Login
Submit Now
No file available [This article belongs to Volume - 58, Issue - 1]
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-05-05-2026-910
Title : AI-Driven Hybrid Precoding–Companding for Multi-Objective PAPR, Spectral, and Reliability Optimization in 6G UFMC/OTFS/NOMA
Ponmani Raja M, , Sujatha S, , Prajoon P,

Abstract : Future 6G physical layers need to work together to improve peak power, spectrum confinement, reliability, energy, and latency when channels are not stationary and service slices are not the same. Traditional approaches for reducing PAPR and shaping spectra, such as PTS/SLM, ?-law companding, and fixed orthogonal precoding, work to some extent but are not very reliable across different types of waveforms (UFMC, OTFS) and power-domain multiplexing (NOMA). To suggest a single, AI-driven signal-shap

Keywords : NOMA, Driven Hybrid Precoding, Reliability Optimization, driven, hybrid