Tree detection techniques are often used to reduce the complexity of a posteriori probability (APP) detection in multiantenna wireless communication systems. In this paper, we introduce an efficient soft-inputsoft-ou...
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Tree detection techniques are often used to reduce the complexity of a posteriori probability (APP) detection in multiantenna wireless communication systems. In this paper, we introduce an efficient soft-inputsoft-output tree detection algorithm that employs a new type of look-ahead path metric in the process of branch pruning (or sorting). While conventional path metrics depend only on symbols on a visited path, the new path metric accounts for unvisited parts of the tree in advance through an unconstrained linear estimator and adds a bias term that reflects the contribution of as-yet undecided symbols. By applying the linear estimate-based look-ahead path metric to an M-algorithm that selects the best M paths for each level of the tree, we develop a new soft-inputsoft-output tree detector, called an improved soft-inputsoft-output M-algorithm (ISS-MA). Based on an analysis of the probability of correct path loss, we show that the improved path metric offers substantial performance gain over the conventional path metric. We also demonstrate through simulations that the proposed ISS-MA can be a promising candidate for soft-input soft-output detection in high-dimensional systems.
In this paper,we propose a minimum mean square error(MMSE)sorted QR decomposition(SQRD)based soft-inputsoft-output(SISO)detection algorithm for coded multiple-input multiple-output orthogonal frequency division multi...
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In this paper,we propose a minimum mean square error(MMSE)sorted QR decomposition(SQRD)based soft-inputsoft-output(SISO)detection algorithm for coded multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)*** proposed detection is derived from the SISO MMSE detection,which is a popular detection strategy for iterative *** each transmitted symbol in the proposed detection,a soft successive interference cancellation(SIC)is performed based on a posteriori probabilities of past detected *** results show that the proposed detection,while needing less computational efforts,achieves significant performance gain compared with the SISO MMSE detection.
In this paper, we present a novel pragmatic approach, referred to as detection by multiple trellises, to perform trellis-based detection over realistic channels. More precisely, we consider channels with unknown param...
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In this paper, we present a novel pragmatic approach, referred to as detection by multiple trellises, to perform trellis-based detection over realistic channels. More precisely, we consider channels with unknown parameters and apply the concept of detection by multiple trellises to forward-backward (FB) algorithms. The key idea of our approach consists, first, of properly quantizing the channel parameters and, then, considering replication of coherent FB algorithms operating on parallel trellises, one per hypothetical quantized value. In order to make the receiver robust against a possibly time-varying channel parameters, the proposed soft-output algorithms perform a proper "manipulation" of the forward and backward metrics computed by the parallel FB algorithms at regularly spaced trellis steps. We consider two significant examples of application: detection over (i) phase-uncertain channels and (ii) fading channels. The performance of the proposed algorithms is investigated considering differentially encoded (DE) quaternary phase shift keying (QPSK) and iterative detection schemes based on low-density parity-check (LDPC) codes. Besides having a low complexity, the proposed soft-output algorithms turn out to be robust, flexible, blind, in the sense that no knowledge of the channel parameter statistics is required, and highly parallelizable, as it is desirable in high-throughput future wireless communication systems.
soft-inputsoft-output (SISO) detector for the iterative detection and decoding of coded spatial modulation (SM) signals needs to compute the extrinsic bit log-likelihood ratio (LLR) associated with each detected bit....
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soft-inputsoft-output (SISO) detector for the iterative detection and decoding of coded spatial modulation (SM) signals needs to compute the extrinsic bit log-likelihood ratio (LLR) associated with each detected bit. The maximum logarithmic maximum a posteriori (max-log-MAP) detector is known to perform closely to the optimal logarithmic maximum a posteriori (log-MAP) detector with much lower computational complexity. In this paper, we first apply several mathematical tricks to derive a new and low-complexity algorithm for the max-log-MAP detector. The proposed algorithm features a new tree pruning technique to visit few lattice points and a smart list administration technique to retain a max-log-MAP detector. By following the proposed algorithm, we then design a hardware architecture for SISO detection of coded SM signals over the scenario of 64-QAM symbols, 8 transmit antennas, and 4 receive antennas. Under the TSMC 40 nm CMOS technology, the VLSI implementation results reveal that our architecture requires 46.4K gates and leads to detection throughput 450 Mbps, while operating at clock frequency 400 MHz and consuming power 46 mW. Being the first hardware architecture for SISO detection of coded SM signals, our proposed low-cost max-log-MAP detector is very attractive.
An improved soft-inputsoft-output (SISO) minimum mean-squared error (MMSE) detection method is proposed for joint coding and precoding OFDM systems under imperfect channel estimation Compared with the traditional mis...
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An improved soft-inputsoft-output (SISO) minimum mean-squared error (MMSE) detection method is proposed for joint coding and precoding OFDM systems under imperfect channel estimation Compared with the traditional mismatched detection which uses the channel estimate as Its exact value the signal model of the proposed detector is more accurate and the influence of channel estimation error (CEE) can be effectively mitigated Simulations indicate that the proposed scheme can improve the bit error rate (BER) performance with fewer pilot symbols
By exchanging soft information between the detector and the channel decoder, an iterative receiver can significantly improve the performance compared to the non-iterative receiver. In this paper, a low complexity soft...
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ISBN:
(纸本)9781612846835;9781612846828
By exchanging soft information between the detector and the channel decoder, an iterative receiver can significantly improve the performance compared to the non-iterative receiver. In this paper, a low complexity soft-inputsoft-output (SISO) fixed-complexity sphere decoding (FSD) algorithm is proposed for iterative MIMO receiver. The algorithm introduces a simplified parallel candidate adding (SPCA) scheme to provide more accurate soft information. Furthermore, by employing our proposed improved hybrid-enumeration method, the SISO-FSD can reduce the computational complexity substantially. Simulation results show that the algorithm can achieve a near Max-Log optimal maximum a posteriori (MAP) detection performance under flat fading Rayleigh channels. Compared to the SISO K-Best algorithm (K=50), the proposed algorithm saves about 60% computations and provides almost the same performance for detecting 4 x 4 64-QAM MIMO signal, making it a promising scheme for real-life hardware implementation.
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