As envisioned in the sixth-generation (6G) networks, connectivity density of users and/or machines is expected to surge in future Internet of Everything. However, most current unsourced multiple access schemes experience a swift decline in performance as the number of active users increases due to higher level of interference, limited symbol representation space and available spectrum resources. To tackle these, we propose in this paper a generalized finite-field unsourced multiple access (FFUMA) with either explicit or implicit pilots. The first several information bits in each user message are used to select the spreading pattern, and the remaining information bits are encoded and spread over the finite field. For the explicitly-piloted transmission, the first few bits are mapped to a low-correlated or compressed-sensing pilot which is then transmitted through the channel. At the receiver, a two-stage algorithm consisting of pilot detection and finite-field data decoding is proposed. For the implicitly-piloted transmission, the first few bits are not physically transmitted, but they are in fact delivered to the receiver once the spreading pattern is correctly recognized. We establish the algorithm for blindly detecting the active finite-field spreading patterns and set forth the joint iterative data detection and decoding over the finite field. Error analyses of the proposed finite-field UMA, including error event decomposition and extrinsic information transfer (EXIT) analysis, are provided to evaluate the performance. The advantages of the proposed FFUMA have been demonstrated by the simulation results in terms of error rates and energy-efficiency.

We propose a neural min-sum (MS) decoder for non-binary LDPC codes by applying deep learning to the decoding with short to moderate code lengths. In this algorithm, the iterations are unfolded into hidden layers of the neural network, and trainable weights are added to the check node updates to mitigate the correlation in the message passing due to short girths in the Tanner graph. To avoid gradient vanishing and to converge faster, we adopt an iterative training mechanism for parameter tuning. Furthermore, we investigate the impact of girth distribution on the proposed neural MS decoder, which reveals the reasons for the expected performance improvement. Numerical results in terms of bit error rate are provided, which show that the neural MS decoder achieves significant gains for decoding non-binary LDPC codes.

Deep autoencoder-based joint source-channel coding (JSCC) has gained significant attention for end-to-end image semantic communication systems. However, existing methods typically optimize a uniform bandwidth-distortion trade-off over the entire image, potentially leading to the loss of crucial details and inconsistent content for tasks with diverse regions of interest. In this paper, we propose a flexible fine-grained bandwidth allocation method for deep JSCC that enables highly efficient, task-scalable image transmission across various semantic communication scenarios using a single codec. Our method optimizes the bandwidth-distortion trade-off by constraining image distortion through a 2D pixel-wise quality map. Guided by the pixel-wise quality map, we introduce a novel conditional affine transformation that generates dedicated semantic feature maps tailored to specific tasks. Additionally, we introduce a semantic guidance network to automatically generate task-aware quality maps via backpropagation without additional retraining. This approach leverages a pretrained variable-length neural JSCC codec and adjusts the transmission quality on a fine-grained level, eliminating the need to train separate models for different tasks. Experimental results demonstrate the effectiveness of our bandwidth allocation method, enhancing task-specific performance in various goal-oriented image communication scenarios without additional training.

In this paper, we propose a novel nonlinear-transform source-channel encoding and decoding scheme for cooperative relay (NTSC-R) systems. This scheme initially maps the source image to a latent space using a learned nonlinear-analysis transform, generating a latent representation. This representation is then encoded into variable-length codewords, which are broadcast to both the relay and destination. At the relay, the messages are either amplified and forwarded (AF) or decoded and forwarded (DF) to the destination. The destination integrates these signals, taking into account their respective contributions to the outcome, to reconstruct a higher-quality image. Unlike existing deep joint source-channel coding (deep JSCC) methods, our approach incorporates an entropy model as a prior for the latent representation, which enables the encoder to adapt codeword lengths based on the significance of the information. At the destination, the receiver merges information considering the quality and confidence level of each received signal. The entire system is trained end-to-end to optimize rate-distortion performance under established metrics. The simulations demonstrate that the proposed NTSC-R outperforms traditional baselines using BPG compression and polar codes, as well as current deep JSCC methods in both AF and DF modes across various image resolutions.

Toward next-generation multiple access, we propose a multiuser transmission framework via non-binary factor graphs defined over Galois field GF(q). The non-orthogonal multiuser transmission is realized by applying the GF(q) multiplication and superposition. In coordination with the high-order modulation, the proposed GF(q) codebook improves the cardinality of the superimposed constellation and reduces interference at each resource element. The proposed structure also characters for flexibility by spatially coupling the base graph when applying to different user scales. We have theoretically proved that both the system capacity and the error performance are improved due to the GF(q) operations. We utilize the simulated annealing algorithm to optimize both the base factor graph and the spatially-coupled graph, therefore short girths are eliminated or mitigated. The detection of the proposed scheme can be performed through iterative belief-propagation (BP) based on symbol-level log-domain probability vectors derived on the GF(q) factor graph. The numerical results in terms of upper bounds of maximum likelihood (ML) detection and symbol error rates through BP detection are both provided, which demonstrate the superiority of the GF(q) factor graphs to their binary counterparts. The performance can be further improved by increasing the field order q and/or by spatially coupling the non-binary factor graph.

Unsourced multiple access (UMA) is a promising technique for the Internet-of-Things systems with a large number of users. In this paper, we explore the good algebraic properties of finite fields and propose a non-binary coding scheme for the UMA. The scheme is based on the slotted structure and T-fold ALOHA, and each active user randomly selects one slot to transmit its message. Finite-field operations are performed for both the channel coding and the multiple access. Compared to binary-field operations, the finite-field sparse spreading and multiuser superposition lead to improved diversity and less interference on each channel use. In addition, the non-binary LDPC code boosts the performance under short/moderate code length. We derive the joint belief-propagation decoding algorithm for the concatenated finite-field factor graph, which is then verified by the extrinsic information transfer (EXIT) analysis. The numerical results in terms of energy-efficiency performance are provided, which clearly demonstrate the superiority to the existing schemes in the region with large user numbers. The proposed scheme has the potential to carry out larger-scale user connections, and the energy-efficiency performance can be further improved by optimizing the coding parameters.

Deep neural networks (DNNs) are sensitive and susceptible to tiny perturbations by adversarial attacks which cause erroneous predictions. Various methods, including adversarial defense and uncertainty inference (UI), have been developed to overcome adversarial attacks in recent years. In this paper, we propose a multi-head uncertainty inference (MH-UI) framework for detecting adversarial attack examples. We adopt a multi-head architecture with multiple prediction heads (i.e., classifiers) to obtain predictions from different depths in the DNNs and introduce shallow information for the UI. Using independent heads at different depths, the normalized predictions are assumed to follow the same Dirichlet distribution, and we estimate the distribution parameter of it by moment matching. Cognitive uncertainty brought by the adversarial attacks will be reflected and amplified in the distribution. Experimental results show that the proposed MH-UI framework has good performance in different settings of adversarial attack detection tasks.

In this paper we propose a novel non-orthogonal multiple access (NOMA) scheme via non-binary factor graphs by introducing operations over Galois field GF(q). By utilizing sparse GF(q) spreading, the proposed scheme benefits from an improved diversity of the superimposed symbols and accordingly a lower probability of constellation overlapping. Meanwhile, the coordination with high-order modulation helps to reduce the interference at the receiver, and therefore a better error performance can be expected. Iterative belief-propagation (BP) detection based on log-domain probabilities is derived for the GF(q) NOMA. The upper bounds of maximum likelihood (ML) detection and simulation results in terms of symbol error rate are provided, which demonstrate the superiority of the proposed scheme to its binary counterparts.

Recently a number of low-density parity-check (LDPC) decoding algorithms based on deep learning have been proposed in the literature. However, most of the work has been targeted for additive white Gaussian noise (AWGN) channels. For more practical scenarios, in this paper we investigate the neural-network based min-sum (MS) decoding for protograph LDPC codes in fading channels. Since the wireless channel is complex and varying, accurate channel state information (CSI) cannot be always available at the receiver. We classify the scenarios into three cases with perfect CSIs, imperfect CSIs, and no CSIs. By assigning learnable weights on the edges in the iterative decoding, the proposed neural decoder compensates for the performance loss caused by the error/lack of CSIs. The trajectory-based extrinsic information transfer (T-EXIT) chart is employed as a theoretical tool to select the proper training dataset for the neural network and the proper channel initialization scheme for the receiver. Numerical results in terms of block error rates are provided, which agree with the T-EXIT analysis. It can be seen that the proposed neural MS decoder clearly outperforms the traditional MS algorithm in Rayleigh fading channels. Meanwhile, the proposed decoder shows a good compatibility so that it can be applied to the cases with different accuracy of CSIs.

How to efficiently transmit a special video over wireless channels? While the established systems work by combining H.26x video coding and 5G LDPC channel coding, its end-to-end transmission efficiency is still far away from the extreme for video sources in a specific domain. In this paper, we seek to design a special semantic communication system tailored for transmitting video calling streams over the wireless channels. Inspired by the recent progress in talking-head animation, we propose a talking- head semantic transmission (THST) system, which can efficiently transmit motion keypoint representation as compact semantic information to drive the free-view talk-heading synthesis at the receiver. Since the motion semantic key points are correlated, our THST system learns a nonlinear analysis transform to map the key points across multiple frames into latent space, then transmits the latent hyper semantic representation to the receiver via deep joint source-channel coding. Our system incorporates a latent prior to estimate the importance diversity on the semantic key points, accordingly, we realize variable rate joint source-channel coding to obtain system level coding gain. Extensive experimental validation shows that our THST system outperforms engineered competing systems on benchmark datasets. Moreover, due to the system level joint source and channel design, our method provides much more robust performance over noisy channels with only 33% bandwidth cost versus the current talking-head compression combined with 5G LDPC coded transmission systems.

The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned data transmission schemes have exhibited superior performance than the established source and channel coding methods. While, so far, research efforts mainly concentrated on architecture and model improvements toward a static target domain. Despite their successes, such learned models are still suboptimal due to the limitations in model capacity and imperfect optimization and generalization, particularly when the testing data distribution or channel response is different from that adopted for model training, as is likely to be the case in real-world. To tackle this, in this paper, we propose a novel online learned joint source and channel coding approach that leverages the deep learning model’s overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the codec model or representations to an individual data or channel state instance, which can further lead to substantial gains in terms of the end-to-end rate-distortion performance. Accordingly, the streaming ingredients include both the semantic representations of source data and the online updated decoder model parameters. The system design is formulated as a joint optimization problem whose goal is to minimize the loss function, a tripartite trade-off among the data stream bandwidth cost, model stream bandwidth cost, and end-to-end distortion. The proposed methods enable the communication-efficient adaptation for all parameters in the network without sacrificing decoding speed. Extensive experiments, including user study, on continually changing target source data and wireless channel environments, demonstrate the effectiveness and efficiency of our approach, on which we outperform existing state-of-the-art engineered transmission scheme (VVC combined with 5G LDPC coded transmission).

In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels. The proposed methods exploit nonlinear transform and conditional coding architecture to adaptively extract semantic features across video frames, and transmit semantic feature domain representations over wireless channels via deep joint source-channel coding. Our framework is collected under the name deep video semantic transmission (DVST). In particular, benefiting from the strong temporal prior provided by the feature domain context, the learned nonlinear transform function becomes temporally adaptive, resulting in a richer and more accurate entropy model guiding the transmission of current frame. Accordingly, a novel rate adaptive transmission mechanism is developed to customize deep joint source-channel coding for video sources. It learns to allocate the limited channel bandwidth within and among video frames to maximize the overall transmission performance. The whole DVST design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under perceptual quality metrics or machine vision task performance metrics. Across standard video source test sequences and various communication scenarios, experiments show that our DVST can generally surpass traditional wireless video coded transmission schemes. The proposed DVST framework can well support future semantic communications due to its video content-aware and machine vision task integration abilities.

As one novel approach to realize end-to-end wireless image semantic transmission, deep learning-based joint source-channel coding (deep JSCC) method is emerging in both deep learning and communication communities. However, current deep JSCC image transmission systems are typically optimized for traditional distortion metrics such as peak signal-to-noise ratio (PSNR) or multi-scale structural similarity (MS-SSIM). But for low transmission rates, due to the imperfect wireless channel, these distortion metrics lose significance as they favor pixel-wise preservation. To account for human visual perception in semantic communications, it is of great importance to develop new deep JSCC systems optimized beyond traditional PSNR and MS-SSIM metrics. In this paper, we introduce adversarial losses to optimize deep JSCC, which tends to preserve global semantic information and local texture. Our new deep JSCC architecture combines encoder, wireless channel, decoder/generator, and discriminator, which are jointly learned under both perceptual and adversarial losses. Our method yields human visually much more pleasing results than state-of-the-art engineered image coded transmission systems and traditional deep JSCC systems. A user study confirms that achieving perceptually similar end-to-end image transmission quality, the proposed method can save about 50% wireless channel bandwidth costs.

In this paper, we propose a class of high-efficiency deep joint source-channel coding methods that can closely adapt to the source distribution under the nonlinear transform, it can be collected under the name nonlinear transform source-channel coding (NTSCC). In the considered model, the transmitter first learns a nonlinear analysis transform to map the source data into latent space, then transmits the latent representation to the receiver via deep joint source-channel coding. Our model incorporates the nonlinear transform as a strong prior to effectively extract the source semantic features and provide side information for source-channel coding. Unlike existing conventional deep joint source-channel coding methods, the proposed NTSCC essentially learns both the source latent representation and an entropy model as the prior on the latent representation. Accordingly, novel adaptive rate transmission and hyperprior-aided codec refinement mechanisms are developed to upgrade deep joint source-channel coding. The whole system design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under established perceptual quality metrics. Across test image sources with various resolutions, we find that the proposed NTSCC transmission method generally outperforms both the analog transmission using the standard deep joint source-channel coding and the classical separation-based digital transmission. Notably, the proposed NTSCC method can potentially support future semantic communications due to its content-aware ability and perceptual optimization goal.

The recent development of deep learning methods provides a new approach to optimize the belief propagation (BP) decoding of linear codes.However, the limitation of existing works is that the scale of neural networks increases rapidly with the codelength, thus they can only support short to moderate codelengths.From the point view of practicality, we propose a high-performance neural min-sum (MS) decoding method that makes full use of the lifting structure of protograph low-density parity-check (LDPC) codes.By this means, the size of the parameter array of each layer in the neural decoder only equals the number of edge-types for arbitrary codelengths.In particular, for protograph LDPC codes, the proposed neural MS decoder is constructed in a special way such that identical parameters are shared by a bundle of edges derived from the same edge-type.To reduce the complexity and overcome the vanishing gradient problem in training the proposed neural MS decoder, an iteration-byiteration (i.e., layer-by-layer in neural networks) greedy training method is proposed.With this, the proposed neural MS decoder tends to be optimized with faster convergence, which is aligned with the early termination mechanism widely used in practice.To further enhance the generalization ability of the proposed neural MS decoder, a codelength/rate compatible training method is proposed, which randomly selects samples from a set of codes lifted from the same base code.As a theoretical performance evaluation tool, a trajectory-based extrinsic information transfer (T-EXIT) chart is developed for various decoders.Both T-EXIT and simulation results show that the optimized MS decoding can provide faster convergence and up to 1dB gain compared with the plain MS decoding and its variants with only slightly increased complexity.In addition, it can even outperform the sum-product algorithm for some short codes.

Classifying the sub-categories of an object from the same super-category (e.g., bird species and cars) in fine-grained visual classification (FGVC) highly relies on discriminative feature representation and accurate region localization. Existing approaches mainly focus on distilling information from high-level features. In this article, by contrast, we show that by integrating low-level information (e.g., color, edge junctions, texture patterns), performance can be improved with enhanced feature representation and accurately located discriminative regions. Our solution, named Attention Pyramid Convolutional Neural Network (AP-CNN), consists of 1) a dual pathway hierarchy structure with a top-down feature pathway and a bottom-up attention pathway, hence learning both high-level semantic and low-level detailed feature representation, and 2) an ROI-guided refinement strategy with ROI-guided dropblock and ROI-guided zoom-in operation, which refines features with discriminative local regions enhanced and background noises eliminated. The proposed AP-CNN can be trained end-to-end, without the need of any additional bounding box/part annotation. Extensive experiments on three popularly tested FGVC datasets (CUB-200-2011, Stanford Cars, and FGVC-Aircraft) demonstrate that our approach achieves state-of-the-art performance. Models and code are available at https://github.com/PRIS-CV/AP-CNN_Pytorch-master.

A new polar-coded modulation (PCM) framework with the bit interleaving is proposed to enhance the polarization diversity among the bit polarized subchannels under the finite block length, consequently, the transmission reliability is further improved. The key idea is asynchronously transmitting the coded bits within one block and spatially coupling multiple coded blocks by the joint modulation, and the polarization diversity among the modulation synthesized subchannels under the parallel partition is enhanced. Combining the binary polar coding, this polarization enhancement at the modulation partition stage is then delivered to the final bit polarized subchannels. The capacity-achieving property under the infinite block length and the polarization superiority with respect to state-of-the-art PCM schemes under the finite block length are proved. Finally, the simulation results indicate the performance gain compared to the conventional PCM and 5G LDPC coded modulation schemes.

Prediction of heat demand using artificial neural networks has attracted enormous research attention. Weather conditions, such as direct solar irradiance and wind speed, have been identified as key parameters affecting heat demand. This paper employs an Elman neural network to investigate the impacts of direct solar irradiance and wind speed on the heat demand from the perspective of the entire district heating network. Results of the overall mean absolute percentage error (MAPE) show that direct solar irradiance and wind speed have quite similar impacts. However, the involvement of direct solar irradiance can clearly reduce the maximum absolute deviation when only involving direct solar irradiance and wind speed, respectively. In addition, the simultaneous involvement of both wind speed and direct solar irradiance does not show an obvious improvement of MAPE. Moreover, the prediction accuracy can also be affected by other factors like data discontinuity and outliers.

In this paper we propose the cooperative nonorthogonal multiple access by constructing the bilayer factor graph. The users at the source transmit their symbols by superimposing on a number of resource elements. If the NOMA transmission is not successful at the destination, the relay assists by decoding and forwarding. The superposition structures on the two links construct a bilayer factor graph. The information from the relay helps to distinguish the constellation overlapping and improve the distance property. Optimizations are carried out to maximize the sum rate of the transmission by using the genetic algorithm. Numerical results in terms of transmission rate and symbol error rate are provided, which illustrate the superiority of the bilayer factor graph in the cooperative NOMA system.

A novel non-orthogonal multiple access scheme through the rearrangement of the modulation constellation is proposed in this paper. Each user's modulated symbols are remapped by a permutation function before they are superimposed on different resource elements. The mutual information of the multiuser system is considered as the criterion to find the optimal remapping for each user. The simulated annealing algorithm is employed to realize the optimization. Based on the numerical results in terms of bit error rate, the proposed scheme clearly outperforms the schemes in the literature.

In this paper we investigate the optimization of the non-orthogonal multiuser transmission through the superposition factor graph. The sum rate of the multiuser system is utilized as the performance metric. The optimization is carried out through the edge connection and the spatial coupling of the base structure. The edge connection associates with the superposition matrix and determines the layout of the joint constellation. By spatially coupling the base structure, the short girth is further removed without changing the load of the system and the complexity in the processing. The genetic algorithm is applied to realize the optimization. Numerical results reveal the characteristics of the optimal factor graphs for different SNR regions. The optimized structures demonstrate superior performance to those which have been proposed in the literature in terms of sum rate and bit error rate.

Spatial coupling has been applied in multiple access system in order to obtain higher spectral efficiency, where different users share the same resource blocks by superimposing data streams with different time offsets. In this paper, we introduce constellation rotation into the spatially-coupled multiuser system to further distinguish users and mitigate the interferences. The optimization of the rotation angles is carried out by maximizing the average mutual information between the transmitted symbols and the received signal. Simulation results show that the constellation rotation generally contributes to the performance improvement of the system and reduces the decoding latency. Benefiting from the optimization, the system with the optimal rotation angle set clearly outperforms the others.

In this paper, polar codes are first applied in non-orthogonal multiple access (NOMA) and the channel polarization idea is extended to NOMA, which is a major multiple access technique in 5G systems. The polar coded NOMA (PC-NOMA) scheme is proposed, whereby the NOMA channel is decomposed into a series of binary-input channels under a two-stage channel polarization transform. In the first stage, the NOMA channel is divided into a group of user synthesized channels by using the multi-level coding structure. In the second stage, based on the structure of bit-interleaved code modulation, user synthesized channels are further decomposed into binary polarized channels. Then, a joint successive cancellation decoding scheme is given to construct the multiuser receiver of PC-NOMA. Finally, a low complexity search algorithm is proposed to schedule the NOMA decoding order which improves the error performance by enhanced polarization among user synthesized channels. The block error ratio performances over additive white Gaussian noise channels indicate that the proposed PC-NOMA obviously outperforms the turbo coded NOMA scheme due to the advantages of the two-stage polarization.

Spatially coupling data transmission (SCDT) is a multiple access technique. Since both SCDT and low density parity-check (LDPC) codes can be represented by a factor graph, a joint sparse graph (JSG) including the single graphs of SCDT and LDPC codes is constructed. Based on the JSG, the joint demodulation and decoding (JDD) by applying belief propagation (BP) algorithm in the parallel schedule is performed, but its convergence rate is slow. In order to accelerate convergence rate, a new serial schedule is proposed. The extrinsic information transfer (EXIT) charts of iterative JDD are investigated for the two schedules and employed to evaluate their converge behaviour. EXIT analysis and simulation results demonstrate that about half iteration number can be saved at the cost of marginal system performance loss. Furthermore, compared with separate demodulation and decoding, turbo-structured JDD and the uncoupling structured JDD, the JDD based on JSG for SCDT by utilizing serial schedule achieves the best performance.

Spatially coupled LDPC codes have attracted much attention due to the promising performance. Recursive encoding with low delay and low complexity has been proposed in the literature for selected node degrees. To realize the recursive encoding of spatially coupled LDPC codes with arbitrary rates, we propose in this paper a modified structure of the parity-check matrix and implement the encoding using a shift-register regardless of the node degrees. By rearranging the edge connections, the parity bits at each coupling position can be jointly determined by the information bits at the current position and the encoded bits at former positions. Performance analysis in terms of design rate and density evolution has been provided. It can be observed that the modified code structure leads to a better belief-propagation threshold. Finite-length simulation results are provided, which verify the theoretical analysis.

In this paper, the exact closed-form outage probability expression is derived for cognitive relay network with incremental decode-and-forward (IDF) protocol in independent non-identically distributed (i.n.i.d.) Nakagami-m fading channels. The outage performance comparisons are made between IDF and DF protocols. Besides, the impact of channel fading parameters is investigated for both secondary transmission links and interference links. The results show that a significant gain can be made by using IDF protocol, especially when the secondary direct link is in good channel condition. Moreover, the outage performance is dominated by the channel quality of the secondary transmission links and is also impacted by the channel quality of the interference links.

In this paper we present bilayer LDPC convolutional codes for half-duplex relay channels. Two types of codes, bilayer expurgated LDPC convolutional codes and bilayer lengthened LDPC convolutional codes, are proposed for decode-and-forward (DF) relaying. In the case of the binary erasure relay channel, we prove analytically that both code constructions achieve the capacities of the source-relay link and the source-destination link simultaneously, provided that the channel conditions are known when designing the codes. Meanwhile, both codes enable the highest transmission rate possible with DF relaying for a wide range of channel parameters. In addition, the regular degree distributions can easily be computed from the channel parameters, which significantly simplifies the code optimization. The code construction and performance analysis are extended to the general binary memoryless symmetric channel, where a capacity-achieving performance is conjectured. Numerical results are provided for both types of codes with finite node degrees over binary erasure channels and binary-input additive white Gaussian noise channels, which verify the aforementioned theoretical analysis.

In this paper, we propose a new family of rate-compatible regular low-density parity-check (LDPC) convolutional codes. The construction is based on graph extension, i.e., the codes of lower rates are generated by successively extending the graph of the base code with the highest rate. Theoretically, the proposed rate-compatible family can cover all the rational rates from 0 to 1. In addition, the regularity of degree distributions simplifies the code optimization. We prove analytically that all the LDPC convolutional codes of different rates in the family are capable of achieving the capacity of the binary erasure channel (BEC). The analysis is extended to the general binary memoryless symmetric channel, for which a capacity-approaching performance can be achieved. Analytical thresholds and simulation results for finite check and variable node degrees are provided for both BECs and binary-input additive white Gaussian noise channels. The results confirm that the decoding thresholds of the rate-compatible codes approach the corresponding Shannon limits over both channels.

In this paper we present regular bilayer LDPC convolutional codes for half-duplex relay channels. For the binary erasure relay channel, we prove that the proposed code construction achieves the capacities for the source-relay link and the source-destination link provided that the channel conditions are known when designing the code. Meanwhile, this code enables the highest transmission rate with decode-and-forward relaying. In addition, its regular degree distributions can easily be computed from the channel parameters, which significantly simplifies the code optimization. Numerical results are provided for the codes with finite node degrees over binary erasure channels. We can observe that the gaps between the decoding thresholds and the Shannon limits are impressively small.

In this paper we construct a family of rate-compatible LDPC convolutional codes for Type-II HARQ systems. For each code family, the codes of lower rates are constructed by successively extending the graph of the high-rate base code. Theoretically, the proposed rate-compatible family includes all rates from 0 to 1. We prove analytically that all LDPC convolutional codes in the family are capacity achieving over the binary erasure channel (BEC). Thus, if applied to an idealized HARQ system over the BEC where the channel parameter stays constant within one complete information delivery, the throughput achieves the capacity of the channel. Moreover, the code construction is realized by regular degree distributions, which greatly simplifies the optimization.

In this paper we propose a new adaptive coding scheme for distributed channel coding for the three-node relay channel. In order to make it feasible for application in wireless sensor networks, the distributed code is built from standard components like Turbo and convolutional codes, and adaptation at the relay is obtained by puncturing the input and output of the employed channel code. The proposed code structure includes distributed Turbo codes and distributed serially concatenated codes as special cases. As the results of our optimization show, significant improvements in terms of rate and coverage are obtained. Compared to theoretical limits a decent performance is achieved considering that the focus is on feasibility.