Abstract
The minimum output energy (MOE) receiver has been developed for multiuser detection when multipath distortion is present. Its performance has been shown to be very close to the minimum mean-square-error (MMSE) receiver at high signal to noise ratio. However, due to the additive noise, the constraint vector required to construct the MOE receiver is a biased estimate of the channel vector. Thus, the MOE receiver exhibits degraded performance. To mitigate the noise effect, the constraint cost function is modified to obtain a modified MOE (MMOE) receiver in this paper, leading to a significantly improved channel estimate and detection performance. It is also revealed that the MMOE method converges to the well-known subspace method under certain conditions. In addition to the additive noise, imperfect estimation of the output data covariance matrix also causes performance loss and is studied in details based on perturbation theory.
Highlights
The rapidly growing demands for integrated wideband/ broadband services have created considerable research interest in developing new wireless communication technologies [1, 2]
The output signal-to-interference-plus-noise ratio (SINR) of the modified MOE (MMOE) receiver and the minimum output energy (MOE) receiver are compared with the minimum mean square error (MMSE) receiver and the SINR ratios plotted in Effect of noise on channel estimation errors
Due to the additive noise, the MOE receiver suffers from performance degradation compared with the blind MMSE receiver
Summary
The rapidly growing demands for integrated wideband/ broadband services have created considerable research interest in developing new wireless communication technologies [1, 2]. Constrained minimum output energy (MOE) approaches [14, 15] and subsequently developed robust methods [16, 17] and provide direct detection techniques in the presence of unknown multipath distortions These blind MOE methods minimize the output energy (or power) of the receiver subject to certain constraints to guarantee no cancellation of the desired signal. Imperfect estimation of the output data covariance matrix based on a finite number of data samples degrades the detection performance. Since the MOE receiver employs the second order statistics of the channel output which are estimated from the received data, the accuracy of covariance estimation depends on the available data record Due to this estimation error, the optimal constraint vector is perturbed to be dependent on N.
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