Detection Of A New Generation Of Short-range Wireless Communication System Signal And Reception Techniques

The short-range wireless communications, characterized by high-data-rate, wide bandwidth, low-power consumption, low cost and personalized ap-plications, have emerged as an important part of future ubiquitous wireless ac-cessing networks, which hence nowadays have drawn general attention. Sup-ported by the Natural Science Foundation of China as well as the National Sci-ence and Technology Major Projects, this thesis is mainly devoted to promot-ing signal processing in the future short-range wireless communications, which may have the great theoretic significance and the wide application prospect.This thesis mainly investigated the signal receiving techniques in next-generation short-range wireless communications, with the emphasis especially put on signal detection and reception in intensive multipath propagations. Ac-cordingly, the main innovative contributions of the investigation can be sum-marized as follows.1. For the issues of parameters extraction, parametric modeling and model evaluation in the intensive multipath channels,1) two kinds of efficient auto-matic cluster identification algorithms, inspired by two promising perspectives of discontinuity/singularity detection and biological pattern clustering, have been proposed, which can significantly improve the accuracy of cluster identi-fications and parameters extractions;2) a novel parametric intra-cluster power delay profile (PDP) model derived from the Fresh reflection theory is present-ed, which, for the first time, reveals a new kind large-scale frequency selectivity and can match the multipath measurement more effectively;3) a clustered PDP weighted low-complexity signal detector is proposed, which can enhance the receiving performance and is hence of significance to verify the effectiveness and accuracy of the proposed cluster identifications and PDP modeling.2. For the low-complexity short-range wireless communications in the p- resence of intensive multipath propagations, a unified new non-coherent signal detection framework is designed.1) A pattern classification-based noncoher-ent signal detection algorithm, based on the covariance matrix of received ran-dom signals and a developed novel characteristic spectrum, is proposed, and a promising biologically inspired signal processing (Bio-SP) framework is then suggested by combining nature intelligence inspired algorithms;2) Four non-coherent detection schemes are designed for several scenarios with different realistic requirements, i.e., the fuzzy c-means (FCM) clustering based blind noncoherent detector, the modified ant colony clustering based blind nonco-herent detector, the Parzen probabilistic neural network (PPNN) based non-coherent detector for the online data-aided (DA) case, and a new biological algorithm-based off-line supervised noncoherent detector;3) A novel quantum memetic algorithm (QMA) inspired by the memetic gene and quantum comput-ing mechanics is developed, which can realize the high-performance numerical optimization and signal classification/detection.3. For the complex scenarios with both intensive multipath propagations and nonlinear power amplifier distortions, a novel nonlinear dynamic state-space model (DSM) is properly formulated. On this basis, relying on the Bayesian statistical inference, a joint channel estimation and blind signal detec-tion algorithm is developed. By excluding the pre-distortion in transceiver-end with high complexity, the distorted signal constellations, after propagated from both nonlinear PA and linear multipath, are directly calibrated in receiver-end. Since the existing Monte-Carlo sequential importance sampling can hardly deal with the nonlinear equalization, a local linearization technique is introduced and a generalized particle filtering is presented. Thus, a sequentially joint and blind estimation technique is proposed which can simultaneously address both multipath interference and nonlinear distortion.4. Considering the high complexity of the existing beam-forming train-ing in the next-generation ultra-high speed WLANs/WPANs, from a promis-ing perspective of non-constrained numerical optimization, two efficient beam- forming training algorithms based on numerical optimization are developed.1) The first algorithm, premised on a Rosenbrock numerical search, is suggested to identify optimal beam-patterns. To overcome the shortcoming of classical Rosenbrock numerical search which may easily fall into local optimums, a pre-search algorithm inspired by the small region dividing-and-conquering is presented. The new algorithm can find the optimal beams with a success prob-ability of100%, which thereby significantly reduces the search complexity, the protocol overhead and power consumption.2) A new numerical search algo-rithm is developed by integrating the probabilistic disturbances mechanic and a novel two-level disturbances control scheme. The developed numerical algo-rithm is of special promise to the discrete-space optimization, which basical-ly avoids local optimums and the computation demanding local exploitations. This new algorithm can identify optimal beams with an probability of100%in intensive multipath propagations.5. In order to address the difficulty of signal detections in the presence of dynamic time-varying fading propagations, a novel dynamic state-space model is developed and then a unified joint channel estimation and blind signal de-tection is proposed.1) A distributed spectrum sensing algorithm for cognitive radios is proposed which relies on the Bayesian statistical inference and Monte-Carlo random sampling theorem. The time-varying fading gain, accompanying the primary user working state, is iteratively estimated in time. To the best of our knowledge, for the first time, this new spectrum sensing algorithm real-izes the joint estimation of unknown primary state and time-varying channel gain, which can significantly improve the sensing performance in time-varying fading channel.2) A blind signal detection algorithm in the presence of time-varying intensive multipath propagations is proposed. An efficient iterative detection algorithm is designed which can jointly estimate the time-varying multipath channel and the unknown transmitted symbols. The new algorith-m can significantly enhance the blind detection performance in time-varying intensive multipath propagations. Finally, the investigations of the whole dissertation are summarized. On this basis, several valuable research directions on signal reception techniques in future short-range wireless connecting are discussed.