Comparison of Exact and Approximate Multi-User Detection for GSM

Lili Nie

AbstractIn today s Group Special Mobile (GSM) system, interference is one of the main constraints in increasing cellular capacity. Multi-User Detection (MUD) is a kind of Interference Cancellation (IC) technique, which can be combined with other IC methods, such as antenna diversity, whitening. This dissertation investigates exact and approximate MUD GSM receivers. It is shown that exact MUD solution provides a big Bit Error Rate (BER) gain compared to conventional receivers. However, it has exponential complexity, making it infeasible to implement it on the limited Mobile Station (MS). In this thesis, the approximation to the exact solution is based on Mean Field theory. Two suboptimum algorithms: Fully Factorized Mean Field (FFMF) receiver and Structured Mean Field (SMF) receiver are implemented and evaluated. FFMF has very low complexity, comparable to that of the Linear Minimum Mean Squared Error (LMMSE) receiver, but much better BER performance for interference dominated scenarios. The SMF receiver gives faster convergence speed. However, for one Cochannel Interference (CCI), its performance is only close to that of FFMF solution in most of the tested CIR range and better than that of FFMF receiver at low CIR values.

Besides, topics such as digital phase modulation, GSM basics, the multi-path fading channel and conventional GSM receivers are also studied.
KeywordsAdjacent Channel Interference (ACI), Cochannel Interference (CCI), Fully Factorized Mean Field (FFMF), Group Sp’cial Mobile (GSM), Interference Cancellation (IC), Inter-Symbol Interference (ISI), Linear Minimum Mean Squared Error (LMMSE)
TypeMaster's thesis [Industrial collaboration]
Year2005
PublisherInformatics and Mathematical Modelling, Technical University of Denmark, DTU
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
SeriesIMM-Thesis-2005-24
NoteSupervised by associate professor Ole Winther
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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