Adaptive detection in ultrawide bandwidth wireless communication systems

The main motivation of this thesis is to design low-complexity high-efficiency pulse-based ultrawide bandwidth (UWB) systems with reasonable bit-error-rate (BER) performance. The thesis starts with proposing a new pulse-based UWB system, namely the hybrid direct-sequence time-hopping (DS-TH) UWB system. This novel pulse-based UWB system is capable of inheriting the advantages of both the pure direct-sequence (DS)-UWB and pure time-hopping (TH)-UWB systems, while avoiding their disadvantages. Furthermore, this hybrid DS-TH UWB scheme can be easily converted to the pure DS-UWB or pure TH-UWB scheme. The BER performance of the hybrid DS-TH UWB systems employing either correlation or minimum mean-square error (MMSE) detection is investigated. From our studies it can be found that both the correlation and MMSE detectors have the capability to make use of the multipath diversity. The correlation detector does not have the capability to remove multiuser interference (MUI) and inter-symbol interference (ISI), while the MMSE detector is capable of mitigating efficiently both the ISI and MUI. While for single-user scenario the correlation detector is near-optimum and has low-complexity, it is shown that for multi-user scenarios the MMSE detector must be employed in order to achieve a reasonable BER performance. However, in this case the complexity of the hybrid DS-TH UWB system is found to be extreme. Furthermore, in order to implement MMSE detection, the signature waveforms, delays and complete channel knowledge of all the active users are required to be known by the receiver, which make the MMSE detection impractical. In practical channels obtaining the channel knowledge is highly challenging, since the received UWB signals usually consist of a huge number of resolvable multipaths and the energy conveyed by each resolvable multipath is usually very low.In order to mitigate the above mentioned problems of the MMSE detection, then, in this thesis a range of training-based adaptive detectors are investigated in the context of the hybrid DS-TH UWB systems. In detail, in this thesis a brief introduction to the literature of adaptive detection is first provided, followed by the philosophies of least mean-square (LMS), normalised least-mean squares (NLMS) and recursive least square (RLS) algorithms. In our study decision directed (DD) approaches are also introduced to the adaptive detectors to improve the BER performance and spectral-efficiency of the hybrid DS-TH UWB systems. Our studies show that the complexity of the adaptive LMS and adaptive NLMS detectors may be even lower than that of the conventional correlation detector. For the RLS adaptive detector, our studies show that, if it is initialised properly, it is capable of attaining a faster convergence rate than the LMS and NLMS adaptive detectors. In this case, the RLS adaptive detector requires less number of training bits, and hence provides higher spectral-efficiency than the LMS and NLMS adaptive detectors for the hybrid DS-TH UWB systems. Furthermore, the RLS adaptive detector is more robust and has more degrees of freedom than the LMS and NLMS adaptive detectors. However, the complexity of the RLS adaptive detector is still too high to be implemented in practical UWB systems.In order to further reduce the complexity of the RLS adaptive detector, rank-reduction techniques are introduced. With the aid of reduced-rank techniques, the filter size can be efficiently reduced, which in turn reduces the number of parameters required to be estimated. Consequently, the convergence speed, tracking ability and robustness of the RLS adaptive detector can be improved. In this thesis, three classes of reduced-rank techniques are investigated associated with the RLS adaptive detector, which are derived based on the principles of principal components analysis (PCA), cross spectral metric (CSM) and Taylor polynomial approximation (TPA), respectively. Our study and simulation results show that, given a sufficient rank of the detection subspace on which the RLS adaptive detector is operated, the reduced-rank RLS adaptive detector is capable of achieving a similar BER performance as the corresponding full-rank RLS adaptive detector, while with a detection complexity that is significantly lower than that of the fullrank RLS adaptive detector. Furthermore, our studies shown that the TPA-based reduced-rank RLS adaptive detector constitutes one of the highly efficient detection schemes for the pulse-based UWB systems. The TPA-based reduced-rank RLS adaptive detector is usually capable of attaining the full-rank BER performance with a very low rank, which is typically in the range of 5 - 8, regardless of the system size in terms of the spreading factor, number of resolvable multipaths and the number of users supported by the UWB systems.Finally, in this thesis we summarise our discoveries and provide discussion on the possible future research issues