This paper presents a space-time turbo (iterative) equalization method for trellis-coded modulation (TCM) signals over broadband wireless channels. For fixed wireless systems operating at high data rates, the multipat...
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This paper presents a space-time turbo (iterative) equalization method for trellis-coded modulation (TCM) signals over broadband wireless channels. For fixed wireless systems operating at high data rates, the multipath delay spread becomes large, making it impossible to apply trellis-based equalization methods. The equalizer proposed here consists of a broadband beamformer which processes antenna array measurements to shorten the observed channel impulse response, followed by a conventional scalar turbo equalizer. Since the applicability of trellis-based equalizers is limited to additive white noise channels, the beamformer is required to preserve the whiteness of the noise at its output. This constraint is equivalent to requiring that the finite-impulse response (FIR) beamforming filters must have a power complementarity property. The power complementarity property imposes nonnegative definite quadratic constraints on the beamforming filters, so the beamformer design is expressed as a constrained quadratic optimization problem. The composite channel impulse response at the beamformer output is shortened significantly, making it possible to use a turbo equalizer for the joint equalization and decoding of trellis modulated signals. The proposed receiver structure is simulated for two-dimensional TCM signals such as 8-PSK and 16-QAM and the results indicate that the use of antenna arrays with only two or three elements allows a large decrease in the channel signal-to-noise ratio needed to achieve a 10(-4) bit-error rate.
We introduce a group algebra formulation for bit-optimal decoding of binary block codes. We use this new framework to give a simple algebraic proof that Pearl's and Gallager's belief propagation decoding algor...
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We introduce a group algebra formulation for bit-optimal decoding of binary block codes. We use this new framework to give a simple algebraic proof that Pearl's and Gallager's belief propagation decoding algorithms are bit-optimal when the Tanner graph of the code is a tree. We believe that these derivations of known results give new insights into the issues of decoding on graphs from the algebraic coding theorist's point of view. (C) 2003 Elsevier Science B.V. All rights reserved.
We introduce a group algebra formulation for bit-optimal decoding of binary block codes. We use this new framework to give a simple algebraic proof that Pearl's and Gallager's belief propagation decoding algor...
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We introduce a group algebra formulation for bit-optimal decoding of binary block codes. We use this new framework to give a simple algebraic proof that Pearl's and Gallager's belief propagation decoding algorithms are bit-optimal when the Tanner graph of the code is a tree. We believe that these derivations of known results give new insights into the issues of decoding on graphs from the algebraic coding theorist's point of view. (C) 2003 Elsevier Science B.V. All rights reserved.
A soft-output data-detection scheme optimized for data-dependent media-noise recording channels is proposed that uses signal-dependent correlation-sensitive (SDCS) metric estimation for post-processing decoding. This ...
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ISBN:
(纸本)0780379748
A soft-output data-detection scheme optimized for data-dependent media-noise recording channels is proposed that uses signal-dependent correlation-sensitive (SDCS) metric estimation for post-processing decoding. This media-noise soft-output (MNS) decoding scheme achieves sub-optimal maximum-likelihood (ML) sequence detection in a non-stationary media-noise channel, while still using traditional Viterbi detection. Because it drastically reduces SDCS metric computation by focusing on only specified dominant error-events in the ML detector, it is less complex than other sub-optimal detection schemes. Moreover, its one-shot post-processing scheme enables the use of a simple lookup-table architecture suitable for highspeed circuit implementation. Simulation showed that the MNS decoding scheme in conjunction with a conventional (MEPRML)-P-2 system provides an excellent tradeoff between data-detection performance and computation complexity for a media-noise-dominant high-density recording channel.
In this paper, we present a method for soft-in/soft-out sequential decoding of recursive systematic convolutional codes, The proposed decoder, the twin stack decoder, is an extension of the well-known ZJ stack decoder...
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In this paper, we present a method for soft-in/soft-out sequential decoding of recursive systematic convolutional codes, The proposed decoder, the twin stack decoder, is an extension of the well-known ZJ stack decoder, and it uses two stacks. The use of the two stacks lends itself to the generation of softoutputs, and the decoder is easily incorporated into the iterative "turbo" configuration. Under thresholded decoding, it is observed that the decoder is capable of achieving near-maximum a posteriori bit-error rate performance at moderate to high signal-to-noise ratios (SNRs), Also, in the iterative (turbo) configuration, at moderate SNRs (above 2.0 dB), the performance of the proposed decoder is within 1.5 dB of the BCJR algorithm for a 16-state, R = 1/3, recursive code, but this difference narrows progressively at higher SNRs, The complexity of the decoder asymptotically decreases (with SNR) as 1/(number of states), providing a good tradeoff between computational burden and performance. The proposed decoder is also within 1.0 dB of other well-known suboptimal soft-out decoding techniques.
After the rediscovery of low-density parity-check (LDPC) codes, their decoding algorithms have been studied by mathematical tools pertinent more to computer science than the conventional algebraic coding theory. We pr...
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A new type of recursive algorithm for decoding turbo codes is proposed, based on a symbol-by-symbol maximum a posteriori probability algorithm. It requires only a forward recursion and has lower computational complexi...
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A new type of recursive algorithm for decoding turbo codes is proposed, based on a symbol-by-symbol maximum a posteriori probability algorithm. It requires only a forward recursion and has lower computational complexity. The number of variables to be stored increases linearly with the decoding delay. For iterative decoding, the extrinsic information given by the new algorithm is also described. This algorithm can be used in continuous decoding for both recursive and non-recursive encoder.
A soft-output Viterbi algorithm (SOVA) that can be used on trellis-coded modulation (TCM), rate-kin convolutional codes, and intersymbol interference (ISI) channels is proposed. The algorithm utilizes the postdetector...
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A soft-output Viterbi algorithm (SOVA) that can be used on trellis-coded modulation (TCM), rate-kin convolutional codes, and intersymbol interference (ISI) channels is proposed. The algorithm utilizes the postdetector architecture proposed by Berrou et nl, [6] to achieve low computational complexity. By starting with Battail's [4] generalized revision algorithm and rereferencing the "relative values" to the surviving path to each state, substantial simplifications are made possible. By comparing the revision operations dictated by the simplified revision equation for a rate-1/n convolutional code to the operations mandated by the rate-1/n postdetector algorithm presented by Berrou ef al,, it is possible to deduce the additional modifications necessary to produce a rate-k/n postdetector algorithm. Computer simulations suggest that the derived rate-k/n algorithm produces reasonably good a posteriori input probability estimates for rate-k/n convolutional codes and trellis codes. The algorithm may also be used for soft-output Viterbi equalization (SOVE) provided that the channel impairments are not too severe.
A variant of the best-path (BP) algorithm that can be used for deducing a posteriori symbol probabilities for input sequences of unlimited length is proposed. Decoders using the proposed algorithm have fixed memory re...
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A variant of the best-path (BP) algorithm that can be used for deducing a posteriori symbol probabilities for input sequences of unlimited length is proposed. Decoders using the proposed algorithm have fixed memory requirements and fixed decoding delays regardless of the length of the transmitted sequence. This is made possible by utilizing the Viterbi algorithm's ability to self-initialize itself and by segmenting the decoding process.
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