Space-time Equalization Research Articles

Implementation of space-time equalizer using multiple single-constrained SMI array processors and MLSE

We implemented a space-time equalizer using two sets of single-constrained sample matrix inversion array processors and a maximum-likelihood sequence estimator by using digital signal processors and field-programmable gate arrays. One of the array processors constrains the direct path, sends the one-symbol-delayed path component to the array output, and suppresses the paths with longer delays. The other array processor constrains the one-symbol-delayed path, sends the direct path component to the array output, and also suppresses the paths with longer delays. The desired paths, thus, extracted, whose signal-to-interference-plus-noise-ratios are improved in both path-diversity branches, are then combined by using a branch-metric-combining Viterbi equalizer. We implemented a receiver equipped with this equalizer and evaluated its bit-error rate performance by using a channel emulator. Experimental results indicate that the space-time equalizer provides both space diversity gains and path diversity gains while suppressing signals on paths with long delays.

Training sequence and memory length selection for space-time Viterbi equalization

We consider signal and receiver design for space-time Viterbi equalization for wireless transmission. We propose a search method to find good training sequences, termed min-norm training sequences, for least-square channel estimation. Compared to either a maximum-length sequence or a randomly generated training sequence, the training sequence obtained can drastically reduce the channel estimation error. We also derive a simple lower bound on the achievable channel estimation error of any training sequence. Knowledge of this lower bound helps the search for min-norm training sequences in that it facilitates a measure of the goodness of the best sequence examined so far. For operation under the situation with unknown channel response lengths, we propose a simple method to select the memory length (tap number) in the Viterbi equalizer based on the SNR of the received signal. The resulting equalization performance is found to be comparable with the case where a preset, fixed memory length is used. However, the proposed method often results in use of a smaller tap number, which translates into a reduction in the computational complexity. Simulation results show that at symbol error rate below 10 (SNR > 5 dB) the amount of complexity reduction is of the order of 5% to 25% on the average, for typical wireless channels.

Optimum space-time processors with dispersive interference: unified analysis and required filter span

We consider optimum space-time equalizers with unknown dispersive interference, consisting of a linear equalizer that both spatially and temporally whitens the interference and noise, followed by a decision-feedback equalizer or maximum-likelihood sequence estimator. We first present a unified analysis of the optimum space-time equalizer, and then show that, for typical fading channels with a given signal-to-noise ratio (SNR), near-optimum performance can be achieved with a finite-length equalizer. Expressions are given for the required filter span as a function of the dispersion length, number of cochannel interferers, number of antennas, and SNR, which are useful in the design of practical near-optimum space-time equalizers.

Integrated hybrid adaptive space-time equaliser for interference rejection in multipath DS/CDMA systems

An integrated hybrid space-time equaliser is proposed for interference suppression in DS/CDMA systems. It outperforms the existing time-domain-only counterpart, and previous work on the integrated space-time receiver in terms of MSE and near-far resistance. A comparison between simulation and analytical results is also presented.

Adaptive channel identification and equalisation algorithms using forward–backward optimisation

An adaptive semi-blind approach for space-time channel identification and equalisation is proposed which exploits the inherent structural constraints and relationships of the channel output vectors and data sequence by means of a forward–backward optimisation procedure. Two adaptive semi-blind algorithms based on this approach are presented and analysed using computer simulations. In addition to being data efficient and fast converging, the proposed algorithms demonstrate good bit error rate performance with an extremely short training sequence in a time-varying multipath channel and low signal-to-noise signal environment.

Complex scaled tangent rotations (CSTAR) for fast space-time adaptive equalization of wireless TDMA

A new update algorithm for space-time equalization of wireless time-division multiple access signals is presented. The method is based on a modified QR factorization that reduces the computational complexity of the traditional QR-decomposition based recursive least squares method and maintains numerical stability. Square roots operations are avoided due to the use of an approximately orthogonal transformation, defined complex scaled tangent rotation.

A subspace approach to blind space-time signal processing for wireless communication systems

The two key limiting factors facing wireless systems today are multipath interference and multiuser interference. In this context, a challenging signal processing problem is the joint space-time equalization of multiple digital signals transmitted over multipath channels. We propose a blind approach that does not use training sets to estimate the transmitted signals and the space-time channel. Instead, this approach takes advantage of spatial and temporal oversampling techniques and the finite alphabet property of digital signals to determine the user symbol sequences. The problem of channels with largely differing and ill-defined delay spreads is discussed. The proposed approach is tested on actual channel data.