Performance Analysis of Cognitive Radio Systems With Imperfect Channel Sensing and Estimation


Performance Analysis of Cognitive Radio Systems With Imperfect Channel Sensing and Estimation


In cognitive radio systems, using sensing-based mostly spectrum access strategies, secondary users are required to perform channel sensing to detect the activities of licensed primary users during a channel, and in realistic scenarios, channel sensing occurs with attainable errors thanks to miss-detections and false alarms. As another challenge, time-varying fading conditions in the channel between the secondary transmitter and also the secondary receiver need to be learned via channel estimation. During this paper, performance of causal channel estimation methods in correlated cognitive radio channels below imperfect channel sensing results is analyzed, and achievable rates for reliable communication under each channel and sensing uncertainty are investigated by considering the input-output mutual data. Initially, cognitive radio channel model with channel sensing error and channel estimation is described. Then, using pilot symbols, minimum mean square error (MMSE) and linear-MMSE (L-MMSE) estimation strategies are utilized at the secondary receiver to find out the channel fading coefficients. Expressions for the channel estimates and mean-squared errors (MSE) are determined, and their dependencies on channel sensing results, and pilot symbol amount and energy are investigated. Since sensing uncertainty results in uncertainty within the variance of the additive disturbance, channel estimation strategies and performance are curiously shown to rely on the sensing reliability. It’s further shown that the L-MMSE estimation method, which is in general suboptimal, performs terribly shut to MMSE estimation. Furthermore, assuming the channel estimation errors and therefore the interference introduced by the primary users as zero-mean and Gaussian distributed, achievable rate expressions of linear modulation schemes and Gaussian signaling are determined. Subsequently, the training period, and data and pilot symbol energy allocations are jointly optimized to maximize the achievable rates for each signalin- schemes.

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