Applications of Hybrid Intelligent Systems in Adaptive Communication

Abstract

Dynamic allocation of the resources for optimum utilization and throughput maximization is one of the most important fields of research nowadays. In this process the available resources are allocated in such a way that they are maximally utilized to enhance the overall system throughput. In this chapter a similar problem is approached which is found in Orthogonal Frequency Division Multiplexing (OFDM) environment, in which the transmission parameters namely the code rate, modulation scheme and power are adapted in such a way that overall system's data rate is maximized with a constrained bit error rate and transmit power. A Fuzzy Rule Base System (FRBS) is proposed for adapting the code rate and modulation scheme while Genetic Algorithm (GA) and Differential Evolution (DE) algorithm are used for adaptive power allocation. The proposed scheme is compared with other schemes in the literature including the famous Water-filling technique which is considered as a benchmark in the adaptive loading paradigm. Supremacy of the proposed technique is shown through computer simulations.

Enhancing Multi-User Detection in Multicarrier 5G and Beyond: A Space-Time Spreading Approach with Parallel Interference Cancellation

“…Therefore, this study investigates the Parallel Interference Canceller (PIC), which promises to mitigate noise enhancement and complexity in 5G networks. Owing to its parallel nature, it proves equally effective in Orthogonal Multiple Access systems (OMA) and NOMA [17][18][19][20]. This proposed scheme serves as a receiver optimization approach when the number of users is excessive.…”

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

“…On the contrary, typical mathematical programming methods replicate natural phenomena using established rules and randomization methods to avoid gradient difficulties in optimization. Many studies on metaheuristic approaches have been done over the last two decades [4,5]. Additionally, multiple metaheuristic algorithms have been developed to address continuous problems, including the Genetic Algorithm [6,7], Differential Evolution [8][9][10], and Artificial Bee Colony [11].…”