Effective Capacity Analysis of Joint Near and Far-Field Communication in 6G URLLC Networks

TL;DR

This paper develops an effective capacity analysis framework for 6G networks that jointly considers near- and far-field communication regimes under uncertainty, providing insights into delay performance and QoS guarantees.

Contribution

It introduces a novel EC analysis model that accounts for distance estimation uncertainty in joint near- and far-field 6G communications, with closed-form expressions for delay performance.

Findings

Estimation variance significantly affects EC performance.

QoS exponent influences delay guarantees.

Boundaries like Fraunhofer distance impact capacity analysis.

Abstract

The emergence of 6G networks enables simultaneous near-field and far-field communications through extremely large antenna arrays and high carrier frequencies. While these regimes enhance spatial multiplexing and link capacity, their coexistence poses new challenges in ensuring quality-of-service (QoS) guarantees for delay-sensitive applications. This paper presents an effective capacity (EC) analysis framework that jointly models near- and far-field communication regimes under distance estimation uncertainty. The user location is modeled as a random variable spanning both propagation regions, and tractable closed-form expression for the EC is derived to quantify delay performance. Numerical results illustrate the impact of estimation variance, QoS exponent, far-field boundary and near-field boundary (Fraunhofer distance) on EC performance.