Harold Artes

Unbiased scattering function estimators for underspread channels and extension to data-driven operation

We propose two methods for the estimation of scattering functions of random time-varying channels. In contrast to existing methods, our methods exploit the underspread property of these channels to achieve good estimation performance and low computational complexity. The first method uses a dedicated sounding to measure the channel. The second method uses the data signal of an ongoing data transmission as sounding signal and thus allows estimation without dedicated sounding. Both methods are effectively unbiased and can be implemented efficiently using the Zak transform. The performance of our scattering function estimators is studied both analytically by means of variance bounds and experimentally through numerical simulation, and their superiority over existing methods is demonstrated.

Detection techniques for MIMO spatial multiplexing systems

We discuss and compare the most important detection techniques for MIMO spatial multiplexing wireless systems, focusing on their performance and computational complexity. Our analysis shows that the limited performance of conventional suboptimal detection techniques is primarily caused by their inability to cope with poorly conditioned channels. The recently proposed sphere projection algorithm is better suited to these channels and can achieve near-optimal performance.AbstractWir diskutieren und vergleichen die Arbeitsweise, Leistungsfähigkeit und Komplexität der wichtigsten Detektionsmethoden für Mehrantennen-Funkübertragungssysteme mit räumlichem Multiplex. Unsere Analyse zeigt, dass die begrenzte Leistungsfähigkeit herkömmlicher suboptimaler Detektoren durch schlecht konditionierte Kanäle bedingt ist. Der kürzlich vorgeschlagene Kugelprojektionsdetektor ist besser für diese Kanäle geeignet; seine Leistungsfähigkeit kann jener des optimalen Detektors nahekommen.

Blind equalization of MIMO channels using deterministic precoding

We present a novel precoding or modulation scheme (matrix modulation) that allows parallel transmission of several data signals over an unknown multiple-input multiple-output (MIMO) channel. We first present a theorem on unique signal demodulation and an efficient iterative demodulation algorithm for transmission over an unknown instantaneous-mixture channel. We then generalize our results to an unknown MIMO channel with memory.

Unbiased scattering function estimation during data transmission

We propose an unbiased, data-driven estimator of the scattering function of random time-varying channels satisfying the usual wide-sense stationary uncorrelated scattering (WSSUS) assumption. The estimator allows continuous on-line operation during data transmission, with segments of the data signal serving as sounding signals. A matched filterbank preprocessing stage is used to compensate for the nonideal properties of these sounding signals. The proposed estimator is well suited to fast time-varying channels, and it exploits the underspread property of typical mobile radio channels for a substantial reduction of computational complexity. The performance of the new estimator is assessed by a bias/variance analysis and simulation results.

Efficient POCS algorithms for deterministic blind equalization of time-varying channels

We present two POCS (projections onto convex sets) algorithms for deterministic blind equalization of linear time-varying (LTV) channels. Our approach is based on a multichannel LTI representation of LTV channels. We prove a theorem on unique reconstruction/equalization, formulate a POCS algorithm for the resolution of a matrix ambiguity, and show how to cope with unequal subchannel lengths. We also present an alternative POCS algorithm for equalization that avoids a singular value decomposition. Both algorithms are guaranteed to converge to the desired solution.

Bounds on the systematic measurement errors of channel sounders for time-varying mobile radio channels

We show that correlative sounders for time-varying mobile radio channels are affected by various systematic measurement errors. We identify and analyze these errors and provide quantitative error bounds in terms of channel and sounder parameters. Furthermore, we propose a novel calibration method yielding substantially improved accuracy. Experiments using measured data and computer simulations illustrate the theoretical results. The findings allow to assess the accuracy of existing measurements as well as to devise improved measurement techniques.

Efficient approximate-ML detection for MIMO spatial multiplexing systems by using a 1-D nearest neighbor search

It is known that suboptimal (equalization-based and nulling-and-cancelling) detectors for MIMO spatial multiplexing systems cannot exploit all of the available diversity. Motivated by the insight that this behavior is mainly caused by poorly conditioned channel realizations, we propose the line-search detector (LSD) that is robust to poorly conditioned channels. The LSD uses a 1-D nearest neighbor search along the least significant singular vector of the channel matrix. It exhibits near-ML performance and has significantly lower complexity than the sphere-decoding algorithm for ML detection.

Efficient near-ML detection for MIMO channels: the sphere-projection algorithm

It is well known that suboptimal detection schemes for MIMO spatial multiplexing systems (equalization-based as well as nulling-and-cancelling detectors) cannot exploit all of the available diversity. We show that this inferior performance is primarily caused by poorly conditioned channel realizations. We then present the novel sphere-projection algorithm (SPA) that is robust to poorly conditioned channels. The SPA is a computationally efficient add-on to standard suboptimal detectors. Simulation results show that the SPA is able to achieve near-ML performance and significantly increased diversity gains. The SPAs computational complexity is comparable to that of nulling-and-cancelling detectors and only a fraction of that of the Fincke-Phost sphere-decoding algorithm for ML detection (Fincke, U. and Phost, M., Math. of Comp., vol.44, p.463-71, 1985).

Multiuser Space–Time Algorithms for Synchronization, Channel Estimation, and Data Detection in an Interference Monitoring System for UMTS/TDD Networks

We present multiuser space-time receiver algorithms for synchronization, channel estimation, and data detection in the downlink of a universal mobile telecommunications system (UMTS)/time-division duplex (TDD) cellular communication system with multiple receive antennas. These algorithms were designed for use in a network monitoring device that analyzes the interference situation present, thereby allowing the operators to improve their networks. For interference analysis, we decode the broadcast channels (BCHs) of surrounding base stations. To cope with the widely differing power levels of signals received from different base stations, we combine multiuser space-time signal processing techniques with reestimation and successive cancellation schemes. Simulation results demonstrate that our algorithms enable reliable BCH data detection even at low SINR.

Reducing sphere decoder complexity by elliptical tree pruning

We propose a computationally efficient detection algorithm with near ML performance for space-time communications systems. Our algorithm is based on pruning (excluding) branches from the search tree arising in the sphere decoding (SD) algorithm. The choice of the branches to be pruned relies on intuitive geometrical reasoning which is in turn based on a simplified channel model designed to capture the effect of badly conditioned channel realizations. The remaining search tree after pruning can be processed by the conventional SD algorithm with strongly reduced complexity as against scanning the complete tree. Simulation results show that despite the pruning near ML results can be obtained.