An estimation-based approach to multiple-input multiple-output (MIMO) channel equalization

The purpose of this paper is to propose and investigate a new approach to implementing a spatio-temporal decision feedback equalizer (DFE) for MIMO (multiple-input multiple-output) channels. A system with an array of n transmit and m receiver antennas where (m ≥ n) is assumed. Both finite-length (finite horizon) and infinite-length (infinite horizon) MIMO decision feedback equalizers are considered. We also assume an ISI (inter-symbol-interference) MIMO channel, which means the channel matrix elements are frequency selective. For the infinite-length case the DFE problem leads to solving a matrix spectral factorization. For the finite-length case the DFE problem leads to solving a corresponding Cholesky factorization. Using the estimation-based spectral factorization we have shown that the solution to the infinite-length MIMO DFE is not unique. In the finite-length case the estimation-based approach leads to a recursive algorithm to perform the Cholesky factorization. The proposed recursive algorithm has low complexity and is also simple to implement. Moreover it leads to a closed form solution for the MIMO DFE matrices

Adaptive equalization of multiple-input multiple-output (MIMO) frequency selective channels

The purpose of this paper is to propose and investigate a new approach to adaptive spatio-temporal equalization for MIMO (multiple-input multiple-output) channels. A system with n transmit and m (n≥m) receiver antennas is assumed. An adaptive MIMO linear equalizer has been considered. For the considered equalizer a least squares solution is formulated, based on which a recursive solution using Riccati recursions is proposed. The solutions are tested by simulating the MIMO system. It is shown that the adaptive solutions will achieve the same performance as the optimum least squares solutions. The effect of the nondiagonal channel elements (acting as interference) on the system performance is also studied. It has been shown that in order to achieve better performance, the interference from nondiagonal channel elements needs to be minimized. This can be done by using orthogonal transmission. Moreover the proposed solutions do not require channel identification and will also enable equalizer adaptation to channel changes

Adaptive equalization of multiple-input multiple-output (MIMO) channels

The paper proposes and investigates a new approach to adaptive spatio-temporal equalization for MIMO (multiple-input multiple-output) channels. A system with n transmit and m (m≥n) receiver antennas is assumed. A decision Feedback equalizer is considered. A least squares solution is first formulated, based on which a recursive solution using Riccati recursions is proposed. The proposed solution is tested by simulating the MIMO system. It is shown that the adaptive solution achieves the same performance as the optimum least squares solution. The effect of the nondiagonal channel elements (acting as interference) on the system performance is also studied. It has been shown that in order to achieve better performance, the interference from nondiagonal channel elements needs to be minimized. This can be done by using orthogonal transmission. Moreover the proposed solution do not require channel identification and will also enable equalizer adaptation to channel changes

Estimation-based synthesis of H_∞-optimal adaptive equalizers over wireless channels

This paper presents a systematic synthesis procedure for H_∞-optimal adaptive FIR equalizers over a time-varying wireless channel. The channel is assumed to be frequency selective with Rayleigh fading. The proposed equalizer structure consists of the series connection of an adaptive FIR filter and a fixed equalizer (designed for the nominal channel). Adaptation of the weight vector of the adaptive FIR filter is achieved using the H_∞-optimal solution of an estimation-based interpretation of the channel equalization problem. Due to its H_∞-optimality, the proposed solution is robust to exogenous disturbances, and enables fast adaptation (i.e., a short training period) without compromising the steady-state performance of the equalization. Preliminary simulation are presented to support the above claims