This article investigates the dynamic frequency selection problem in broadband anti-jamming communications through distributed sensing and deep reinforcement learning (DRL). In broadband anti-jamming scenarios, a single agent is often impractical for sensing the whole range of the band due to various restrictions on implementation. In this article, a novel distributed sensing architecture is proposed, where a number of distributed sensors are cooperated for sensing the whole band with each sensor only responsible for an individual sub-band. In this way, a fusion center should collect the sensing spectrum from each sub-band and the problem of spectrum compression is formulated under the framework of autoencoder. To cope with the problem of unavailable reward in distributed sensing, we propose a domain-adapted reward estimation method, with which the agent could work near perfectly under the framework of DRL. Simulation results show that the distributed sensing method could work effectively, and the compression ratio of the proposed autoencoder-based method could be 14 times higher than that of the direct quantization approach without any degradation on the final anti-jamming performance. In addition, the proposed DRL-based anti-jamming agent using reconstructed spectrum waterfall and estimated rewards achieves close-to-ideal performance in the environment with unknown jamming patterns.

In wireless communications, traditional malicious jamming attacks that employ a single jammer and focus on a single domain are becoming increasingly ineffective due to the rapid advancements in learning-based anti-jamming technology. To address these issues, this paper proposes a novel multi-domain cooperative jamming method for DRL-based anti-jamming frequency hopping (FH) communications. At the jamming side, intelligent multi-domain attacks could be effectively implemented by coordinating the adjustment of frequency and power parameters among multiple jammers to disrupt the legitimate user’s communication. In this context, the interaction between the legitimate user and multiple cooperative jammers is modeled as a One-Leader Multi-Follower (OLMF) Stackelberg game, where the efficient jamming resource allocation problem among the cooperative jammers is formulated as a potential game. Then, the existence of Nash equilibrium for the potential game is demonstrated, which further ensures the Stackelberg equilibrium for the OLMF Stackelberg game. Additionally, a hierarchical deep reinforcement learning (HDRL) method is introduced to approach the final equilibrium for the cooperative jammers with a high-dimensional action space. Simulation results demonstrate that when facing DRL-based anti-jamming agents, the proposed multi-domain cooperative jamming approach achieves a jamming success rate that is 35% higher than traditional jamming, 20% higher than reactive jamming, and 15% higher than DRL-based non-cooperative jamming. Even when the legitimate user employs the opponent modeling based dynamic best response anti-jamming strategy, our method still converges to the Stackelberg equilibrium and achieves a 10% jamming performance gain compared to the multi-tone intelligent jamming method and the non-cooperative jamming method.

Bit flipping can be used as a postprocessing technique to further improve the performance for successive cancellation list (SCL) decoding of polar codes. However, the number of bit-flipping trials could increase the decoding latency significantly, which is not welcome in practice. In this paper, we propose a low latency SCL bit flipping decoding scheme, which is restricted to just single round of post-processing. The use of multiple votes for a more accurate estimation of path survival probability is proposed to locate the first error event of SCL decoding. Simulations show the sound improvement compared to the existing SCL bit-flipping decoding methods.

Entropy minimization has been widely used in unsupervised domain adaptation (UDA). However, existing works reveal that the use of entropy-minimization-only may lead to collapsed trivial solutions for UDA. In this article, we try to seek possible close-to-ideal UDA solutions by focusing on some intuitive properties of the ideal domain adaptation solution. In particular, we propose to introduce diversity maximization for further regulating entropy minimization. In order to achieve the possible minimum target risk for UDA, we show that diversity maximization should be elaborately balanced with entropy minimization, the degree of which can be finely controlled with the use of deep embedded validation in an unsupervised manner. The proposed minimal-entropy diversity maximization (MEDM) can be directly implemented by stochastic gradient descent without the use of adversarial learning. Empirical evidence demonstrates that MEDM outperforms the state-of-the-art methods on four popular domain adaptation datasets.

Object detection often costs a considerable amount of computation to get satisfied performance, which is unfriendly to be deployed in edge devices. To address the trade-off be-tween computational cost and detection accuracy, this paper presents a dual path network, named DPNet, for efficient object detection with lightweight self-attention. In backbone, a single input/output lightweight self-attention module (LSAM) is designed to encode global interactions between different positions. LSAM is also extended into a multiple-inputs version in feature pyramid network (FPN), which is employed to capture cross-resolution dependencies in two paths. Extensive experiments on the COCO dataset demonstrate that our method achieves promising detection results. More specifically, DPNet obtains 29.0% AP on COCO test-dev, with only 1.14 GFLOPs and 2.27M model size for a 320 × 320 image.

Due to the powerful ability to encode image details and semantics, many lightweight dual-resolution networks have been proposed in recent years. However, most of them ignore the benefit of boundary information. This paper introduces a lightweight dual-resolution network, called DRBANet, aiming to refine semantic segmentation results with the aid of boundary information. DRBANet also adopts dual parallel architecture, including: high resolution branch (HRB) and low resolution branch (LRB). Specifically, HRB mainly consists of a set of Efficient Inverted Bottleneck Modules (EIBMs), which learn feature representations with larger receptive fields. LRB is composed of a series of EIBMs and an Extremely Lightweight Pyramid Pooling Module (ELPPM), where ELPPM is utilized to capture multi-scale context through hierarchical residual connections. Finally, a boundary supervision head is designed to capture object boundaries in HRB. Extensive experiments on Cityscapes and CamVid datasets demonstrate that our method achieves promising trade-off between segmentation accuracy and running efficiency.

Abstract We present an overview of quantum-safe cryptography (QSC) with a focus on post-quantum cryptography (PQC) and information-theoretic security. From a cryptographic point of view, lattice and code-based schemes are among the most promising PQC solutions. Both approaches are based on the hardness of decoding problems of linear codes with different metrics. From an information-theoretic point of view, lattices and linear codes can be constructed to achieve certain secrecy quantities for wiretap channels as is intrinsically classical- and quantum-safe. Historically, coding theory and cryptography are intimately connected since Shannon’s pioneering studies but have somehow diverged later. QSC offers an opportunity to rebuild the synergy of the two areas, hopefully leading to further development beyond the NIST PQC standardization process. In this paper, we provide a survey of lattice and code designs that are believed to be quantum-safe in the area of cryptography or coding theory. The interplay and similarities between the two areas are discussed. We also conclude our understandings and prospects of future research after NIST PQC standardisation.

Recently, boundary information has gained great attraction for semantic segmentation. This paper presents a novel encoder-decoder network, called BANet, for accurate semantic segmentation, where boundary information is employed as an additional assistance for producing more consistent segmentation outputs. BANet is composed of three components: the pre-trained backbone using dilated-ResNet101, semantic flow branch (SFB) and boundary flow branch (BFB) for semantic segmentation and boundary detection, respectively. More specifically, to delineate more accurate object shapes and boundaries, a global attention block (GAB) is designed in SFB as global guidance for high-level feature. On the other hand, BFB directly extracts features on boundaries, avoiding the unexpected interference from the non-boundary parts. Finally, we adopt a joint loss function to further optimize the segmentation results and boundary outputs synchronously. Moreover, compared with previous state-of-the-art methods, e.g., non-local block and ASPP module, our BFB leverages detection accuracy and computational efficiency in a lightweight fashion. To evaluate BANet, we have conducted extensive experiments on several semantic segmentation datasets: Cityscapes, PASCAL Context, and ADE20K. The experimental results show that, with the aid of boundary information, BANet is able to produce more consistent segmentation predictions with accurately delineated object shapes and boundaries, leading to the state-of-the-art performance on Cityscapes, and competitive results on PASCAL Context and ADE20K with respect to recent semantic segmentation networks.

For WiFi indoor localization, collecting the data to construct the fingerprint dataset is a time-consuming and laborious job. In this paper, we propose to alleviate the time-intensive data-labeling by generating artificial fingerprint using a single AC-GAN. Compared to the existing multi-GAN approaches, we show that the use of a single AC-GAN could achieve the improved accuracy in localization, which is validated by extensive experiments. We also discuss its potential limit.

Summary Capsules as well as dynamic routing between them are most recently proposed structures for deep neural networks. A capsule groups data into vectors or matrices as poses rather than conventional scalars to represent specific properties of target instance.Based on pose,a capsule should be attached to a probability (often denoted as activation) for its presence. The dynamic routing helps capsule network achieve more generalization capacity with fewer model parameters. However, the bottleneck, which prevents widespread applications of capsule, is the expense of computation during routing. To address this problem, we generalize existing routing methods within the framework of weighted kernel density estimation, proposing two fast routing methods with different optimization strategies. Our methods prompt the time efficiency of routing by nearly 40% with negligible performance degradation. By stacking a hybrid of convolutional layers and capsule layers, we construct a network architecture to handle inputs at a resolution of 64 × 64 pixels. The proposed models achieve a parallel performance with other leading methods in multiple benchmarks.

This paper introduces a lightweight convolutional neural network, called FDDWNet, for real-time accurate semantic segmentation. In contrast to recent advances of lightweight networks that prefer to utilize shallow structure, FDDWNet makes an effort to design more deeper network architecture, while maintains faster inference speed and higher segmentation accuracy. Our network uses factorized dilated depth-wise separable convolutions (FDDWC) to learn feature representations from different scale receptive fields with fewer model parameters. Additionally, FDDWNet has multiple branches of skipped connections to gather context cues from intermediate convolution layers. The experiments show that FDDWNet only has 0.8M model size, while achieves 60 FPS running speed on a single RTX 2080Ti GPU with a 1024 × 512 input image. The comprehensive experiments demonstrate that our model achieves state-of-the-art results in terms of available speed and accuracy trade-off on CityScapes and CamVid datasets.

For image semantic segmentation, a fully convolutional network is usually employed as the encoder to abstract visual features of the input image. A meticulously designed decoder is used to decoding the final feature map of the backbone. The output resolution of backbones which are designed for image classification task is too low to match segmentation task. Most existing methods for obtaining the final high-resolution feature map can not fully utilize the information of different layers of the backbone. To adequately extract the information of a single layer, the multi-scale context information of different layers, and the global information of backbone, we present a new attention-augmented module named Dense-attention Context Module (DCM), which is used to connect the common backbones and the other decoding heads. The experiments show the promising results of our method on Cityscapes dataset.

Learning part-based features with multiple branches has been proven as an effective way to deliver high performance person re-identification. Existing works mostly exploit extra constraints on different branches to ensure the diversity of extracted features, which may lead to the increased complexity in network architecture and the difficulty for training. In this letter, we propose a quite simple multi-branch structure consisting of a global branch as well as a part branch in an asymmetrical way. We empirically demonstrate that such simple architecture can provide surprisingly high performance without imposing any extra constraint. On top of this, we further prompt the performance with a lightweight implementation of attention module. Extensive experimental results prove that the proposed method, termed Asymmetrical Network (AsNet), outperforms state-of-the-art methods with obvious margin on standard benchmark datasets such as Market1501, DukeMTMC, CUHK03. We believe that AsNet can serve as a strong baseline for related research and the source code is publicly available at https://github.com/www0wwwjs1/asnet.git.

Recent progress in deep convolutional neural networks has considerably changed the landscape of speech recognition, computer vision, natural language processing and so on. However, limited by the large number of parameters, the memory space and high computational complexity, it is a challenging task to deploy the deep neural network model in embedded system. To solve this problem, we propose Equal Trained Ternary Quantization (ETTQ), a ternary quantization method by improving Trained Ternary Quantization, which uses only a full-precision scaling coefficient for each layer, and quantize the weights to three levels. These positive and negative weights have same absolute values that are trainable parameters. Experiments show that the performance of ETTQ is only slightly worse than TTQ, but can converge faster and more stable during the training over CIFAR-10 and CIFAR-100 datasets.

Fully convolutional neural networks (FCNs) have shown great success in dense estimation tasks. One key pillar of such progress is mining multi-scale context cues from features in different convolutional layers. This paper introduces contextual aggregating network(CAN), a generic convolutional feature ensembling framework for semantic segmentation. Our framework first captures multi-scale contextual clues by concatenating multi-level feature representation, which carries both coarse semantics and fine details. Then it adaptively integrates stacked features to perform dense pixel estimation. The proposed CAN is trainable end-to-end, and allows us to fully investigate multi-scale context information embedded in images. The experiments show the promising results of our method on PASCAL VOC 2012 and Cityscapes dataset.

Open set domain adaptation by back propagation (OSDA-BP) was recently proposed as a novel end-to-end training approach for tackling the open set scenario, where only a few categories of interest are shared between source and target data. This paper provides an insightful understanding of the binary cross entropy loss employed in OSDA-BP for picking up the potential unknown samples. With this new understanding, we propose to replace the binary cross entropy loss with a symmetrical Kullback Leibler(KL) distance based loss. This improved OSDA-BP method is extensively evaluated over Office-31 dataset and a considerable performance improvement is observed.

The extensive computational burden limits the usage of CNNs in mobile devices for dense estimation tasks. In this paper, we present a lightweight network to address this problem, namely LEDNet, which employs an asymmetric encoder-decoder architecture for the task of real-time semantic segmentation. More specifically, the encoder adopts a ResNet as backbone network, where two new operations, channel split and shuffle, are utilized in each residual block to greatly reduce computation cost while maintaining higher segmentation accuracy. On the other hand, an attention pyramid network (APN) is employed in the decoder to further lighten the entire network complexity. Our model has less than 1M parameters, and is able to run at over 71 FPS in a single GTX 1080Ti GPU. The comprehensive experiments demonstrate that our approach achieves state-of-the-art results in terms of speed and accuracy trade-off on CityScapes dataset.

In this paper, we propose a new class of lattices constructed from polar codes, namely polar lattices, to achieve the capacity (1/2) log(1+SNR) of the additive white Gaussiannoise (AWGN) channel. Our construction follows the multilevel approach of Forney et al., where we construct a capacity-achieving polar code on each level. The component polar codes are shown to be naturally nested, thereby, fulfilling the requirement of the multilevel lattice construction. We prove that the polar lattices are AWGN-good. Furthermore, using the technique of source polarization, we propose discrete Gaussian shaping over the polar lattice to satisfy the power constraint. Both the construction and shaping are explicit, and the overall complexity of encoding and decoding is O(N log N) for any fixed target error probability.

Recently, exploring multiple feature maps from different layers in fully convolutional networks (FCNs) has gained substantial attention to capture context information for semantic segmentation. This paper presents a novel encoder-decoder architecture, called dense deconvolutional network (DDN), for semantic segmentation, where the feature maps of deeper convolutional layers are densely upsampled for the shallow deconvolutional layers. The proposed DDN is trainable end-to-end, and allows us to fully investigate multiple scale context cues embedded in images. The experimental results show that our DDN outperforms previous FCNs and encoder-decoder networks (EDNs) on PASCAL VOC 2012 dataset.

Recently, self-normalizing neural networks have been proposed with a scaled version of exponential linear units (SELUs), which can force neuron activations automatically converge towards zero mean and unit variance without use of batch normalization. As the negative part of SELUs is an exponential function, it is computationally intensive. In this paper, we introduce self-normalizing piecewise linear units (SPeLUs) for fast approximation of SELUs, adopting piecewise linear functions instead of the exponential part. Various possible shapes are discussed for piecewise linear units with stable self-normalizing properties. Experiments show that SPeLUs can provide an efficient and fast alternative to SELUs, with almost similar classification performance over MNIST, CIFAR-10 and CIFAR-10 datasets. With SPeLUs, we also show that batch normalization can be simply neglected for constructing deep neural nets, which could be advantageous for fast implementation of deep neural networks.

In this study, the author investigates the practical limitation of the recently proposed embedded cryptographic signature authentication scheme at the physical layer. By employing the log‐likelihood ratio of a tag bit and its approximation, the author shows that the equivalent authentication channel observed by the secondary receiver can be viewed as a binary‐input additive white Gaussian noise channel. Then, the sphere‐packing lower bound can be employed to show the transmission capability for practical finite‐length authentication tags. To achieve the same effective coverage area for both the primary and secondary receivers, it essentially requires efficient low‐rate channel coding schemes with near sphere‐packing‐bound performance at the secondary receiver, which contrasts sharply with the pessimistic conclusion of Jiang et al.

It is known that affinity propagation can perform exemplar-based unsupervised image clustering by taking as input similarities between pairs of images and producing a set of exemplars that best represent the images, and then assigning each nonexemplar image to its most appropriate exemplar. However, the clustering performance of affinity propagation is largely limited by the adopted similarity between any pair of images. As the scale invariant feature transform (SIFT) has been widely employed to extract image features, the nonmetric similarity between any pair of images was proposed by “hard” matching of SIFT features (e.g., counting the number of matching SIFT features). In this letter, we notice, however, that the decision of hard matching of SIFT features is binary, which is not necessary for deriving similarities. Hence, we propose a novel measure of similarities by replacing hard matching with the so-called soft matching. Experimental examples show that significant performance gains can be achieved by the resulting affinity propagation algorithm.

In the past, two main approaches for the purpose of authentication, including information-theoretic authentication codes and complexity-theoretic message authentication codes (MACs), were almost independently developed.In this paper, we propose a new cryptographic primitive, namely, artificial-noiseaided MACs (ANA-MACs), which can be considered as both computationally secure and information-theoretically secure.For ANA-MACs, we introduce artificial noise to interfere with the complexity-theoretic MACs and quantization is further employed to facilitate packet-based transmission.With a channel coding formulation of key recovery in the MACs, the generation of standard authentication tags can be seen as an encoding process for the ensemble of codes, where the shared key between Alice and Bob is considered as the input and the message is used to specify a code from the ensemble of codes.Then, we show that the introduction of artificial noise in ANA-MACs can be well employed to resist the key recovery attack even if the opponent has an unlimited computing power.Finally, a pragmatic approach for the analysis of ANA-MACs is provided, and we show how to balance the three performance metrics, including the completeness error, the false acceptance probability, and the conditional equivocation about the key.The analysis can be well applied to a class of ANA-MACs, where MACs with Rijndael cipher are employed.

In this paper, we propose a novel Artificial-Noise-Aided PHYsical layer Phase Challenge-Response Authentication Scheme (ANA-PHY-PCRAS) for practical orthogonal frequency division multiplexing (OFDM) transmission. In this new scheme, Tikhonov-distributed artificial noise is introduced to interfere with the phase-modulated key for resisting potential key-recovery attacks. Then, we address various practical issues for ANA-PHY-PCRAS with OFDM transmission, including correlation among subchannels, imperfect carrier, and timing recoveries. Among them, we show that the effect of sampling offset is significant and a search procedure in the frequency domain should be incorporated for verification. With practical OFDM transmission, the number of uncorrelated subchannels is often insufficient. Hence, we employ a time-separated approach for allocating enough subchannels, and a modified ANA-PHY-PCRAS is proposed to alleviate the discontinuity of channel phase at far-separated time slots. Finally, the key equivocation is derived for the worst case scenario. We conclude that the enhanced security of ANA-PHY-PCRAS comes from the uncertainties of both the wireless channel and introduced artificial noise, compared with the traditional challenge-response authentication scheme implemented at the upper layer.

We propose a low-rate turbo-Hadamard coding approach for narrow-band interference (NBI) suppression. The proposed approach can be viewed as the employment of a coded spread-spectrum signalling with time-varying spreading sequences and the NBI suppression is naturally achieved in iterative decoding. Compared with the traditional code-aided NBI suppression with direct-sequence spread-spectrum (DS-SS) signalling, the spreading sequences for the turbo-Hadamard coding approach are non-constant in time and determined from the columns of a Hadamard matrix subject to the low-rate coding rule. The low-rate turbo-Hadamard coding approach is sharply compared with the code-aided DS-SS approach with convolutional coding. With a spreading sequence of length 32, it is shown that the proposed turbo-Hadamard approach outperforms the code-aided DS-SS approach even when its transmission information rate is about 5 times higher than that of the coded-aided DS-SS approach.

To improve the mean acquisition time performance of the zero-padding (ZP) method, we propose to fully exploit the computation capability of FFT by partially folding the local pseudo-noise (PN) code. The detection and mean acquisition time performance of the proposed method is analyzed and compared with that of the zero-padding method. It is shown that the proposed method can reduce the mean acquisition time significantly at the expense of less than 1.5 dB degradation in detection performance.

In this paper, we propose the explicit construction of a new class of lattices based on polar codes, which are provably good for the additive white Gaussian noise (AWGN) channel. We follow the multilevel construction of Forney et al. (i.e., Construction D), where the code on each level is a capacity-achieving polar code for that level. The proposed polar lattices are efficiently decodable by using multistage decoding. Performance bounds are derived to measure the gap to the generalized capacity at given error probability. A design example is presented to demonstrate the performance of polar lattices.

For practical bi-directional relaying, symbols transmitted by two sources cannot arrive at the relay with perfect symbol and frame alignments and the asynchronous multiple-access channel (MAC) should be seriously considered. In this paper, we consider the Low-Density Parity-Check (LDPC)-coded BPSK signalling over the general asynchronous MAC with both frame and symbol misalignments. For the symbol-asynchronous MAC, we present a formal log-domain generalized sum-product-algorithm (Log-G-SPA) for efficient decoding. When the frame-asynchronism is encountered at the relay, we propose an original approach by employing the cyclic LDPC codes and the simple cyclic-redundancy-check (CRC) coding technique. Simulation results demonstrate the effectiveness of the proposed approach.

We propose a verification-based Interval-Passing (IP) algorithm for iteratively reconstruction of nonnegative sparse signals using parity check matrices of low-density parity check (LDPC) codes as measurement matrices. The proposed algorithm can be considered as an improved IP algorithm by further incorporation of the mechanism of verification algorithm. It is proved that the proposed algorithm performs always better than either the IP algorithm or the verification algorithm. Simulation results are also given to demonstrate the superior performance of the proposed algorithm.

Lattice precoding is an effective strategy for multiantenna broadcast. In this paper, we show that approximate lattice precoding in multiantenna broadcast is a variant of the closest vector problem (CVP) known as η-CVP. The proximity factors of lattice reduction-aided precoding are defined, and their bounds are derived, which measure the worst-case loss in power efficiency compared to sphere precoding. Unlike decoding applications, this analysis does not suffer from the boundary effect of a finite constellation, since the underlying lattice in multiantenna broadcast is indeed infinite.

The authors study the optimisation of the rate-compatible (RC)-irregular-repeat-accumulate (IRA) codes for asymmetric Slepian–Wolf coding to deal with flexible correlated sources. By assuming two different additive white Gaussian noise channels, the authors formulate a non-uniform extrinsic information transfer method to optimise the degree distribution of RC-IRA codes. The parity check matrices of RC-IRA codes are constructed from a mother matrix by combining puncturing and check-splitting, which achieves a good trade-off between the lowest-rate codes and the highest-rate codes. Simulation results show that the optimised non-uniform error-correcting code performs very close to the Slepian–Wolf limit over the target compression rate range and outperforms the prior results.

Based on the recently-proposed iterative receiver by Colavolpe et al., we extend it to further incorporate the mechanism of frequency offset compensation. As a progressive approach, the factor graph of a long frame is first divided into numerous subgraphs of short block, iterative decoding can successfully start its work as the effect of frequency offset can be well mitigated due to the small size of subgraph. As the iteration goes on, the extrinsic information from decoder can be employed to get finer estimate of the frequency offset. The system performance can be steadily improved as the block size is progressively expanded to the final frame size.

In this paper, we show how to compute the Slepian-Wolf lower bound for the reconciliation of physical layer secret keys between two radio terminals. Two coding approaches, including the syndrome method and the parity method, have been proven to require the same number of bits exchanged between two terminals for the reconciliation in the noiseless environment. We also present a practical coding approach based on LDPC codes, and the gap between the practical approach and the Slepian-Wolf bound is given.

A natural relationship between weighted bit-flipping (WBF) decoding and belief-propagation-like (BP-like) decoding is explored. This understanding can help us develop WBF algorithms from BP-like algorithms. For min-sum decoding, one can find that its WBF algorithm is the algorithm proposed by Jiang et al. For BP decoding, we propose a new WBF algorithm and show its performance advantage. The proposed WBF algorithms are parallelized to achieve rapid convergence. Two efficient simulation-based procedures are proposed for the optimization of the associated thresholds.

The clustering of cycles to form stopping sets are first observed by T. Tian and et al. As the determination of stopping sets of minimum size is NP-hard, we propose to consider clustering of cycles to avoid the stopping sets of small sizes. In particular, the clustering of two cycles are considered for an improved version of progressive edge-growth construction of LDPC codes.

In this paper, a modification to the weighted bit- flipping (WBF) decoding algorithm for low-density parity-check (LDPC) codes is proposed. This modification, based on the adjustment in reliabilities of the check sums during iterative decoding, can be applied to various WBF algorithms. Our studies show that the modification can be achieved with a small increase in complexity, but leads to an appealing improvement in error performance.

The parity-check matrix of a quasi-cyclic low- density parity-check (QC-LDPC) code can be compactly represented by a polynomial parity-check matrix. By using this compact representation, we derive a necessary and sufficient condition for determining the girth of QC-LDPC codes in a systematic way. The new condition avoids an explicit enumeration of cycles for determining the girth of codes, and thus can be well employed to generate QC-LDPC codes with large girth.

A natural relationship between weighted bit-flipping (WBF) decoding and message-passing decoding is explored. This understanding can help us develop a dual WBF decoding algorithm from one type of message-passing decoding algorithm and vice versa. For min-sum decoding, one can find that its dual WBF algorithm is the algorithm proposed by Jiang et al. For belief-propagation (BP) decoding, we propose a new WBF algorithm and show its performance advantage. For some high-rate low-density parity-check (LDPC) codes of large row weight, it is shown that the WBF algorithm proposed by Liu and Pados performs extraordinarily well. However, its dual message- passing decoding does not work well. Furthermore, we propose a parallel implementation framework for various WBF algorithms. Compared to serial implementations, various WBF algorithms in their parallel form converge significantly faster and often perform better.

Recently, Gallager's bounding techniques have been used to derive tight performance bounds for coded systems in fading channels. Most works in this field have thus far dealt with coherent decoding. This paper develops Gallager bounds for noncoherent systems in fading channels. Unlike coherent decoding, the exact error probability of a noncoherent decoder/detector conditioned on the fading coefficients does not admit a closed-form expression. This difficulty is overcome in this paper by employing the Chernoff technique. Although it weakens the bounds to some extent, the Chernoff technique enables the derivations of the limit-before-average (LBA) bound and Gallager bounds in closed form for noncoherent fading channels. Numerical examples show that the proposed bounds are convergent and are tighter than the conventional union bound.

In this paper, we propose a new upper bound on the error performance of binary linear codes over block-fading channels by employing Gallager's first- and second-bounding techniques. As the proposed bound is numerically intensive in its general form, we consider two special cases, namely, the spherical bound and the DS2-exponential bound, which are found to be tight in nonergodic and near-ergodic block-fading channels, respectively. The tightness of the proposed bounds is demonstrated for turbo codes. Many existing bounds for quasistatic or fully interleaved fading channels can be viewed as special cases of the proposed Gallager bound.

The parity-check matrix of a quasi-cyclic code can be represented by a polynomial parity-check matrix with a significantly lower dimension. By using this compact representation, we can develop an efficient method for locating the girth of the quasi-cyclic code. The proposed girth-locating algorithm can be well employed to generate quasi-cyclic low-density parity-check codes with large girth

In this letter, we show how to compute the asymptotic growth rate of input-output weight enumerator (AGR-IOWE) for some accumulate-based codes by using the sharp tools already developed. Numerical results on the AGR-IOWE for irregular repeat-accumulate (IRA) codes, systematic regular RA (SRA) codes, and concatenated zigzag codes are reported. It is observed that the SRA code has the same AGR-IOWE as a comparable concatenated zigzag code. For both SRA and concatenated zigzag codes, if keeping the code rate fixed, the increase of the grouping factor for the component punctured accumulate code may result in better asymptotic performance under maximum-likelihood decoding, but often worse performance under iterative sum-product decoding.

We derive upper bounds on the decoding error probability of binary block codes over noncoherent block additive white Gaussian noise (AWGN) and fading channels, with applications to turbo codes. By a block AWGN (or fading) channel, we mean that the carrier phase (or fading) is assumed to be constant over each block but independently varying from one block to another. The union bounds are derived for both noncoherent block AWGN and fading channels. For the block fading channel with a small number of fading blocks, we further derive an improved bound by employing Gallager's first bounding technique. The analytical bounds are compared to the simulation results for a coded block-based differential phase shift keying (B-DPSK) system under a practical noncoherent iterative decoding scheme proposed by Chen et al. We show that the proposed Gallager bound is very tight for the block fading channel with a small number of fading blocks, and the practical noncoherent receiver performs well for a wide range of block fading channels

Space-time codes increase the transmission rate of a communication system in fading channels significantly. The decoding of space-time codes generally depends on perfect channel estimation. Differential space-time modulation (DSTM) can work, in a noncoherent manner, over continuously fading channels. However, it exhibits an irreducible error floor in time-correlated fading channels. The impact of correlated Rayleigh fading on DSTM is investigated, and a decision-feedback linear prediction receiver for DSTM is presented. Computer simulations show that the linear prediction receiver reduces the error floor substantially.

In this paper, the effect of unmatched despreading chip waveforms for locally generated early and late despreading codes in a coherent delay-locked loop (CDLL) for DS/SS systems is investigated. Linear and nonlinear theories are employed to evaluate the performance of the CDLL. Based on linear theory, optimum despreading chip waveforms are pursued in the sense of minimizing root mean square (RMS) tracking error with both time limited (full response) and time unlimited constraints. Nonlinear analysis shows that the use of designed chip waveforms reduces RMS tracking error and increase mean time to lose lock (MTLL). Both rectangular and sinc chip pulse-shaping waveforms are considered as two widely used examples. It is also found that the designed despreading chip waveforms are optimized for any specified early-late spacing.