Ergodic Capacity Analysis in Cognitive Radio Systems under Channel Uncertainty

TL;DR

This paper analyzes the ergodic capacity of cognitive radio systems over flat fading channels with channel estimation using Wiener filters, considering sensing accuracy and power constraints, and compares causal and noncausal filter performance.

Contribution

It provides a comprehensive analysis of capacity under channel uncertainty with both causal and noncausal Wiener filters, including optimal detection and power strategies.

Findings

Achievable rates decrease as SNR increases for both filters.

Noncausal filters outperform causal filters at lower SNRs.

Optimal detection probabilities balance false alarms and missed detections.

Abstract

In this paper, pilot-symbol-assisted transmission in cognitive radio systems over time selective flat fading channels is studied. It is assumed that causal and noncausal Wiener filter estimators are used at the secondary receiver with the aid of training symbols to obtain the channel side information (CSI) under an interference power constraint. Cognitive radio model is described together with detection and false alarm probabilities determined by using a Neyman-Person detector for channel sensing. Subsequently, for both filters, the variances of estimate errors are calculated from the Doppler power spectrum of the channel, and achievable rate expressions are provided considering the scenarios which are results of channel sensing. Numerical results are obtained in Gauss-Markov modeled channels, and achievable rates obtained by using causal and noncausal filters are compared and it is shown that the difference is decreasing with increasing signal-to-noise ratio (SNR). Moreover, the optimal probability of detection and false alarm values are shown, and the tradeoff between these two parameters is discussed. Finally, optimal power distributions are provided.