In this letter, we analyze the average symbol error rate (SER) performance for multiple-input multiple-output (MIMO) wireless communication links with transmit beamforming and maximum ratio combining (MRC), known as MIMO-MRC, in the presence of multiple interferers in Rayleigh fading channels. An upper bound and an approximation of the average SER for M-ary signaling and an exact average SER for some modulation formats are evaluated. Moreover, an exact closed-form expression of the average SER in an interference-limited environment is derived. The analytical results are confirmed by numerical simulations.

Blind adaptive channel identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the so-called, second order statistics techniques. Adaptive blind channel identification techniques based on a off-line least-squares approach have been proposed but this method assumes noise-free case. The method resorts to an adaptive filter with a linear constraint. This paper proposes a new approach based on eigenvalue decomposition. Indeed, the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the received signals contains the channel impulse response. And we present a adaptive algorithm to solve this problem. The performance of the proposed technique is evaluated over real measured channel and is compared to existing algorithms.

Space-time block coding is an attractive solution for improving quality in wireless links. In general, the multiple-input multiple-output (MIMO) channel is correlated by an amount that depends on the propagation environment as well as the polarization of the antenna elements and the spacing between them. In this paper, asymptotic performance and exact symbol error probability (SEP) of orthogonal space-time block code (STBC) are considered in spatially correlated Rayleigh fading MIMO channel. We derive the moment generating function (MGF) of effective signal-to-noise ration (SNR) after combining scheme at the receiver. Using the MGF of effective SNR, we calculate the probability density function (pdf) of the effective SNR and derive exact closed-form SEP expressions of PAM/PSK/QAM with M-ary signaling. We prove that the diversity order is given by the product of the rank of the transmit and receive correlation matrix. Moreover, we quantify the loss in coding gain due to the spatial correlation. Simulation results demonstrate that our analysis provides accuracy.

In this paper, we propose a blind adaptive channel identification and equalization algorithm with phase offset compensation for single-input multiple-output (SIMO) channel. It is based on the one-step forward multichannel linear prediction error method and can be implemented by an RLS algorithm. Phase offset problem is inherent part of any second-order statistics-based blind identification and equalization. To solve this problem, we use a blind adaptive algorithm called the constant modulus derotator (CMD) algorithm based on constant modulus algorithm (CMA). Moreover, unlike many known subspace (SS) methods or cross relation (CR) methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch.

In this letter, we analyze symbol error probability (SEP) and diversity gain of orthogonal space-time block codes (OSTBCs) in spatially correlated Rician fading channel. We derive the moment generating function (MGF) of an effective signal-to-noise ratio (SNR) at the receiver and use it to derive the SEP for M-PSK modulation. We use this result to show that the diversity gain is achieved by the product of the rank of the transmit and receive correlation matrix, and the loss in array gain is quantified as a function of the spatial correlation and the line of sight (LOS) component.

This paper proposes a new decision feedback decoding scheme for Alamouti-based space-time block coding (STBC) transmission over time-selective fading channels. In wireless channels, time-selective fading effects arise mainly due to Doppler shift and carrier frequency offset. Modelling the time-selective fading channels as the first-order Gauss-Markov processes, we use recursive algorithms such as Kalman filtering, LMS and RLS algorithms for channel tracking. The proposed scheme consists of the symbol decoding stage and channel tracking algorithms. Computer simulations confirm that the proposed scheme shows the better performance and robustness to time-selectivity.

In this paper, we consider a blind channel estimation and equalization for single input multiple output (SIMO) channels. It is based on the one-step forward multichannel linear prediction error method. The derivation of the existing method is based on the noiseless assumption, however, we analyze the effects of additive noise at the output of the one-step forward multichannel linear prediction error filters. Moreover, we derive analytical results for the error in the blind channel estimation and equalization using linear prediction.

In this paper, we investigate the performance of maximum ratio combining (MRC) in the presence of multiple cochannel interferences over a flat Rayleigh fading channel. Closed-form expressions of signal-to-interference-plus-noise ratio (SINR), outage probability, and average symbol error rate (SER) of quadrature amplitude modulation (QAM) with M-ary signaling are obtained for unequal-power interference-to-noise ratio (INR). We also provide an upper-bound for the average SER using moment generating function (MGF) of the SINR. Moreover, we quantify the array gain loss between pure MRC (MRC system in the absence of CCI) and MRC system in the presence of CCI. Finally, we verify our analytical results by numerical simulations.