Channel Capacity Enhancement with Nonlinear Distorted Signal Detection Using OFDM-NOMA Systems with Optimization System

The integration of Non-Orthogonal Multiple Access (NOMA) techniques with Orthogonal Frequency Division Multiplexing (OFDM) in 6G networks, aiming to reduce frequency selectivity in multipath channels. It highlights the challenges faced by OFDM signals in nonlinear High-Power Amplifiers (HPA) due to their high Peak-to-Average Power Ratio (PAPR). The study explores the potential of Massive Multiple-Input Multiple-Output (MIMO) technology, particularly in millimeter-wave (mmWave), to enhance energy and spectrum efficiency, productivity, and capacity in communication systems. The research proposes a Fuzzy Firefly Algorithm (FFA) trained with Radial Basis Function NeuralNetworks (RBFNN) for non-linear channel equalisation, and a Particle Filter (PF) based iterative approach for distortion compensation and interference cancellation in nonlinear data detection.