In multi-user multiple-input single-output orthogonal frequency-division multiplexing (MISO-OFDM) downlinks with limited feedback, both linear precoders (LP) and Tomlinson-Harashima precoders (THP) experience performance degradation due to inaccurate channel state information at the transmitter (CSIT). This analysis treats the downlink channels as random quantities and exploits their second order statistics in robust precoding schemes to correct the errors introduced in the feedback procedure. The time-domain channel vectors are found to reduce the feedback overhead more than the frequency-domain vectors. A compression and restoration method and a codebook design are also given to obtain compact feedback quantities. Simulations show that the robust LP and THP are superior to the previous methods with tradeoffs possible between performance and feedback overhead.

As a generalization of orthogonal frequency-division multiplexing (OFDM) systems, multi-input multi-output (MIMO) OFDM systems are very sensitive to carrier frequency offset (CFO). This paper proposes a blind CFO estimation method based on power spectrum analysis, which has high bandwidth efficiency and is much less complex. This method can be used to estimate the residual CFO, which is less than half of the subcarrier spacing. The method uses a cosine cost function to get a closed-form CFO estimate. Simulation results illustrate that the method is effective for MIMO OFDM systems.

The problem of approximate joint diagonalization of a set of matrices is instrumental in numerous statistical signal processing applications. This paper describes a relative gradient non-orthogonal approximate joint diagonalization (AJD) algorithm based on a non-least squares AJD criterion and a special AJD using a non-square diagonalizing matrix and an AJD method for ill-conditioned matrices. Simulation results demonstrate the better performance of the relative gradient AJD algorithm compared with the conventional least squares (LS) criteria based gradient-type AJD algorithms. The algorithm is attractive for practical applications since it is simple and efficient.

This paper describes a channel estimation and equalization algorithm using three-layer artificial neural networks (ANNs) with feedback for multiple input multiple output wireless communication systems. An ANN structure with feedback was designed to use different learning algorithms in the different ANN layers. This actually forms a Turbo iteration process between the different algorithms which effectively improves the estimation performance of the channel equalizer. Simulation results show that this channel equalization algorithm has better computational efficiency and faster convergence than higher order statistics based algorithms.

The paper analyzes the problem of blind source separation (BSS) based on the nonlinear principal component analysis (NPCA) criterion. An adaptive strong tracking filter (STF) based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive least-squares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.

A multiuser detector was developed for fast fading code-division multiple-access systems by representing the channels as a system with the multiplicative noise (SMN) model and then using the known optimal filtering algorithm for the SMN for multiuser detection (MUD). This multiuser detector allows the channel response to be stochastic in one symbol duration, which can be regarded as an effective method of MUD for fast fading CDMA systems. Performance analyses show that the multiuser detector is theoretically valid for CDMA systems over fast fading channels. Simulations show that the multiuser detector performs better than the Kalman filter-based multiuser detector with a faster convergence rate and lower bit error rate.

Orthogonal frequency-division multiplexing (OFDM) systems are sensitive to carrier frequency offset (CFO) which introduces intercarrier interference and significantly degrades system performance. This paper describes an iterative blind receiver consisting of a sequential Monte Carlo detector, a CFO estimator, and a compensator to reduce intercarrier interference. The framework is of low complexity due to the separation of tasks in a joint detection problem. In addition, the CFO estimator utilizes soft output of the sequential Monte Carlo detector, which reduces the information loss caused by hard decisions and can obtain the CFO estimate in only one OFDM symbol. Simulation results demonstrate the effectiveness of the algorithm.