Decision-feedback subset aided multiple-symbol differential detection

In view of the inaccuracy of the estimated symbols on the edge of the observation window, a decision-feedback subset aided multiple-symbol differential detection(MSDD) framework, dubbed DF-S-MSDD, is proposed in ultra-wideband impulse radio(UWB-IR) system with differential space-time block-code(DSTBC) modulation. Specifically, motivated by the decision-feedback aided MSDD(DF-MSDD), a subset of the decision-feedback symbols is selected, and the optimal symbols are preserved, and then all the remaining symbols are optimized. Furthermore, the simulations validate that the proposed DF-S-MSDD provides solid bit error-rate performance with a low complexity in UWB-IR system with DSTBC modulation.

Data streams classification with ensemble model based on decision-feedback

The main challenges of data streams classification include infinite length, concept-drifting, arrival of novel classes and lack of labeled instances. Most existing techniques address only some of them and ignore others. So an ensemble classification model based on decision-feedback（ECM-BDF） is presented in this paper to address all these challenges. Firstly, a data stream is divided into sequential chunks and a classification model is trained from each labeled data chunk. To address the infinite length and concept-drifting problem, a fixed number of such models constitute an ensemble model E and subsequent labeled chunks are used to update E. To deal with the appearance of novel classes and limited labeled instances problem, the model incorporates a novel class detection mechanism to detect the arrival of a novel class without training E with labeled instances of that class. Meanwhile, unsupervised models are trained from unlabeled instances to provide useful constraints for E. An extended ensemble model Ex can be acquired with the constraints as feedback information, and then unlabeled instances can be classified more accurately by satisfying the maximum consensus of Ex. Experimental results demonstrate that the proposed ECM-BDF outperforms traditional techniques in classifying data streams with limited labeled data.

Particle filter based stochastic interference cancellation for frequency-selective MIMO channel equalization

To mitigate the effects of the previous symbol decision errors of a decision-feedback （DF） equalizer on the current decision, a particle filter （PF） based DF equalizer for frequency selective multiple-input-multiple-output （MIMO） channel is proposed. On the basis of the analyses of DF equalization for the MIMO wireless system, it is found that a stochastic interference cancellation （IC） scheme can be employed to prevent the error propagation in a severe space-time interference scenario. This is because the random rather than the deterministic scheme can reduce the probability of an error decision even if an error decision occurs. Besides, the signal-to-interference-plus-noise ratio （SINR） based IC order, which is obtained via pilot, can guarantee the optimality of the cancellation. The bit error rate （BER） performance of the proposed scheme is verified through simulation experiments under different multipath interference environment.

A New Multiuser Detector for Synchronous DS/CDMA System

Several multiuser detectors for Code-Division Multiple-Access(CDMA) system have be'n studied recently. In this paper we propose the orthogonal decision-feedback detector. It combines the decorrelating decisionfeedback detector with the orthogonal detector, and has the merit of good performance of the decorrelating decisionfeedback detector and simple structure of the orthogonal detector The performance of the new multiuser detector is close to the decorrelating decision-feedback detector but its construction is simpler. Because of the similarities betWeen the multiuser detection problem and the decoding of the convolutional code, a tree search algorithm is taken on the detector which makes its performance improved. and approaches the single user bound.