Sparsity and Resolvability: Re-evaluating Channel Representations For Next Generation Networks

TL;DR

This paper introduces a signal processing framework for understanding and adapting channel representations in next-generation wireless networks, especially under challenging conditions like high mobility and hardware impairments.

Contribution

It proposes an interchanged domain frame concept that adapts channel representations based on propagation regime and SNR, improving performance assessment and system design.

Findings

Different channel representations affect equalization and detection performance.

The framework links sparsity, resolvability, and selectivity through observable indicators.

Application to the Vehicular A channel shows implications for communication and sensing.

Abstract

As wireless networks transition toward 6G, high mobility, clustered scattering, and hardware impairments increasingly challenge classical assumptions on channel sparsity, resolvability, and stationarity. In these regimes, performance assessments based on apparent sparsity or nominal delay and Doppler separation can be misleading, since finite observation, sampling granularity, windowing, and fractional delay or Doppler spreading introduce coupling and leakage that reshape the effective channel seen by the receiver. This article provides a signal processing centric framework that links sparsity, resolvability, and selectivity through receiver observable indicators, including the fraction of power captured by dominant coefficients, the level of coefficient correlation, the effective delay and Doppler resolving capability over the observation window, and processing induced leakage. Building on these observations, we propose an interchanged domain frame concept principle, where the representation and the degree of component separation are adapted according to the propagation regime, the effective SNR under impairments, and the application objective. Using the Extended Vehicular A channel profile as a running case study, we show how different representations lead to different equalization and detection behavior, with implications for communication, sensing, and physical layer security.