Advancements in Equalization Techniques for Improving Data Throughput and Reliability in Next-Generation Communication Systems

Abstract

Digital communication links operating under aggressive spectral reuse, high mobility, and power-constrained architectures rely on equalization to mitigate intersymbol and intercarrier interference introduced by band-limited channels, radio-frequency front-end imperfections, and multiuser coupling. As carrier frequencies extend toward millimeter-wave and sub-terahertz bands, and as baseband sampling becomes coarsely quantized for power efficiency, equalization strategies must adapt to rapidly time-varying, frequency-selective, and hardware-impaired regimes. This paper develops a broad technical treatment of equalization advances for next-generation systems by analyzing linear, nonlinear, and learning-augmented detectors across single-carrier, multicarrier, and delay–Doppler waveforms. A unified mathematical perspective is used to connect estimation-theoretic derivations, message-passing viewpoints, and optimization-based formulations, highlighting performance–complexity–energy trade-offs under realistic constraints such as low-resolution conversion, hybrid beamforming, and oscillator phase noise. Algorithmic robustness is examined under channel uncertainty, non-Gaussian disturbances, and structured interference arising in massive multiple-antenna and cell-free architectures. Emphasis is placed on stable numerical formulations, scalable preconditioning, and hardware-friendly updates that map efficiently to fixed-point pipelines, systolic arrays, and modern accelerators. The discussion integrates channel-shortening and sequence-detection ideas with iterative decoding, shows how state-evolution tools predict operating points under large-system limits, and outlines regimes where model-driven deep unrolling can improve convergence without sacrificing interpretability. The paper articulates modeling assumptions, identifies key operating regions for different equalizer classes, and provides implementation notes that clarify latency, memory footprint, and data movement bottlenecks. The overall goal is to present technically grounded guidance that helps map waveform, coding, and front-end design choices to equalization architectures capable of sustaining reliable and spectrally efficient links under stringent power and mobility conditions.