In this contribution, we present low-complexity detection algorithms in large-scale MIMO systems where they achieve significantly better bit error rate (BER) performance than known heuristic algorithms in large-scale ...
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In this contribution, we present low-complexity detection algorithms in large-scale MIMO systems where they achieve significantly better bit error rate (BER) performance than known heuristic algorithms in large-scale MIMO literature, such as local ascent search (LAS) and reactive tabu search (RTS) algorithms, especially at higher-order modulations. The proposed techniques are developed from the conventional quadratic programming (QP) detector. The first one is based on performing two stages of a QP detector with a novel combination of both interference cancellation and shadow area constraints of the constellation. The second one is based on the branch and bound search tree algorithm. The efficacy of the proposed algorithms is investigated at various QAM modulations. Computer simulations show that the proposed algorithms outperform LAS and RTS algorithms in both uncoded and turbo coded BER performance, especially at higher QAM levels, with no significant change in complexity as the modulation level increases. Also, an extension of the QP detector for iterative detection and decoding is developed for the case of QPSK using a low complexity approach.
We propose a new practical multiuser decoding scheme based on physical-layer network coding (PNC) for a K-user fading multiple-access channel. In the scheme, the users encode their messages with a same practical chann...
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We propose a new practical multiuser decoding scheme based on physical-layer network coding (PNC) for a K-user fading multiple-access channel. In the scheme, the users encode their messages with a same practical channel code, and the coded and modulated signals are transmitted simultaneously. The receiver first reconstructs K linear functions of the K users' messages based on PNC. Then all messages are recovered by solving the K linear equations. Compared to the well-known iterative detection and decoding schemes, we show by numerical results that the proposed scheme achieves near interference-free bound performance without utilizing receiver iterations.
For iterative detection and decoding (IDD) in multiple-input multiple-output systems, the maximum a posteriori probability (MAP) detector would be ideal in terms of the performance. However, due to its high computatio...
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For iterative detection and decoding (IDD) in multiple-input multiple-output systems, the maximum a posteriori probability (MAP) detector would be ideal in terms of the performance. However, due to its high computational complexity, various suboptimal low-complexity approximate MAP detectors have been studied. In this study, a lattice reduction (LR)-based detector is considered for a near-optimal performance for IDD. The authors improve further the performance by employing a partial bit-wise minimum mean square error (MMSE) approach with randomised sampling, which has a lower complexity than that of the full bit-wise MMSE method. Moreover, the list of candidate vectors obtained by randomised sampling is extended using a MAP-aided integer perturbation algorithm for a better performance with low additional complexity. Through simulation results, it is shown that a near-optimal performance can be obtained which is better than that of the LR-based randomised successive interference cancellation and the full bit-wise MMSE methods.
Recently, the iterative detection and decoding technique based on parallel interference cancellation with minimum mean square error (PIC-MMSE) has received considerable attention. To improve the performance, the detec...
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ISBN:
(纸本)9781509034239
Recently, the iterative detection and decoding technique based on parallel interference cancellation with minimum mean square error (PIC-MMSE) has received considerable attention. To improve the performance, the detector usually adopts a self-iteration which iteratively estimate the soft bit information (SBI). This paper proposes two main idea to improve the performance as well as to reduce the complexity of the PIC-MMSE based MIMO detector. In order to reduce the complexity, we map PIC-MMSE filtered symbol to a specific region so that the detector does not require any search process to find the minima. In addition, we propose an optimization technique to increase the reliability of the soft symbols. Simulation results show that the complexity of the proposed method is reduced to linear-order without performance degradation, and the proposed optimization method can efficiently improve the performance with reasonable complexity.
The iterative detection and decoding technique based on parallel interference cancellation (PIC) has received considerable attention. To reduce the computational complexity in the estimation of soft bit information, t...
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ISBN:
(纸本)9781509021437
The iterative detection and decoding technique based on parallel interference cancellation (PIC) has received considerable attention. To reduce the computational complexity in the estimation of soft bit information, this paper proposes to apply the symbol mapping technique to the PIC-MMSE based MIMO detector. In each layer, the PIC-MMSE filtered symbol is normalized and mapped to a specific range of the first quadrant in a recursive manner. In addition, we present an efficient method to calculate the soft symbols in the PIC process. Simulation results show that the complexity of the proposed method with symbol mapping is reduced to linear-order without performance degradation, and the complexity of the soft symbols computation is greatly reduced as well.
Dynamic grain state estimation (DGSE) algorithms for 2-D magnetic recording (TDMR) employ probabilistic message-passing algorithms that jointly estimate magnetic grain configurations and coded data bits, in order to i...
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Dynamic grain state estimation (DGSE) algorithms for 2-D magnetic recording (TDMR) employ probabilistic message-passing algorithms that jointly estimate magnetic grain configurations and coded data bits, in order to iteratively assist channel decoding. At high densities (e.g., between 1 and 3 magnetic grains per coded bit), occasionally, a bit will not be written on any grain, and hence will effectively be overwritten (or erased) by bits on surrounding grains. DGSE enables the detection of overwritten bits so that their log-likelihood ratios are assigned small magnitudes, effectively making them erasures, which are easily filled in by linear channel codes. Past papers employing Bahl-Cocke-Jelinek-Raviv-based detectors on a simple four-rectangular-grain model have shown that the DGSE is surprisingly resilient to mismatch between the detector's assumed grain model and the actual model generating the data. This paper presents a novel DGSE-TDMR detector based on the generalized belief propagation (GBP) algorithm. The new detector employs a random discretized-nuclei Voronoi grain model. Simulation results show that the GBP-based TDMR turbodetector accurately detects the overwritten bits and that it achieves low decoded bit error rates at densities as high as 0.4966 user bits per grain.
We consider soft-output detection of a binary continuous phase modulation (CPM) generated through a low-cost transmitter, thus characterized by a significant modulation index uncertainty, and sent over a channel affec...
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We consider soft-output detection of a binary continuous phase modulation (CPM) generated through a low-cost transmitter, thus characterized by a significant modulation index uncertainty, and sent over a channel affected by phase noise. The proposed detector is designed by adopting a simplified representation of a binary CPM signal with the principal component of its Laurent decomposition and is obtained by using the framework based on factor graphs and the sum-product algorithm. It does not require an explicit estimation of the modulation index nor of the channel phase and is very robust to large uncertainties of the nominal value of the modulation index. Being soft-output in nature, this detector can be employed for iterativedetection/decoding of practical coded schemes based on a serial concatenation, possibly through a pseudo-random interleaver, of an outer encoder and a CPM modulation format.
We present novel soft-input soft-output (SISO) multiple-input multiple-output (MIMO) detectors based on the Chase detection principle [1] in the context of iterative detection and decoding (IDD). The proposed detector...
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We present novel soft-input soft-output (SISO) multiple-input multiple-output (MIMO) detectors based on the Chase detection principle [1] in the context of iterative detection and decoding (IDD). The proposed detector complexity is linear in the signal modulation constellation size and the number of spatial streams. Two variants of the SISO detector are developed, referred to as SISO block decision-feedback Chase detector (B-Chase) and SISO linear Chase detector (L-Chase). An efficient method is presented that uses the decoder output to modulate the signal constellation decision boundaries inside the detector leading to the SISO detector architecture. The performance of these detectors significantly improves with just a few number of IDD iterations. The effect of transmit and receive antenna correlation is simulated. For the high-correlation case, the superiority of SISO B-Chase over the SISO L-Chase is demonstrated.
The number of transmit and receive antennas in multi-input multi-output (MIMO) systems is increasing rapidly to enhance the throughput and reliability of next-generation wireless systems. Benefits of large size MIMO s...
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The number of transmit and receive antennas in multi-input multi-output (MIMO) systems is increasing rapidly to enhance the throughput and reliability of next-generation wireless systems. Benefits of large size MIMO systems, however, can be realized only when the quality of estimated channels is ensured at the transmitter and receiver side alike. In this paper, we introduce a new decision-directed channel estimation technique dealing with pilot shortage in the MIMO-OFDM systems. The proposed channel estimator uses soft symbol decisions obtained by iterative detection and decoding (IDD) scheme to enhance the quality of channel estimate. Using the soft information from the decoders, the proposed channel estimator selects reliable data tones, subtracts interstream interferences, and performs re-estimation of the channels. We analyze the optimal data tone selection criterion, which accounts for the reliability of symbol decisions and correlation of channels between the data tones and pilot tones. From numerical simulations, we show that the proposed channel estimator achieves considerable improvement in system performance over the conventional channel estimators in realistic MIMO-OFDM scenarios.
In this paper, we consider the problem of carrier phase estimation for an M-ary phase shift keying modulated frequency-hopping system affected by strong phase noise. An improved joint detection and decoding algorithm ...
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ISBN:
(纸本)9781467376877
In this paper, we consider the problem of carrier phase estimation for an M-ary phase shift keying modulated frequency-hopping system affected by strong phase noise. An improved joint detection and decoding algorithm is presented based on the factor graph and sum-product algorithm framework. The messages on the factor graph are approximated into the sums of multiple Tikhonov distributions. Numerical results demonstrate that the proposed algorithm yields a very good performance in low signal-to-noise ratio while holding a relative low computational complexity compared with the existing ones.
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