Reducing latency in optical 6G waveforms using joint detection and channel estimation techniques

Abstract

The pursuit of ultra-low latency is a pivotal goal in advancing the capabilities of optical non-orthogonal multiple access sixth generation (O-NOMA-6G) waveforms. This study explores the application of maximum likelihood (ML) and expectation-maximization (EM) techniques to mitigate latency in optical communication systems. ML offers direct parameter estimation for rapid symbol detection, while EM addresses latency through iterative estimation of hidden variables and parameters. By leveraging the benefits of both techniques, this research proposes novel latency reduction approaches in optical 6G. The investigation encompasses theoretical analysis, simulation, and performance evaluation under Rician and Rayleigh channel conditions for different parameters such as bit error rate (BER), power spectral density (PSD) and peak to average power density (PSD). Simulation results demonstrate that ML and EM effectively reduce latency, and enable seamless integration of time-sensitive applications in optical 6G networks as compared with the conventional ML and ML methods. The outcomes of this study provide valuable insights into throughput and PSD enhancement contributing to the realization of ultra-responsive and O-NOMA 6G waveform.