Signal Processing Techniques for Increasing Channel Capacity in Wireless Communications

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As the digital signal processing technology advances, the use of adaptive arrays to combat multipath fading and to reduce interference becomes increasingly valuable as a means of adding capacity to mobile communications. This dissertation address the major obstacles encountered in applying the two most applicable adaptive array algorithms to time division multiple access (TDMA) wireless communication systems.

We first investigated the reference signal based adaptive diversity combining algorithm, which conventionally relies on feedback symbols in the absence of reference signals. Our computer simulation revealed that on a fast time varying fading channel, error propagation in the decision directed tracking mode severely degrades the performance. We developed a simultaneous diversity combining and decoding technique which incorporated QR decomposition-based recursive least-square parallel weights tracking and M-D decoding algorithms. In contrast to the conventional system where only one set of array weights is kept and updated, in our system, we update M sets of candidate weights. Thus we are able to make a more reliable symbol decision based on D symbols without compromising weights tracking speed. The M-D algorithm was first developed for the binary convolutional codes and then extended to Trellis-coded modulation. This technique significantly reduces error propagation. Simulation results showed that about 8 to 10dB improvement in the total interference suppression at low ISR and about 5dB improvement at high ISR can be achieved with a moderate increase in complexity.

In the next part of the dissertation, we proposed and studied the use of the constrained adaptive array algorithm for extracting signals from interferences at separable directions. This algorithm requires direction-of-arrival (DOA) information and does not need reference signals. However, most of the high resolution DOAs estimation methods are only effective for noncoherent signals, while in mobile radio channels, coherent signals are inevitable. We developed a general spatial smoothing (SS) technique and a forward backward spatial smoothing technique for two dimensional arrays to decorrelate coherent signals from arbitrary directions. We found and proved the necessary and sufficient conditions on an array configuration for applying SS. This array must have an orientational invariance structure with an ambiguity free center array, and the number of subarrays must be larger than or equal to the size of the largest group of coherent signals. We also studies the causes of ambiguities and found some ambiguity free array manifolds. We expanded the application of our SS to several high resolution DOA estimation and constrained adaptive beamforming algorithms. All the predicted results were verified by simulations. In the last part of the dissertation, we investigated the applications of adaptive array technique in DS/CDMA systems. We applied reference-signal- based simultaneous diversity combining and decoding to reduce fading and suppress interference caused by poor synchronization and power control.