Systolic Design of MIMO Lattice Detection and Channel Modeling for Wireless Communication Systems

Two independent but equally challenging problems in the wireless communication systems are considered in this dissertation. First, the systolic design of the multiple-input, multiple-output (MIMO) lattice reduction-aided detection is proposed. Lattice-reduction-aided detection (LRAD) has been shown to be an effective low-complexity method with near-ML performance. However, lattice reduction needs to be performed as channel state changes. As the channel change rate is high, or a large number of channel matrices need be processed such as in a MIMO-OFDM system, a fast-throughput algorithm and the corresponding implementation structure are needed for real-time applications. In this dissertation we advocate the use of systolic array architectures for MIMO receivers, and in particular we exhibit one of them based on LRAD. The "LLL lattice reduction algorithm'' and the ensuing linear detections or successive spatial-interference cancellations can be located in the same array, which is hardware-efficient. Two modified LLL algorithms suitable for parallel processing are considered here for the systolic design, LLL algorithm with full-size reduction and all-swap lattice-reduction algorithm. In order to simplify the systolic array design, we replace the Lovasz' condition in the definition of LLL-reduced lattice with the looser Siegel's condition and limit the range of μ value. Simulation and FPGA emulation results show that the proposed systolic LLL is 1.6 time faster than the conventional LLL while the bit-error-rate performance of LRAD is still maintained with these relaxations. Second, we consider the modeling of fading channels under abrupt changes. Fading channel is generally nonstationary in time, especially when there are moving objects near the field of transmission. The statistics of the channel are changing due to the temporal and spatial inhomogeneity. To characterize the temporal variation of the channel, short-term statistics need to be estimated. Instead of estimating the statistics over a fixed short period, we applied the Bayesian change point detection (CPD) for five common channel models to capture the locations of changes in time. The detected change points partition the channel into segments that are characterized by different parameters. We also derive the MAP and MMSE estimators for the model parameters of each segment based on the intermediate results of CPD. Therefore, once a change is detected, the parameters are obtained immediately. Test results on 802.11n channel simulator and channel measurement show the effectiveness of the CPD and the proposed estimators.We also found CPD to be useful in biological applications. A bird phrase segmentation using entropy-based change point detection is proposed. Spectrograms of bird calls are usually sparse while the background noise is relatively white. Therefore, considering the entropy of a sliding time- frequency block on the spectrogram, the entropy dips when detecting a signal and rises back up when the signal ends. Rather than a hard threshold on the entropy to determine the beginning and ending of a signal, CPD is used to detect the statistical changes in the entropy sequence. With the novel spectral whitening method as the front-end processing, our proposed segmentation method generates more accurate time labels, reduces the false alarm rate and achieves higher classification rates than the conventional time-domain energy detection method