A novel single lag auto-correlation minimization (SLAM) algorithm for blind adaptive channel shortening

A blind adaptive channel shortening algorithm based on minimizing the sum of the squared autocorrelations (SAM) of the effective channel was recently proposed. We submit that identical channel shortening can be achieved by minimizing the square of only a single autocorrelation. Our proposed single lag autocorrelation minimization (SLAM) algorithm has, therefore, very low complexity and also it does not require, a priori, the knowledge of the length of the channel. We also constrain the autocorrelation minimization with a novel stopping criterion so that the shortening signal to noise ratio (SSNR) of the effective channel is not minimized by the autocorrelation minimization. The simulations have shown that SLAM achieves higher bit rates than SAM

An adaptive step-size code-constrained minimum output energy receiver for nonstationary CDMA channels

The adaptive step-size (AS) code-constrained minimum output energy (CMOE) receiver for nonstationary code-division multiple access (CDMA) channels is proposed. The AS-CMOE algorithm adaptively varies the step-size in order to minimise the CMOE criterion. Admissibility of the proposed method is confirmed via the reformulation of the CMOE criterion as an unconstrained optimisation. The ability of the algorithm to track sudden changes of the channel structure in multipath fading channels is assessed. Sensitivity to the initial values of the step-size and the adaptation rate of the algorithm is also investigated

Random partial update sum-squared autocorrelation minimization algorithm for channel shortening (RPUSAM).

Partial updating is an effective method for reducing computational complexity in adaptive filter implementations. In this work, a novel random partial update sum-squared auto-correlation minimization (RPUSAM) algorithm is proposed. This algorithm has low computational complexity whilst achieving improved convergence performance, in terms of achievable bit rate, over a partial update sum-squared auto-correlation minimization (PUSAM) algorithm with a deterministic coefficient update strategy. The performance advantage of the RPUSAM algorithm is shown on eight different carrier serving area test loops (CSA) channels and comparisons are made with the original SAM and the PUSAM algorithms

A new cross-correlation and constant modulus type algorithm for PAM-PSK signals

We address the problem of blind recovery of multiple sources from their linear convolutive mixture with the cross-correlation and constant modulus algorithm. The steady state mean-squared error of this algorithm is first derived to justify the proposal of a new cross-correlation and constant modulus type algorithm for PAM-PSK type non-constant modulus signals. Simulation studies are presented to support the improved steady-state performance of the new algorithm

A modified underdetermined blind source separation algorithm using competitive learning

The problem of underdetermined blind source separation is addressed. An advanced classification method based upon competitive learning is proposed for automatically determining the number of active sources over the observation. Its introduction in underdetermined blind source separation successfully overcomes the drawback of an existing method, in which the goal of separating more sources than the number of available mixtures is achieved by exploiting the sparsity of the non-stationary sources in the time-frequency domain. Simulation studies are presented to support the proposed approach

Closed-loop extended orthogonal space time block coding for four relay nodes under imperfect synchronization

In future collaborative wireless communication systems with high data rate, interference cancellation is likely to be required in cooperative networks at the symbol level to mitigate synchronization errors. In this paper, we therefore examine closed-loop extended orthogonal space time block coding (CL EO-STBC) for four relay nodes and apply parallel interference cancellation (PIC) detection scheme to mitigate the impact of imperfect synchronization. Simulation results illustrate that the closed-loop EO-STBC scheme under imperfect synchronization can achieve good performance with simple linear processing and outperform previous methods. Moreover, a PIC scheme is shown to be very effective in mitigating impact of imperfect synchronization with low structural and computational complexity

Adaptive partial update channel shortening in impulsive noise environments

Partial updating is an effective method for reducing computational complexity in adaptive filter implementations. In this paper adaptive partial update channel shortening algorithms in impulsive noise environments are proposed. These algorithms are based on updating a portion of the coefficients at each time sample instead of the entire set of coefficients. These algorithms have low computational complexity whilst retaining essentially identical performance to the sum-absolute autocorrelation minimization (SAAM) algorithm due to Nawaz and chambers. Simulation studies show the ability of the deterministic partial update SAAM (DPUSAAM) algorithm and the Random Partial Update SAAM (RPUSAAM)algorithm to achieve channel shortening and hence an acceptable level of bitrate within a multicarrier system

Heuristic pattern correction scheme using adaptively trained generalized regression neural networks

In many pattern classification problems, an intelligent neural system is required which can learn the newly encountered but misclassified patterns incrementally, while keeping a good classification performance over the past patterns stored in the network. In the paper, an heuristic pattern correction scheme is proposed using adaptively trained generalized regression neural networks (GRNNs). The scheme is based upon both network growing and dual-stage shrinking mechanisms. In the network growing phase, a subset of the misclassified patterns in each incoming data set is iteratively added into the network until all the patterns in the incoming data set are classified correctly. Then, the redundancy in the growing phase is removed in the dual-stage network shrinking. Both long- and short-term memory models are considered in the network shrinking, which are motivated from biological study of the brain. The learning capability of the proposed scheme is investigated through extensive simulation studie

A CMOE-CMA RAKE receiver structure for near-far frequency selective fading CDMA channels

A novel initialization scheme for a constant modulus algorithm RAKE (CMA-RAKE) receiver for frequency-selective fading asynchronous code-division multiple access (CDMA) channels is proposed. The solutions from the minimization of the constrained minimum output energy (CMOE) criterion are adopted as the initialization of the subsequent CMA receivers. The proposed receiver is proven to be near-far resistant at different levels of near-far problems. Simulations confirm the superiority of the receiver over the existing RAKE receivers in terms of signal-to-interference plus noise (SINR) ratio over a wide range of near-far situation

Blind adaptive channel shortening with a generalized lag-hopping algorithm which employs squared auto-correlation minimization [GLHSAM].

A generalized blind lag-hopping adaptive channel shortening (GLHSAM) algorithm based upon squared auto-correlation minimization is proposed. This algorithm provides the ability to select a level of complexity at each iteration between the sum-squared autocorrelation minimization (SAM) algorithm due to Martin and Johnson and the single lag autocorrelation minimization (SLAM) algorithm proposed by Nawaz and Chambers whilst guaranteeing convergence to high signal to interference ratio (SIR). At each iteration a number of unique lags are chosen randomly from the available range so that on the average GLHSAM has the same cost as the SAM algorithm. The performance of the proposed GLHSAM algorithm is confirmed through simulation studies