In the advent of very high data rates of the upcoming 3G long-term evolution telecommunication systems, there is a crucial need for efficient and flexible turbo decoder implementations. In this study, a max-log-map tu...
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In the advent of very high data rates of the upcoming 3G long-term evolution telecommunication systems, there is a crucial need for efficient and flexible turbo decoder implementations. In this study, a max-log-map turbo decoder is implemented as an application-specific instruction-set processor. The processor is accompanied with accelerating computing units, which can be controlled in detail. With a novel memory interface, the dual-port memory for extrinsic information is avoided. As a result, processing one trellis stage with max-log-map algorithm takes only 1.02 clock cycles on average, which is comparable to pure hardware decoders. With six turbo iterations and 277MHz clock frequency 22.7Mbps decoding speed is achieved on 130nm technology. Copyright (C) 2008 Perttu Salmela et al.
This paper proposes a modified iterative scheme(MI) for the data transmitted by more efficient way over inter-symbol interference(ISI) channels,then compares and analyzes the performance for three equalization algorit...
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This paper proposes a modified iterative scheme(MI) for the data transmitted by more efficient way over inter-symbol interference(ISI) channels,then compares and analyzes the performance for three equalization algorithms in MI: improved max-log-map(I-MLM) algorithm,maximum a posteriori(map) algorithm and linear minimum meansquared error(LMMSE) *** we analyze and compare these three algorithms in traditional turbo equalization(TE) ***,according to the good understanding of the aforementioned algorithms and the novel iterative(NI) scheme which combines parallel with serial concatenation turbo-like scheme,we propose the MI scheme and compare the performance of three algorithms in MI, named as MI-I-MLM,MI-map and MI-LMMSE *** analytical and simulation results demonstrate that the performance of MI-I-MLM is very close to MI-map and much better than MI-LMMSE while its computational complexity is much lower.
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