Effective Capacity of a Rayleigh Fading Channel in the Presence of Interference

TL;DR

This paper develops an analytical model for the effective capacity of Rayleigh fading channels with interference, aiding in statistical QoS provisioning in interference-limited wireless networks.

Contribution

It introduces a Laplace's method-based analytical approach for effective capacity in interference-limited Rayleigh fading channels, validated by numerical simulations.

Findings

Analytical model closely matches simulation results.

Effective capacity decreases with increased interference.

Tail probability analysis provides insights into delay and rate limits.

Abstract

In recent years the concept of the effective capacity that relates the physical layer characteristics of a wireless channel to the data link layer has gained a lot of attraction in wireless networking research community. The effective capacity is based on G\"artner-Ellis' large deviation theorem and it is used to provide the statistical QoS provisioning in the wireless networks. Effective capacity also helps in the analysis of the resource allocation or scheduling policies in various wireless systems such as Relay networks, multi-user systems and multi-carrier systems subject to statistical QoS requirements. The effective capacity in noise limited wireless network has already been investigated in the recent works. Considering the interference limited wireless channels, in this paper we propose an analytical approach based on Laplace's method for the effective capacity of uncorrelated Rayleigh fading channel in the presence of uncorrelated Rayleigh fading interference. The accuracy of the analytical model for the effective capacity is validated by numerical simulations. We also provide the evaluation of tail probability of the delay and maximum sustainable rate. The validation results reveal that the proposed mathematical approach to the effective capacity can open the path for further researches in statistical QoS provisioning in interference limited wireless networks.