Type of Document Master's Thesis Author Mohammad, Maruf H. Author's Email Address firstname.lastname@example.org URN etd-12122002-150624 Title Blind Acquisition of Short Burst with Per-Survivor Processing (PSP) Degree Master of Science Department Electrical and Computer Engineering Advisory Committee
Advisor Name Title Tranter, William H. Committee Chair Reed, Jeffrey Hugh Committee Member Woerner, Brain D. Committee Member Keywords
- Maximum likelihood (ML) detection
- Blind channel estimation
- Per-Survivor Processing (PSP)
- Viterbi Algorithm
Date of Defense 2002-11-26 Availability restricted AbstractBlind Acquisition of Short Burst with Per-Survivor Processing (PSP)
This thesis investigates the use of Maximum Likelihood Sequence Estimation (MLSE) in the presence of unknown channel parameters. MLSE is a fundamental problem that is closely related to many modern research areas like Space-Time Coding, Overloaded Array Processing and Multi-User Detection. Per-Survivor Processing (PSP) is a technique for approximating MLSE for unknown channels by embedding channel estimation into the structure of the Viterbi Algorithm (VA). In the case of successful acquisition, the convergence rate of PSP is comparable to that of the pilot-aided RLS algorithm. However, the performance of PSP degrades when certain sequences are transmitted.
In this thesis, the blind acquisition characteristics of PSP are discussed. The problematic sequences for any joint ML data and channel estimator are discussed from an analytic perspective. Based on the theory of indistinguishable sequences, modifications to conventional PSP are suggested that improve its acquisition performance significantly. The effect of tree search and list-based algorithms on PSP is also discussed. Proposed improvement techniques are compared for different channels. For higher order channels, complexity issues dominate the choice of algorithms, so PSP with state reduction techniques is considered. Typical misacquisition conditions, transients, and initialization issues are reported.
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