Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
A comparison of pilot-aided channel estimation methods for OFDMsystems
IEEE Transactions on Signal Processing
Pilot-assisted estimation of MIMO fading channel response and achievable data rates
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
The capacity of discrete-time memoryless Rayleigh-fading channels
IEEE Transactions on Information Theory
Fading channels: how perfect need "perfect side information" be?
IEEE Transactions on Information Theory
How much training is needed in multiple-antenna wireless links?
IEEE Transactions on Information Theory
Gaussian codes and weighted nearest neighbor decoding in fading multiple-antenna channels
IEEE Transactions on Information Theory
Optimized diversity combining with imperfect channel estimation
IEEE Transactions on Information Theory
Capacity and power allocation for fading MIMO channels with channel estimation error
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Optimal decoder for channels with estimation errors
IEEE Transactions on Wireless Communications
Spectrum sensing with active cognitive systems
IEEE Transactions on Wireless Communications
Hi-index | 0.00 |
Analysis of mutual information is important to wireless transmission over fading channels, and is usually done by assuming perfect channel state information (CSI) available at the receiver. In many practical applications, however, the CSI must be estimated by sending a limited number of pilot symbols and thus, can suffer from considerable inaccuracy. The influence of CSI inaccuracy on achievable mutual information, though analyzed in the past, is not well understood. The difficulty arises from the presence of a product term of signal and channel estimation error in the received signal model, and the lack of appropriate tools to determine its probability density function. This situation forces the adoption of a bounding technique in current literature by treating the product term either as a signal component or as a noise component. In this paper, we take a different methodology by accurately fitting the received signal with the multivariate Pearson's type-7 (MPT-7) distribution. The new results are simple in expression, very accurate as evidenced by simulation, and capable of directly revealing the dependence of the achievable mutual information on a particular channel estimator.