control parameters of DE [24], and a self-adaptive DE with discrete mutation control parameters.…”