Analytic Performance Evaluation of Underlay Relay Cognitive Networks with Channel Estimation Errors

TL;DR

This paper provides an analytical BER expression for underlay relay cognitive networks with decode-and-forward relays, accounting for channel estimation errors, and demonstrates how these errors degrade network performance.

Contribution

It introduces a novel exact closed-form BER expression for multi-hop underlay relay cognitive networks with channel estimation errors, simplifying performance analysis.

Findings

BER performance is significantly degraded by channel estimation errors.

Network topology and number of hops critically affect performance.

The analytical expression aligns well with Monte-Carlo simulation results.

Abstract

This paper evaluates the bit error rate (BER) performance of underlay relay cognitive networks with decode-and-forward (DF) relays in arbitrary number of hops over Rayleigh fading with channel estimation errors. In order to facilitate the performance evaluation analytically we derive a novel exact closed-form representation for the corresponding BER which is validated through extensive comparisons with results from Monte-Carlo simulations. The proposed expression involved well known elementary and special functions which render its computational realization rather simple and straightforward. As a result, the need for laborious, energy exhaustive and time-consuming computer simulations can be ultimately omitted. Numerous results illustrate that the performance of underlay relay cognitive networks is, as expected, significantly degraded by channel estimation errors and that is highly dependent upon of both the network topology and the number of hops.