Ng Cho Yiu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 70-74). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Preface --- p.x / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Multiple Access --- p.1 / Chapter 1.1.1 --- Time Division Multiple Access --- p.2 / Chapter 1.1.2 --- Frequency Division Multiple Access --- p.3 / Chapter 1.1.3 --- Code Division Multiple Access --- p.3 / Chapter 1.1.4 --- Hybrid Multiple Access Scheme --- p.4 / Chapter 1.2 --- Goal Programming --- p.5 / Chapter 2 --- Previous Works in Channel Assignment --- p.10 / Chapter 2.1 --- Voice Service Network --- p.10 / Chapter 2.2 --- Data Network --- p.11 / Chapter 2.2.1 --- Throughput Optimization --- p.13 / Chapter 2.2.2 --- Channel Assignment Schemes with QoS Consideration --- p.14 / Chapter 3 --- General Channel Assignment Scheme --- p.16 / Chapter 3.1 --- Baseline Model --- p.17 / Chapter 3.2 --- Goal Ranking --- p.22 / Chapter 3.3 --- Model Transformation --- p.22 / Chapter 3.4 --- Proposed Algorithms --- p.23 / Chapter 3.4.1 --- Channel Swapping Algorithm --- p.24 / Chapter 3.4.2 --- Best-First-Assign Algorithm --- p.26 / Chapter 4 --- Special Case Algorithms --- p.28 / Chapter 4.1 --- Single Order of Selection Diversity --- p.28 / Chapter 4.1.1 --- System Model --- p.29 / Chapter 4.1.2 --- Proposed Algorithm --- p.30 / Chapter 4.1.3 --- Extension of Algorithm --- p.31 / Chapter 4.2 --- Single Channel Assignment --- p.32 / Chapter 4.2.1 --- System Model --- p.33 / Chapter 4.2.2 --- Proposed Algorithms --- p.34 / Chapter 5 --- Performance Evaluation --- p.37 / Chapter 5.1 --- General Channel Assignment and Single Channel Assignment --- p.37 / Chapter 5.1.1 --- System Model --- p.38 / Chapter 5.1.2 --- Lower Bound of Weighted Sum of Unsatisfactory Function --- p.40 / Chapter 5.1.3 --- Performance Evaluation I --- p.41 / Chapter 5.1.4 --- Discussion --- p.44 / Chapter 5.1.5 --- Performance Evaluation II --- p.44 / Chapter 5.2 --- Single Order of Selection Diversity Algorithm --- p.47 / Chapter 5.2.1 --- System Model --- p.47 / Chapter 5.2.2 --- Performance Evaluation I --- p.49 / Chapter 5.2.3 --- Performance Evaluation II --- p.53 / Chapter 6 --- Conclusion and Future Works --- p.58 / Chapter 6.1 --- Conclusion --- p.58 / Chapter 6.2 --- Future Works --- p.60 / Chapter 6.2.1 --- Multi-cell Channel Assignment --- p.60 / Chapter 6.2.2 --- Theoretical Studies --- p.62 / Chapter 6.2.3 --- Adaptive Algorithms --- p.62 / Chapter 6.2.4 --- Assignment of Non-orthogonal Channels --- p.63 / Chapter A --- Proof of Proposition 3.1 --- p.64 / Chapter B --- Proof of Proposition 4.1 --- p.66 / Chapter C --- Assignment Problem --- p.68 / Bibliography --- p.74

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Recently, there has been an increasing demand for OFDM system operating in high mobility environment. In such situation, wireless channel is both frequency-selective and time-varying, a.k.a. doubly-selective, making it hard for the receiver to keep track of the channel state information (CSI). Moreover, the statistical information of channel, e.g., tap positions, channel length, Doppler shifts and noise power, is generally unknown to the receiver. In this thesis, two kinds of mobile OFDM systems are investigated for data detection and channel estimation. Different from previous works, which highly depend on the statistical information of the doubly selective channel to deliver accurate channel estimation and data detection results, we focus on more practical scenarios with unknown channel orders and Doppler frequencies. Firstly, point-to-point OFDM system with high mobility is considered. Due to the unknown channel characteristics, we formulate the channel using GCE-BEM with a large oversampling factor. The resulted GCE-BEM coefficients are sparse on delay-Doppler domain and contain only a few nonzero elements. To enable the identification of nonzero entries, sparsity enhancing Gaussian priors with Gamma hyperpriors are adopted. An iterative algorithm is developed under variational inference (VI) framework. The proposed algorithm iteratively estimate the channel, recover the unknown data using Viterbi algorithm and learn the channel and noise statistical information, using only limited number of pilot subcarrier in one OFDM symbol. Secondly, we investigate multihop amplify-and-forward (AF) OFDM system, where system structure is generally unknown to the receiver due to the variable number of hops and relaying paths in high mobility environment. We notice that in AF relaying systems, the composite source-relay-destination channel is sufficient for data detection. Then we integrate the multilink, multihop channel matrices into one composite channel matrix, which turns out to have the same structure as the point-to-point OFDM channel. The reformulated system model is more concise and a similar iterative algorithm to that of the point-to-point case can be derived to estimate the composite channel and detect data. This means that the proposed framework applies to OFDM system under high mobility regardless of the system structure. Simulation results show that the performance of the proposed algorithm is very close to that of the optimal channel estimation and data detection algorithm, which requires specific information of system structure, channel tap positions, channel lengths, Doppler shifts as well as noise powers. It is worth noting that, the close-to-ideal performance of the proposed algorithms is achieved with none of the above information. / published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy