As an advanced nonorthogonal multiple access (NOMA) technique, the low density signature (LDS) has never been used in filter bank multicarrier (FBMC) systems. In this paper, we model a low density weight matrix (LDWM)...
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As an advanced nonorthogonal multiple access (NOMA) technique, the low density signature (LDS) has never been used in filter bank multicarrier (FBMC) systems. In this paper, we model a low density weight matrix (LDWM) to utilize the intrinsic interference in FBMC systems when single-tap equalization is employed, and propose a LDS-FBMC scheme which applies LDS to FBMC signals. In addition, a joint sparse graph (JSG) for FBMC named JSG-FBMC is proposed to combine single graphs of LDS, LDWM, and low density parity-check (LDPC) codes which respectively represent techniques of NOMA, multicarrier modulation, and channel coding. By employing the message passing algorithm, a joint receiver performing detection and decoding simultaneously on the joint sparse graph is designed. Extrinsic information transfer charts and construction guidelines of the joint sparse graph are studied. Simulations show the superiority of JSG-FBMC to state-of-the-art techniques such as OFDM, FBMC, LDS-OFDM, LDS-FBMC, and turbostructured LDS-FBMC.
In order to improve the spectral efficiency and reliability of communication systems, both the multiple-input multiple-output (MIMO) technique and non-binary low-density parity-check (NB-LDPC) codes are considered as ...
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ISBN:
(纸本)9781538618233
In order to improve the spectral efficiency and reliability of communication systems, both the multiple-input multiple-output (MIMO) technique and non-binary low-density parity-check (NB-LDPC) codes are considered as powerful tools to meet the requirements. In order to further optimize the MIMO NB-LDPC systems and adapt to massive MIMO scenarios, a new joint detection and decoding (JDD) method of massive MIMO with NB-LDPC codes, called layered graph-merged detection and decoding (GMDD), is proposed. In this paper, factor graphs associated to symbol-based belief propagation (BP) MIMO detection and NB-LDPC decoding are merged into a single graph. For massive MIMO condition, a MIMO detection graph is attached to several NB-LDPC decoding graphs, which makes the methods unique to existed ones. Compared with separated detection and decoding (SDD), for BER less than 5 x 10(-2) the proposed method can achieve nearly 7 dB gain with same or even fewer iterations. Corresponding hardware architecture and complexity analysis are also given in this paper.
In order to improve the spectral efficiency and reliability of communication systems, both the multiple-input multiple-output (MIMO) technique and non-binary low-density parity-check (NB-LDPC) codes are considered as ...
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In order to improve the spectral efficiency and reliability of communication systems, both the multiple-input multiple-output (MIMO) technique and non-binary low-density parity-check (NB-LDPC) codes are considered as powerful tools to meet the requirements. In order to further optimize the MIMO NB-LDPC systems and adapt to massive MIMO scenarios, a new joint detection and decoding (JDD) method of massive MIMO with NB-LDPC codes, called layered graph-merged detection and decoding (GMDD), is proposed. In this paper, factor graphs associated to symbol-based belief propagation (BP) MIMO detection and NB-LDPC decoding are merged into a single graph. For massive MIMO condition, a MIMO detection graph is attached to several NB-LDPC decoding graphs, which makes the methods unique to existed ones. Compared with separated detection and decoding (SDD), for BER less than × 0~(-2) the proposed method can achieve nearly dB gain with same or even fewer iterations. Corresponding hardware architecture and complexity analysis are also given in this paper.
We consider a receiver design for a Hybrid Automatic-Repeat-reQuest (HARQ) system protected by polar codes against transmission errors. This integrated detection-decoding receiver targets a packet link for which chann...
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We consider a receiver design for a Hybrid Automatic-Repeat-reQuest (HARQ) system protected by polar codes against transmission errors. This integrated detection-decoding receiver targets a packet link for which channel state information during HARQ retransmission is unavailable at the receiver. Under such channel uncertainties, we propose a second-order cone programming (SOCP) approach for diversity combining without resorting to decision-directed channel estimation prone to error propagation. We formulate an integrated SOCP receiver by jointly exploiting the constraints from diversity channel models, subspace separation, and forward error correction codes. Unlike traditional turbo receiver algorithms that require iterative exchange of soft information between detector and decoder, our proposed SOCP receiver solves as a single-integrated convex optimization problem. This formulation is also versatile and extendable to a plurality of practical scenarios. We further investigate the means for enhancing the receiver performance and the HARQ throughput. Numerical results demonstrate the substantial performance benefits of the proposed joint SOCP receiver.
Soft iterative detection and decoding techniques have been shown to achieve near-capacity performance in multiple antenna systems. In most cases, obtaining optimal soft information in a joint detection and decoding al...
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Soft iterative detection and decoding techniques have been shown to achieve near-capacity performance in multiple antenna systems. In most cases, obtaining optimal soft information in a joint detection and decoding algorithm by marginalizing over the entire observation space is prohibitively complex. In this paper, an improved scheme adaptable to various list-type detectors providing superior performance is proposed.
Soft iterative detection and decoding techniques have been shown to be able to achieve near-capacity performance in multiple-antenna systems. To obtain the optimal soft information by marginalizing over the entire obs...
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Soft iterative detection and decoding techniques have been shown to be able to achieve near-capacity performance in multiple-antenna systems. To obtain the optimal soft information by marginalizing over the entire observation space is intractable: one must resort to suboptimal methods to implement such receivers. Although list-type detectors such as those founded upon the sphere decoding algorithm provide outstanding error performance, issues such as the optimal initial sphere radius, optimal radius update strategy, and their highly variable computational complexity are still unresolved. In this paper, a new detection scheme is proposed addressing the above issues. Our simulation results show that by sacrificing less than 2 dB at error rate of 10(-5), we are able to gain up to 38.6% complexity reduction as well as a detector structure that is more suitable for practical system implementation.
The joint Viterbi detector/decoder (JVDD) is a recently proposed receiver scheme that does both detection and decoding on a single trellis rather than doing detection on a trellis and decoding on factor graph as in th...
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ISBN:
(纸本)9781479969296
The joint Viterbi detector/decoder (JVDD) is a recently proposed receiver scheme that does both detection and decoding on a single trellis rather than doing detection on a trellis and decoding on factor graph as in the conventional iterative detector today. The JVDD attempts to return minimum metric legal codeword (MMLC) that is the optimum decision over an AWGN/ISI channel. However in so doing, the computational complexity quickly becomes untenable. In order to manage the computational complexity, GDLD and GDPD codes have been proposed that specifically target the complexity issue of the JVDD. In this work we perform a more in-depth study on several of the codes' parameters on both the performance and the complexity of the JVDD.
Soft iterative detection and decoding techniques have been shown to be able to achieve near-capacity performance in multiple antenna systems. To obtain the optimal soft information by marginalizing over the entire obs...
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ISBN:
(纸本)9781424416424
Soft iterative detection and decoding techniques have been shown to be able to achieve near-capacity performance in multiple antenna systems. To obtain the optimal soft information by marginalizing over the entire observation space is intractable;and it is not clear what the best way to obtain the suboptimal soft information is. In this talk, an improved scheme is proposed which can be adapted to various list-type detectors and provide superior performance.
joint processing algorithm of detection and decoding for linear block coded multiple-input multiple-output (MIMO) system is investigated in this paper. Iterative detection and decoding (IDD) algorithm, which is consid...
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ISBN:
(纸本)9781424483273
joint processing algorithm of detection and decoding for linear block coded multiple-input multiple-output (MIMO) system is investigated in this paper. Iterative detection and decoding (IDD) algorithm, which is considered as the optimal approach for convolutional coded MIMO system, yet is not processing efficient enough for linear block coded MIMO system under given computational complexity. In this case, although list-type detection and list-type decoding are separately used to generate a list of candidates (hard information), only the associated soft information is calculated and passed. Motivated by the joint ML principle and list-type process, we propose a much simpler and more efficient method, named list detection and decoding (LDD) algorithm. LDD exploits the common nature of list-type process in IDD to generate a list of candidates (hard information) for decoding-and-decision instead of iterative processing in IDD. We show that LDD can achieve near-optimal performance while enabling a performance-complexity tradeoff with different list size. Furthermore, the fixed complexity is suitable for hardware implementation. Simulation results show that LDD outperforms IDD while having a lower decoding complexity.
Optimal receivers recovering signals transmitted across noisy communication channels employ a maximum-likelihood (ML) criterion to minimize the probability of error. The problem of finding the most likely transmitted ...
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Optimal receivers recovering signals transmitted across noisy communication channels employ a maximum-likelihood (ML) criterion to minimize the probability of error. The problem of finding the most likely transmitted symbol is often equivalent to finding the closest lattice point to a given point and is known to be NP-hard. In systems that employ error-correcting coding for data protection, the symbol space forms a sparse lattice, where the sparsity structure is determined by the code. In such systems, NIL data recovery may be geometrically interpreted as a search for the closest point in the sparse lattice. In this paper, motivated by the idea of the "sphere decoding" algorithm of Fincke and Pohst, we propose an algorithm that finds the closest point in the sparse lattice to the given vector. This given vector is not arbitrary, but rather is an unknown sparse lattice point that has been perturbed by an additive noise vector whose statistical properties are known. The complexity of the proposed algorithm is thus a random variable. We study its expected value, averaged over the noise and over the lattice. For binary linear block codes, we find the expected complexity in closed form. Simulation results indicate significant performance gains over systems employing separate detection and decoding, yet are obtained at a complexity that is practically feasible over a wide range of system parameters.
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