This paper introduces an efficient algorithm based on the Parity-Consistent Decomposition (PCD) method to determine the WD of pre-transformed polar codes. First, to address the bit dependencies introduced by the pre-transformation matrix, we propose an iterative algorithm to construct an \emph{Expanded Information Set}. By expanding the information bits within this set into 0s and 1s, we eliminate the correlations among information bits, thereby enabling the recursive calculation of the Hamming weight distribution using the \emph{PCD method}. Second, to further reduce computational complexity, we establish the theory of equivalence classes for pre-transformed polar codes. Codes within the same equivalence class share an identical weight distribution but correspond to different \emph{Expanded Information Set} sizes. By selecting the pre-transformation matrix that minimizes the \emph{Expanded Information Set} size within an equivalence class, we optimize the computation process. Numerical results demonstrate that the proposed method significantly reduces computational complexity compared to existing deterministic algorithms.

This paper proposes a unified analytical framework for reconfigurable intelligent surface (RIS)-assisted two-way full-duplex (TW-FD) communication systems in 6G Internet of Things (IoT) scenarios. The proposed framework specifically addresses RISs with N reflective elements, facilitating efficient bidirectional communication. A novel unified moment-based analytical approach is developed, accommodating diverse fading models including Rayleigh, Nakagami-n, Weibull, Nakagami-m, and κ-μ, thereby significantly enhancing the versatility and practicality for complex 6G IoT environments. To comprehensively validate the applicability of our analysis, both independently identically distributed (i.i.d.) and independently non-identically distributed (i.n.i.d.) fading channel scenarios are investigated. By employing the Edgeworth expansion method, we derive analytical expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the end-to-end signal-to-interference-plus-noise ratio (SINR). Additionally, closed-form expressions for probability, average symbol error rate (SER) for various modulation schemes, and end-to-end ergodic rate are provided. Monte Carlo simulations demonstrate the accuracy and robustness of the theoretical models proposed. The results presented in this work not only contribute substantially to the analytical methodologies for RIS-assisted communication but also offer practical guidance for the design and optimization of future 6G IoT systems.

This paper investigates distributed source-channel coding for correlated image semantic communication, where different but correlated image sources from distributed transmitters are separately encoded and transmitted through dedicated wireless channels to a common receiver. We propose a novel distributed nonlinear transform coding (NTC)-based approach to explicitly model source correlation from both probabilistic and geometric perspectives. The probabilistic perspective is achieved by a joint entropy model that approximates the joint distribution of latent representations and guides adaptive rate allocation, while the geometric perspective is achieved by a feature alignment module that aligns latent features for maximal correlation learning at the decoder.We implement this distributed NTC framework for both source coding only, referred to as D-NTSC, and joint source-channel coding (JSCC), referred to as D-NTSCC. Variational inference is employed to derive principled loss functions that jointly optimize encoding, decoding, and joint entropy modeling. Extensive experiments on real-world multi-view datasets demonstrate that D-NTSC and D-NTSCC outperform existing distributed source coding and distributed JSCC baselines, respectively, achieving state-of-the-art performance in both pixel-level and perceptual quality metrics.

This paper introduces a novel parity-consistent decomposition (PCD) method for accurately determining the weight distribution (WD) of polar codes. In the PCD method, polar codes are represented as a union of a new type of polar cosets, characterized by the property that any two adjacent bits in the uncoded sequence are either both information bits or both frozen bits. This representation reduces the number of polar cosets compared to the existing automorphism-based (AUT) method. We introduce a recursive decomposition method for this new polar coset, which facilitates efficient recursive computation of the WD of polar codes. The PCD method is organized within a tree structure, which facilitates the evaluation of its computational complexity. We then investigate the transitivity of the action of lower triangular affine (LTA) transformations on a code set composed of the new polar cosets. This transitivity property enables a further reduction in the number of cosets that need to be computed. Furthermore, we propose a pruning algorithm to determine the partial weight distribution (PWD) of polar codes based on the tree structure of the PCD method. Given an upper bound on the Hamming weight for the PWD, we update the Hamming weight threshold for each code in the tree and remove the codes whose minimum Hamming weights exceed their corresponding thresholds. Numerical results demonstrate the superiority of the PCD method over existing methods.

Real-time speech communication over wireless networks remains challenging, as conventional channel protection mechanisms cannot effectively counter packet loss under stringent bandwidth and latency constraints. Semantic communication has emerged as a promising paradigm for enhancing the robustness of speech transmission by means of joint source channel coding (JSCC). However, its cross-layer design hinders practical deployment due to the incompatibility with existing digital communication systems. To address this, we perform JSCC over the network layer to combat packet loss and support real deployment. Inspired by the generative latent modeling, we propose Glaris, a generative latent-prior-based resilient speech semantic communication framework that performs resilient transform coding in the generative latent space. Generative latent priors enable high-quality packet loss concealment (PLC) at the receiver side, well-balancing semantic consistency and reconstruction fidelity. Additionally, an integrated error resilience mechanism is designed to mitigate the error propagation and improve the effectiveness of PLC. Compared with traditional packet-level forward error correction (FEC) strategies, our new method achieves enhanced robustness over dynamic wireless networks while reducing redundancy overhead significantly. Experimental results on the LibriSpeech dataset demonstrate that Glaris consistently outperforms existing error-resilient codecs, achieving JSCC-level robustness while maintaining seamless compatibility with existing systems, and it also strikes a favorable balance between transmission efficiency and error resilience.

To support efficient and accurate multi-task inference in edge environments, we propose a task-oriented broadcast communication system that enables an edge transmitter to serve multiple edge devices with heterogeneous inference tasks. The proposed system adopts a two-phase design inspired by Marton’s channel coding with rate splitting and the information bottleneck principle. In the first phase, a common feature vector is extracted to capture the shared information across tasks. In the second phase, task-specific private feature vectors are generated conditioned on the common feature to preserve unique task-relevant information. To facilitate interference-robust task execution, our scheme leverages the intrinsic structural alignment between the task correlations and broadcast channel properties; specifically, the common and private features are mapped directly to Marton’s common and private codewords. A variational approximation method is introduced to optimize the feature extraction process in both phases, allowing for compact and informative representations while reducing redundant data transmission. Extensive experiments on a real-world multi-label dataset demonstrate that the proposed method achieves superior inference accuracy and robustness over wireless networks, compared to traditional digital compression and deep learning-based joint source-channel coding schemes. These results confirm the potential of task-oriented design for scalable and reliable edge intelligence.

Currently, balancing low bitrate coding with speech quality is a highly debated topic in the research community. At very low bitrates, existing methods often fail to maintain speech naturalness, intelligibility, and personalization. To address this issue, we introduce an innovative ultra-low bitrate semantic speech coding approach, termed Semantic Speech Coding (SSC). Specifically, the multi-level feature extraction and compression mechanism sequentially extracts and compresses speech features at different levels, ensuring speech quality at ultra-low bit rates. Using a semantic vector quantization codec to fuse spectral and pitch features to extract essential semantic information, achieving more efficient compression while enhancing intelligibility and naturalness. The low-data-overhead speaker feature encoder captures time-invariant speaker characteristics, enabling personalized speech synthesis without additional data overhead, ensuring the synthesized speech retains personalization and naturalness. The diffusion loss mechanism employs a conditional diffusion model to progressively restore details, mitigating the detail loss typically seen in conventional codecs, further enhancing the naturalness and realism of the synthesized speech. We achieved significant improvements in speech quality at an ultra-low bitrate of 106 bps, which approaches the theoretical upper limit of information rate.

Recent contributions of semantic information theory reveal the set-element relationship between semantic and syntactic information, represented as synonymous relationships. In this paper, we propose a synonymous variational inference (SVI) method based on this synonymity viewpoint to re-analyze the perceptual image compression problem. It takes perceptual similarity as a typical synonymous criterion to build an ideal synonymous set (Synset), and approximate the posterior of its latent synonymous representation with a parametric density by minimizing a partial semantic KL divergence. This analysis theoretically proves that the optimization direction of perception image compression follows a triple tradeoff that can cover the existing rate-distortion-perception schemes. Additionally, we introduce synonymous image compression (SIC), a new image compression scheme that corresponds to the analytical process of SVI, and implement a progressive SIC codec to fully leverage the model’s capabilities. Experimental results demonstrate comparable rate-distortion-perception performance using a single progressive SIC codec, thus verifying the effectiveness of our proposed analysis method.

Airway mucus plugs are a key clinical feature of respiratory diseases. Their precise identification and evaluation significantly impact asthma management, aiding diagnosis, treatment planning, and outcome prediction. Current clinical detection based on manual CT scan inspection remains time consuming and prone to subjective bias. Although deep learning methods have shown promise in mucus plug detection, existing solutions still face performance limitations. These challenges primarily stem from the similarity of mucus plugs to vascular structures and airways, combined with their morphological heterogeneity and minuscule target sizes. To overcome these limitations, we propose a novel detection model called the Two-Stream Mucus Plug Detector (TSMPD) based on Faster R-CNN architecture. Our model employs two parallel stream: The first stream conducts inter-slice differential analysis through bivariant attention to highlight CT feature transition zones, particularly capturing both intensity gradients and morphological alterations like boundary thickening and blurring. The second stream performs intra-slice grayscale analysis by leveraging density variations for rapid mucus region of interest masking, significantly narrowing the candidate search space. Experimental results show that the proposed method outperforms previous detection methods, with the potential for clinical implementation in CT imaging systems.

Analyzing interactions among multiple medical time-series variables is essential for biomarker discovery and clinical decision-making; however, traditional statistical methods struggle with the diverse, nonlinear, and temporal nature of such data, and deep learning techniques often lack interpretable time-series relationships. To address these limitations, we propose Covariance-Variational Autoencoder (Cov-VAE), which extends the Variational Autoencoder (VAE) framework by relaxing the independent latent distribution assumption, incorporating covariance estimation to compute mutual information among variables, and embedding a variable-level attention mechanism within the encoder–decoder architecture to reveal complex interactions. On a real-world medical time-series dataset, Cov-VAE outperforms the standard VAE and other dimensionality reduction models by achieving notable improvements in mean absolute error (MAE), mean squared error (MSE), and Kullback–Leibler (KL) divergence. Furthermore, by constructing a multivariate mutual information–based variable relationship graph, our method identifies and validates critical combinations of medical variables, outperforming existing approaches. This work delivers a powerful, interpretable method for complex time-series multivariable analysis in healthcare.

This paper introduces a novel parity-consistent decomposition (PCD) algorithm for accurately determining the weight distribution (WD) of polar codes. In the PCD algorithm, polar codes are represented as a union of a new type of polar cosets, which avoids fixing two adjacent information bits as 0 and 1. This representation reduces the number of polar cosets compared to the existing automorphism-based algorithm. Furthermore, we introduce a recursive decomposition method for this new polar coset, which facilitates efficient recursive computation of the WD of polar codes. The PCD algorithm is organized within a tree-like structure, which facilitates the evaluation of its computational complexity. Comparative analysis with the automorphism-based method demonstrates that the PCD algorithm significantly reduces computational complexity.

Affine frequency division multiplexing (AFDM) has emerged as a promising waveform for high-mobility communication systems, whose performance in doubly dispersive channels relies on accurate channel estimation. However, conventional AFDM channel estimation schemes exhibit significant limitations. Therefore, we propose a low-complexity channel estimation scheme which adopts different pilot designs for integer and fractional Doppler cases. Specifically, the generalized approximate message passing (GAMP) algorithm is employed to replace the expectation step of the sparse Bayesian learning algorithm based on expectation maximization (EM), thereby reducing computational complexity. Extensive simulation results demonstrate that, compared with conventional methods, the proposed scheme not only offers advantages in pilot power consumption and overhead, but also achieves excellent performance and low complexity in various Doppler cases.

Affine Frequency Division Multiplexing (AFDM) is considered as a promising solution for next-generation wireless systems due to its satisfactory performance in high-mobility scenarios. By adjusting AFDM parameters to match the multi-path delay and Doppler shift, AFDM can achieve two-dimensional time-frequency diversity gain. However, under fractional delay-Doppler channels, AFDM encounters energy dispersion in the affine domain, which poses significant challenges for signal detection. This paper first investigates the AFDM system model under fractional delay-Doppler channels. To address the energy dispersion in the affine domain, a unitary transformation based approximate message passing (UAMP) algorithm is proposed. The algorithm performs unitary transformations and message passing in the time domain to avoid the energy dispersion issue. Additionally, we implemented block-wise processing to reduce computational complexity. Finally, the empirical extrinsic information transfer (E-EXIT) chart is used to evaluate iterative detection performance. Simulation results show that UAMP significantly outperforms Gaussian approximate message passing (GAMP) under fractional delay-Doppler conditions.

Rate-splitting multiple access (RSMA) is a flexible multiple access scheme that leverages the advantages of superposition coding to enhance user throughput, making it particularly advantageous for Internet of Things (IoT) scenarios. In this article, we consider practical coding and modulation schemes, and develop an analytical method to analyze effective throughput with finite block lengths and QPSK modulation. First, an analysis of the polarization effect between users in the RSMA system is conducted, with the impact of finite length coding taken into consideration. Thereafter, the effective throughput is proposed as the optimization objective. Effective throughput, which accurately reflects both system throughput and error probability, is a pivotal metric for designing IoT systems. Subsequently, to address this optimization problem, we propose an alternating optimization method that leverages successive convex approximation (SCA) and convex optimization in an iterative manner to find feasible solutions. Utilizing the optimized coding rates and precoding matrices, we design a polar-coded RSMA scheme for IoT application. Numerical results demonstrate that under QPSK modulation, RSMA can achieve up to a 33.08% gain in effective throughput compared with NOMA/SDMA, while polar-coded RSMA can reach up to 98.71% of the optimal performance target.

End-to-end visual communication systems typically optimize a trade-off between channel bandwidth costs and signal-level distortion metrics. However, under challenging physical conditions, this traditional coding and transmission paradigm often results in unrealistic reconstructions with perceptible blurring and aliasing artifacts, despite the inclusion of perceptual or adversarial losses for optimizing. This issue primarily stems from the receiver’s limited knowledge about the underlying data manifold and the use of deterministic decoding mechanisms. To address these limitations, this paper introducesDiffCom, a novel end-to-endgenerative communicationparadigm that utilizes off-the-shelf generative priors and probabilistic diffusion models for decoding, thereby improving perceptual quality without heavily relying on bandwidth costs and received signal quality. Unlike traditional systems that rely on deterministic decoders optimized solely for distortion metrics, ourDiffComleverages raw channel-received signal as a fine-grained condition to guide stochastic posterior sampling. Our approach ensures that reconstructions remain on the manifold of real data with a novel confirming constraint, enhancing the robustness and reliability of the generated outcomes. Furthermore,DiffComincorporates a blind posterior sampling technique to address scenarios with unknown forward transmission characteristics. Extensive experimental validations demonstrate thatDiffComnot only produces realistic reconstructions with details faithful to the original data but also achieves superior robustness against diverse wireless transmission degradations. Collectively, these advancements establishDiffComas a new benchmark in designing generative communication systems that offer enhanced robustness and generalization superiorities.

In this paper, we propose “SoundSpring”, a cutting-edge error-resilient audio transceiver that marries the robustness benefits of joint source-channel coding (JSCC) while also being compatible with current digital communication systems. Unlike recent deep JSCC transceivers, which learn to directly map audio signals to analog channel-input symbols via neural networks, our SoundSpring adopts the layered architecture that delineates audio compression from digital coded transmission, but it sufficiently exploits the impressive in-context predictive capabilities of large language (foundation) models. Integrated with the casual-order mask learning strategy, our single model operates on the latent feature domain and serve dual-functionalities: as efficient audio compressors at the transmitter and as effective mechanisms for packet loss concealment at the receiver. By jointly optimizing towards both audio compression efficiency and transmission error resiliency, we show that mask-learned language models are indeed powerful contextual predictors, and our dual-functional compression and concealment framework offers fresh perspectives on the application of foundation language models in audio communication. Through extensive experimental evaluations, we establish that SoundSpring apparently outperforms contemporary audio transmission systems in terms of signal fidelity metrics and perceptual quality scores. These new findings not only advocate for the practical deployment of SoundSpring in learning-based audio communication systems but also inspire the development of future audio semantic transceivers.

We consider the turbo frequency domain equalization (FDE) approach based on expectation propagation (EP) to the single-carrier with index modulation (SCIM) transmission over frequency-selective channels. The SCIM-FDE system implements vector-by-vector signal transmission in the time domain, in which elements within each transmitted symbol vector display correlation. In this paper, we propose a novel vector-wise EP (VW-EP) equalization, where the discrete prior distribution of each symbol vector is approximated with a multivariate complex Gaussian distribution. Hence the extrinsic information in the EP feedback is extended to be in terms of the mean vector and covariance matrix. The effectiveness of the proposed scheme can be validated through the error-correction performance and extrinsic information transfer (EXIT) analysis. Numerical results show that the SCIM-FDE transmission with the VW-EP turbo equalization demonstrates remarkable performance.

The forthcoming sixth-generation (6G) wireless system brings forth the need to meet increasingly stringent performance criteria, necessitating refinement and advanced design of channel coding and modulation techniques. Within this context, polar codes stand out as a competitive candidate due to their favorable attributes. This study delves into probabilistically-shaped polar-coded modulation (PS-PCM) for its potential to enhance spectral efficiency and error-correction performance, offering a pragmatic design geared toward standardization. Specifically, we present a coding chain for PS-PCM that ensures backward compatibility with legacy 5G polar codes. We also propose, for the first time, a channel-independent method for constructing PS-PCM that eliminates the need for on-the-fly computations, which is an imperative attribute for practical system implementation. The core of this code construction method is the utilization of a surrogate channel to allocate rates to each component code while considering the varying probability distributions and protection levels of label bits. The assigned component code rates can be seamlessly integrated into the modulation and coding scheme (MCS) table to facilitate rapid construction for both base stations and user equipment. Furthermore, we devise flexible rate matching methods to allow fine granularity adjustment of rates and blocklengths for PS-PCM. Simulation results validate the efficacy of our proposed approaches, which also provide insights and benchmarks for the capability of PS-PCM to meet requirements for enhanced spectral efficiency, higher reliability, and more flexibility.

With the widespread adoption of massive multiple-input multiple-output (MIMO) and millimeter wave (mmWave) communication techniques, the overhead of beam measurement and the complexity of beam management become even more severe issues due to the dramatic increase of the number of beams. Traditional methods often overlook the selection of optimal probing beams, thus limiting beam prediction performance. Additionally, existing solutions based on deep learning exhibit high complexity, which often hinders their practical deployment. In this work, we propose a lightweight, integrated neural network approach tailored for joint probing-beam pattern selection and beam prediction. Specifically, our solution includes two main components. First, formulating the selection of probing beams as a sampling operation, we envision a sampling network where, to enable gradient back-propagation of network parameters in spite the non-differentiable nature of sampling, the standard sampling function is approximated with a fitting function. Then, drawing inspiration from the physical structure of antenna arrays and 3D beam formation, we develop a beam-prediction network based on convolutional neural networks and self-attention mechanisms. Experimental results demonstrate that, thanks to the learned pattern, our proposed scheme achieves very good prediction performance (exceeding the state of the art by over 15% in top-1 accuracy), using a neural network with almost 90% less parameters than existing machine learning-based solutions.

Recent advancements in neural image codecs (NICs) are of significant compression performance, but limited attention has been paid to their error resilience. These resulting NICs tend to be sensitive to packet losses, which are prevalent in real-time communications. In this paper, we investigate how to elevate the resilience ability of NICs to combat packet losses. We propose ResiComp, a pioneering neural image compression framework with feature-domain packet loss concealment (PLC). Motivated by the inherent consistency between generation and compression, we advocate merging the tasks of entropy modeling and PLC into a unified framework focused on latent space context modeling. To this end, we take inspiration from the impressive generative capabilities of large language models (LLMs), particularly the recent advances of masked visual token modeling (MVTM). In specific, ResiComp develops a bi-directional masked Transformer to model the contextual dependencies among latents with dual-functionality: 1) iteratively acts as a conditional entropy model to boost compression efficiency; 2) operates latent PLC to improve resilience. During training, we integrate MVTM to mirror the effects of packet loss, enabling a dual-functional Transformer to restore the masked latents by predicting their missing values and conditional probability mass functions. Our ResiComp jointly optimizes compression efficiency and loss resilience. Moreover, ResiComp provides flexible coding modes, allowing for explicitly adjusting the efficiency-resilience trade-off in response to varying Internet or wireless network conditions. Extensive experiments demonstrate that ResiComp can significantly enhance the NIC’s resilience against packet losses, while exhibits a worthy trade-off between compression efficiency and packet loss resilience. Additionally, packet-level simulations, conducted using diverse network models based on real traces, demonstrate that ResiComp exhibits much better robustness to fluctuating network conditions compared to redundancy-based approaches like VTM + FEC.

Automatic segmentation of Organs-At-Risk (OARs) and Clinical Target Volume(CTV) is crucial for the radiotherapy treatment planning of cervical cancer. This task is challenging due to the variation in sizes, shapes, and positions as well as the similar textures among the OARs and CTV. In this paper, we propose a manifold learning-based method based on U-Net. Firstly, the weight matrix of each convolutional layer is constrained to the Stiefel manifold. This constraint enhances the model’s ability to preserve the consistency of the learned feature from CT images. Secondly, we transform the optimization in Euclidean space into Riemannian optimization. This enables the model to optimize the segmentation performance on the manifold space, allowing the model to adapt to the irregular shapes of CTV and OARs. Our experimental results demonstrate that the proposed manifold learning-based method achieves superior performance in segmenting OARs and CTV for cervical cancer as compared to other SOTA methods. Overall, our proposed method demonstrates the potential of manifold learning techniques to improve the segmentation performance of medical images.

The multiple-input multiple-output orthogonal time frequency space (MIMO-OTFS) has emerged as a promising scheme for high-mobility wireless communications. The unitary approximate message passing (UAMP) detection has demonstrated outstanding performance in the single-input single-output (SISO)-OTFS system. However, We found that directly employing the UAMP detection for the MIMO-OTFS system results in performance degradation and the emergence of an error floor. Therefore, we propose a minimum mean square error (MMSE)-aided UAMP scheme, incorporating MMSE to assist UAMP by providing the prior information. Notably, we reduce the complexity by setting a signal-to-noise ratio (SNR) threshold, only when SNR exceeds the threshold shall we start the MMSE-aided mechanism. Simultaneously, we analyze the impact of fractional delay and consider it into system model, aiming to optimize the practicality of the system. Simulation results under fast-fading Rayleigh channels demonstrate that the proposed MMSE-aided UAMP significantly outperforms the original UAMP in the medium to high SNR range.

In this paper, we introduce a deep learning-based beamspace channel estimation approach that better exploits the inherent sparsity of the mmWave MIMO channel. On one hand, we replace conventional complicated phase shifter networks with switch-based selection networks, whose sparse connectivity is more adapted to the sparsity of mm Wave channels. On the other hand, we propose an attention-Unet model for accurate beamspace channel estimation. The architecture comprises an encoder-decoder structure with attention mechanism. By selectively focusing on the dominant part, the attention mechanism can further capture the sparsity of the beamspace channel. Simulation results demonstrate that the proposed approach outperforms the existing phase shifter-based techniques under both the widely used Saleh-Valenzuela channel model and the open-source DeepMIMO dataset based on ray-tracing.

In this paper, we propose an integrated polar coding scheme for both energy adaption and reliable transmission to enhance the existing results of joint energy and information transfer. More specifically, the encoding process is carried out by an energy adaptive encoder and a polar encoder. The energy adaptive encoder is implemented by a polar decoder to determine a subset of the bits, ensuring that the codewords passed through the polar encoder have an optimal distribution that achieves the energy-capacity function. The bits are allocated based on the conditional entropy derived from source and channel polarization. At the receiver, we slightly modify the decoder according to the prior distribution. Numerical simulations show that lk-block-length polar code achieves a gain of roughly or greater than 0.5 dB compared to the 100k-block-length low-density parity-check (LDPC) counterpart at a bit error rate (BER) of 10−3.

In this article, an improved Bose-Chaudhuri–Hocquenghem (BCH)-Polar concatenated unsourced random-access coding scheme is considered for the Internet of Things system. At each active user side, a concatenated random-access code is used, where the outer code is denoted as a BCH-based T-fold code, which is constructed by using the parity-check matrix of a BCH code to distinguish no more than T user packets, and a polar code is used as the inner code. At the receiver side, a novel T-fold aided successive-cancelation list (TA-SCL) decoding approach is proposed that exploits the information transferred between the inner- and outer-decoding of the BCH-Polar concatenated random-access code. The TA-SCL decoding approach resorts to indicators of the T-fold decoding to aid in the survival path selection of the successive cancelation list (SCL) decoding for the polar code. Within the Berlekamp-Massey algorithm (BMA) decoding procedure of the T-fold code, its input, whether it is in an error-free case or contains more than T users or not, can be determined by its output indicators. Therefore, these indicators of the outer BMA can play a genius role in the last-survival path selection for the inner SCL decoding. Simulation results show that, compared with the traditional method, the proposed TA-SCL decoding approach can improve the$E_{b}/N_{0}$performance for the BCH-Polar concatenated scheme in the uncoordinated Gaussian multiple-access channel by about 1 dB with the computational complexity cost times increased by much more less than the size of SCL list.

This paper conducts the first analysis of the bit error probabilityPbof polar codes under successive cancellation (SC) decoding, with a focus on information bits. We introduce the concept of component bit error probabilityPb(i), which corresponds to thei-th stage of SC decoding, andPbis computed as the sum of allPb(i) over the information set. To facilitate the evaluation ofPb(i) andPb, we propose an analytical approach that leverages the algebraic structure of a particular type of polar codes termed polar subcodes. This approach allows us to derive closed-form approximations for the upper bounds on bothPb(i) andPb. Our analysis applies to both systematic and non-systematic polar codes, where we further investigate the homogeneity and introduce the input-output weight distribution of polar subcodes to obtain more concise expressions for the proposed approximate upper bounds. Experimental results are also presented to validate the effectiveness of these approximations.

Major adverse cardiovascular events (MACE) encompass pivotal cardiovascular outcomes such as myocardial infarction, unstable angina, and cardiovascular-related mortality. Patients undergoing peritoneal dialysis (PD) exhibit specific cardiovascular risk factors during the treatment, which can escalate the likelihood of cardiovascular events. Hence, the prediction and key factor analysis of MACE have assumed paramount significance for peritoneal dialysis patients. Current pathological methodologies for prognosis prediction are not only costly but also cumbersome in effectively processing electronic health records (EHRs) data with high dimensionality, heterogeneity, and time series. Therefore in this study, we propose the CVEformer, an attention-based neural network designed to predict MACE and analyze risk factors. CVEformer leverages the self-attention mechanism to capture temporal correlations among time series variables, allowing for weighted integration of variables and estimation of the probability of MACE. CVEformer first captures the correlations among heterogeneous variables through attention scores. Then, it analyzes the correlations within the time series data to identify key risk variables and predict the probability of MACE. When trained and evaluated on data from a large cohort of peritoneal dialysis patients across multiple centers, CVEformer outperforms existing models in terms of predictive performance.

Multiple access technologies are critical technologies in every communication era. As a promising paradigm for next-generation mobile communication, semantic communication has explored new semantic information space resources. Based on the characteristic that different semantic models cannot understand semantic information generated by other models, we propose the concept of semantic orthogonal signals. Combining the advantages of Deep joint source and channel coding (DeepJSCC), an Orthogonal-Model Division Multiple Access (O-MDMA) technology that can be applied to any semantic model is proposed. The essence of O-MDMA is to migrate the anti-interference capability of DeepJSCC to the multi-user capacity. Compared with Non-Orthgonal Multiple Access (NOMA) and Model Division Multiple Access (MDMA) technologies, O-MDMA has better performance. The O-MDMA can be integrated with NOMA, and experimental results show that the combined technique can save more bandwidth.

In this paper, we propose a new class of high-efficiency semantic coded transmission methods to realize end-to-end speech transmission over wireless channels. We name the whole system as Deep Speech Semantic Transmission (DSST). Specifically, we introduce a nonlinear transform to map the speech source to semantic latent space and feed semantic features into source-channel encoder to generate the channel-input sequence. Guided by the variational modeling idea, we set an entropy model on the latent space to estimate the importance diversity among semantic feature embeddings. Accordingly, these semantic features of different importance can be reasonably allocated with different coding rates, which maximizes the system coding gain. Furthermore, we introduce a channel signal-to-noise ratio (SNR) adaptation mechanism such that a single model can be applied over various channel states. The end-to-end optimization of our model leads to a flexible rate-distortion (RD) tradeoff, supporting an adaptive rate wireless speech semantic transmission. Experimental results verify that our DSST system clearly outperforms current engineered speech transmission systems on both objective and subjective metrics. Compared with existing neural speech semantic transmission methods, our model saves up to 75% of channel bandwidth costs when achieving the same quality. Audio samples are available at https://ximoo123.github.io/DSST.

In this paper, we aim to redesign the vision Transformer (ViT) as a new backbone to realize semantic image transmission, termed wireless image transmission transformer (WITT). Previous works build upon convolutional neural networks (CNNs), which are inefficient in capturing global dependencies, resulting in degraded end-to-end transmission performance especially for high-resolution images. To tackle this, the proposed WITT employs Swin Transformers as a more capable backbone to extract long-range information. Different from ViTs in image classification tasks, WITT is highly optimized for image transmission while considering the effect of the wireless channel. Specifically, we propose a spatial modulation module to scale the latent representations according to channel state information, which enhances the ability of a single model to deal with various channel conditions. As a result, extensive experiments verify that our WITT attains better performance for different image resolutions, distortion metrics, and channel conditions. The code is available at https://github.com/KeYang8/WITT.

Humans tend to focus on only a few regions of interest (ROI) rather than perceiving the entire scene. This insight is also useful for machine tasks. Built upon the properties of ROI, in this paper, we propose a learned image transmission framework toward machine tasks, which ensures both the image reconstruction quality and the task accuracy. The whole system is optimized under a tripartite RDA tradeoff across the channel bandwidth cost (rate, R), the signal reconstruction quality (distortion, D), and the machine task performance (accuracy, A). According to the image content complexity distribution and the specific task, we incorporate both the entropy model and the ROI map to guide the source-channel coding rate allocation. As a result, we obtain the system coding gain. During this process, we develop two types of real-time ROI generation methods, suitable for high and low bandwidth cost regions, respectively. Experimental results show that our approach vastly outperforms state-of-the-art engineered image transmission methods and emerging image transmission methods. Moreover, we conduct an extensive ablation study to demonstrate the importance of individual components in our method, by which we expect to facilitate future research on this novel approach for machine-type semantic communications.

Learned image transmission (LIT) has shown promising progress in recent years to boost the end-to-end transmission performance in semantic communications. To further enhance the system efficiency, in this paper, we propose a novel LIT framework built on multiple-input multiple-output (MIMO) fading channels. In particular, the proposed framework supports concurrent transmission of multiple streams, which can maximize the multiplexing gain in end-to-end semantic communication systems. By jointly considering the entropy distribution of the image semantic features and the wireless MIMO channel states, we design a spatial multiplexing mechanism that can adaptively realize coding rate allocation and stream mapping. As a result, source content and channel environment will be seamlessly coupled, which maximizes the coding gain. Moreover, the proposed LIT model is versatile: a single model can support various transmission rates. The whole model is optimized under the constraint of transmission rate-distortion (RD) tradeoff. Experimental results verify that our scheme substantially increases the throughput of semantic communication systems, and outperforms traditional MIMO communication systems under realistic fading channels.

Guessing random additive noise decoding (GRAND) is a recently proposed decoding algorithm which can achieve the error performance of maximum likelihood (ML) decoding. However, GRAND and its variants are only suitable for some short codes with high code rates and have large average query numbers. To mitigate these problems, we propose a successive cancellation list (SCL)-GRAND decoding algorithm for the cyclic redundancy check concatenated polar (CRC-polar) codes. The proposed decoder first divides the received sequence into two subblocks. Then SCL is used to decode the upper subblock and output several candidates into the candidate list. For each candidate, GRAND is used to decode the lower subblock and finally choose the most-likely codeword as the decoded result. Since the SCL is integrated into the SCL-GRAND algorithm, this algorithm can achieve lower complexity and better flexibility than the original GRAND.

We propose a novel neural waveform compression method to catalyze emerging speech semantic communications. By introducing nonlinear transform and variational modeling, we effectively capture the dependencies within speech frames and estimate the probabilistic distribution of the speech feature more accurately, giving rise to better compression performance. In particular, the speech signals are analyzed and synthesized by a pair of nonlinear transforms, yielding latent features. An entropy model with hyperprior is built to capture the probabilistic distribution of latent features, followed by quantization and entropy coding. The proposed waveform codec can be optimized flexibly towards arbitrary rate, and the other appealing feature is that it can be easily optimized for any differentiable loss function, including perceptual loss used in semantic communications. To further improve the speech quality, we incorporate residual coding to mitigate the degradation arising from quantization distortion at the latent space. Results indicate that achieving the same perceptual quality score, the proposed method saves up to 27% coding rate than widely used adaptive multi-rate wideband (AMR-WB) codec as well as emerging neural waveform coding methods.

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.

In a multi-user system, system resources should be allocated to different users. In traditional communication systems, system resources generally include time, frequency, space, and power, so multiple access technologies such as time division multiple access (TDMA), frequency division multiple access (FDMA), space division multiple access (SDMA), code division multiple access (CDMA), and non-orthogonal multiple access (NOMA) are widely used. In semantic communication, which is considered a new paradigm of the next-generation communication system, we extract high-dimensional features from signal sources in a model-based artificial intelligence approach from a semantic perspective and construct a model information space for signal sources and channel features. From the high-dimensional semantic space, we excavate the shared and personalized information of semantic information and propose a novel multiple access technology, named model division multiple access (MDMA), which is based on the resource of the semantic domain. From the perspective of information theory, we prove that MDMA can attain more performance gains than traditional multiple access technologies. Simulation results show that MDMA saves more bandwidth resources than traditional multiple access technologies, and that MDMA has at least a 5-dB advantage over NOMA in the additive white Gaussian noise (AWGN) channel under the low signal-to-noise (SNR) condition.

Generating food images from recipe and ingredient information can be applied to many tasks such as food recommendation, recipe development, and health management. For the characteristics of food images, this paper proposes ML-CookGAN, a novel CGAN. This network enables the generation of food images based on recipe and ingredient labels. The generator of ML-CookGAN, Multi-Label Fusion Generator, converts recipe and ingredient labels into different granularity features and generates corresponding food images. The discriminator of ML-CookGAN, Multi-Branch Discriminator, implements discrimination and classification with a multi-branch structure. In addition, we propose two training strategies, Region-Wise Pooling and Image Style Distillation, to better the network performance. Region-Wise Pooling handles region-wise features with the discriminator. Image Style Distillation aims at extracting image latent features to assist image generation by an unsupervised method. The experiments conducted on VIREO Food-172 databases validate the proposed method to generate high-quality Chinese food images. And Region-Wise Pooling and Image Style Distillation are proven to enhance the diversity and realism of generated food images.

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.

The recent deep learning-based joint source-channel coding (deep JSCC) framework has shown superior performance on end-to-end wireless image transmission without suffering from the “cliff effect”. However, a fundamental limit of current deep JSCC schemes is that the unbalanced regional importance of the source image has not been explicitly taken into account. It evenly distributes the coding rate to every image patch leading to an evident degradation of the overall coding efficiency. To break this fundamental limit, we propose a novel end-to-end wireless image transmission scheme in this paper. Our scheme integrates the deep JSCC architecture and the quadtree-structured regional rate allocation strategy adopted in the HEVC standard, collected under the name “resolution-adaptive deep JSCC (RaDJSCC)”. Our new architecture perceives the content of the transmitted image and adaptively allocates more channel bandwidth to the complex pixel blocks. Results show that for high-resolution images, the proposed RaDJSCC transmission method generally outperforms the emerging analog transmission schemes using deep JSCC and the digital transmission schemes using classical separated source and channel coding, e.g., BPG + LDPC.

We study the problem of deep joint source-channel coding (D-JSCC) for correlated image sources, where each source is transmitted through a noisy independent channel to the common receiver. In particular, we consider a pair of images captured by two cameras with probably overlapping fields of view transmitted over wireless channels and reconstructed in the center node. The challenging problem involves designing a practical code to utilize both source and channel correlations to improve transmission efficiency without additional transmission overhead. To tackle this, we need to consider the common information across two stereo images as well as the differences between two transmission channels. In this case, we propose a deep neural networks solution that includes lightweight edge encoders and a powerful center decoder. Besides, in the decoder, we propose a novel channel state information aware cross attention module to highlight the overlapping fields and leverage the relevance between two noisy feature maps. Our results show the impressive improvement of reconstruction quality in both links by exploiting the noisy representations of the other link. Moreover, the proposed scheme shows competitive results compared to the separated schemes with capacity-achieving channel codes.

The existing indoor fingerprinting methods based on received signal strength (RSS) are rather accurate after intensive offline calibration for a specific scenario, but the well-calibrated localization model (can be a pure statistical one or a data-driven one) will present poor generalization ability in a new scenario, which results in big loss in knowledge and human effort. To break the scenario-specific localization bottleneck, we propose a new-fashioned data-driven fingerprinting method for localization based on meta-learning, named by MetaLoc, that can adapt itself rapidly to a new, possibly unseen, scenario with very little calibration work. Specifically, the underlying localization model is taken to be a deep neural network (NN), and we train an optimal set of group-specific meta-parameters by leveraging historical data collected from diverse well-calibrated indoor scenarios and the maximum mean discrepancy criterion. Simulation results confirm that the meta-parameters obtained for MetaLoc achieves very rapid adaptation to new scenarios, competitive localization accuracy, and high resistance to significantly reduced reference points (RPs), saving a lot of calibration effort.

In this paper, we propose a new class of distributed joint source-channel coding (DJSCC) methods, namely triple polar codes (T-PC), for transmitting a pair of correlated binary sources over noisy channels. In the T-PC structure, one source is protected by a systematic polar code (SPC), and the other source is encoded into a double polar code (D-PC) word. Following this, we prove the T-PC approaches the corner point of the achievable rate-region of DJSCC. We further propose a distributed joint source-channel decoding algorithm, which involves two components: a cyclic redundancy check (CRC) aided successive cancellation list (CA-SCL) decoding of the SPC and a joint successive cancellation list (J-SCL) decoding of the D-PC. The CA-SCL and J-SCL decoding procedures alternately generate hard-decisions of sources which are iteratively exchanged as the side information and result in superior performance compared with the state-of-the-art polar code based DJSCC scheme.

In this paper, we investigate the joint design and optimization of source-channel polar coding with 2m-ary transmission. A joint framework is proposed which includes a source polar code to compress the redundant source, followed by a set of component polar codes over a 2m-ary modulation to protect the source against errors and achieve increased spectral efficiency. We prove that our scheme suffices to achieve the theoretical limit of source-channel separation theorem. For finite-length cases, a joint decoder that exploits both the residual redundancy and channel characteristics is also derived to further reduce the error rate. Simulation results verify the effectiveness of the scheme.

At the present stage, most of the packet header detection algorithms in massive access are to solve the problem of multi-user collision in the additive white gaussian noise (AWGN) channel. On the other hand, many algorithms do not consider that the number of user collisions is unknown and varies randomly. First, we propose an adaptive algorithm based on correlation. Secondly, based on the coordinate ascent variational inference (CAVI) algorithms, we present an improved header detection method in fading channels. Compared with the traditional packet head detection method based on compressive sensing reconstruction algorithm, this improved method solves the problem that the number of users who collide is unknown, and it also has good performance in fading channels. In addition, the method can also cope with the random change of the number of users in collision.

This paper proposes an improved version of concatenated code scheme in massive random access, with performance enhancement and low-complexity implementation. Under theoretical idealization, most existing solutions only consider improving energy efficiency, ignoring the cost of implementation complexity. However, both of them are vital when it comes to practical realization. Although the concatenated code system is simple to implement, its performance is relatively weak, and the equivalent mapping detection method adopted at the receiving end causes performance loss. Therefore, based on concatenated code system, this paper makes performance improvements in the following three aspects: (i) using multi-level decisions to avoid loss of information; (ii) a priori information aided detection utilizing the distribution of multi-level symbols to minimize the error probability of detection; (iii) soft information output to enhance soft decoders at the next stage. Meanwhile, in decoding complexity, the improved algorithm retains the low complexity feature of the concatenated code system and achieves linear implementation. This scheme successfully enhances energy efficiency at a lower complexity cost by improving the detection algorithm. We prove that the proposed method is an implementation-oriented massive random access with advantages in energy efficiency and performance by theory and simulation.

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.

In this paper, we propose a block polarization (BP) scheme as a framework to study polar-coded hybrid automatic repeat request (Polar-HARQ) under bit-interleaved polar-coded modulation (BIPCM). The BP scheme of BIPCM can combine multiple independent polar subcodes to form a longer polar code under the block polarization between polar subcodes. The BP scheme of HARQ further carries out block polarization based on the BP scheme of BIPCM, which can combine multiple transmitted BP schemes of BIPCM together to form a longer polar code. When the underlying channels are non-uniform, such as the polarization effect of high-order modulation will lead to non-uniform reliabilities, it is not suitable to use the fixed sequence (e.g., the polarization weight (PW) sequence or Polar sequence in the 5G standard) to construct polar codes. The underlying channels on each polar subcode have uniform reliability by dividing different blocks according to the reliabilities of the underlying channel. Based on this characteristic of BP scheme, we propose a rate-allocation (RA) method to study fast code construction of BP scheme. The key idea of the proposed RA method is finding out the equivalent channel whose average symmetric capacity equals the target transmission rate. The allocated rates are computed according to the symmetric capacities of split channels under the block polarization between polar subcodes. Then the ranking of bit indices within each polar subcode can be obtained by fixed sequence. In this way, the RA code construction is concise and robust for diverse configurations which are the desired features for practical implementation. Simulation results show that the proposed BP scheme under RA construction can achieve almost identical performance as the Gaussian approximation construction with much lower complexity.

Cell-free (CF) millimeter-wave (mmWave) Massive multiple-input multiple-output (MIMO) will be a promising technology in the next-generation mobile communication system. Traditionally, beam selection is widely studied in the single-cell scenario for analog beamforming under hybrid beamforming architectures. Meanwhile, there is also a great necessity to study beam selection in CF systems featuring multiple access points (APs). In this paper, we propose two beam selection schemes for the hybrid CF mmWave massive MIMO systems. First, by suppressing both the intra-AP and inter-AP interference of beams, the proposed capacity maximization-based selection scheme (CM-S) aims to maximize the system sum rate. Second, utilizing the proportional fairness (PF) scheduling algorithm, our proposed scheme of joint user scheduling and beam selection (JDSBS) iteratively performs user selection and beam selection in several continuous slots, which reduces the user's outage probability by more than 10 times with little performance loss. Finally, the simulation results show the advantages of the proposed schemes and indicate that our proposed schemes can achieve better performance when users are dense.

We analyze the prediction confidence heat map of current state-of-the-art models in medical image segmentation. We find that low confidence usually comes with a high probability of error. Based on this observation, we propose a novel Recurrent Recalibration Network (R2Net) for medical images segmentation. Specifically, the decoder will forward multiple times in a recurrent manner while the encoder forward only once to extract features during training time. Except for the first time, each decoder input features at time t will be recalibrated by the confidence heat maps which were output from the decoder at previous time t −1. As a result, R2Net can learn to focus on the regions which are error-prone to segment. The recurrent manner makes the feature reusable and saves the parameters. We validate the proposed models on two benchmark datasets: multiorgan segmentation and cardiac organ segmentation datasets. The experimental results show that our Recurrent Recalibration scheme consistently improves the segmentation performance of various models while preserving computational efficiency.

Orthogonal time-frequency space signaling (OTFS) is a novel modulation scheme that can exploit full time-frequency (TF) diversity when coupled with a proper receiver. Toward this end, this paper presents a joint channel estimation and equalization approach for OTFS based on the expectation propagation (JCEE-EP). First, We apply discrete prolate spheroidal basis expansion model (DPS-BEM) to describe the time-varying (TV) channel and obtain a Gaussian prior of BEM coefficients via LS estimator. Based on the delay-Doppler (DD) domain to time domain transformation, we then decompose the joint posterior probability into a couple of factors and approximate each one by Gaussian distributions according to the EP principle. As the moment-matching (MM) step in channel factor update is computationally intractable, we resort to the first-order conditional EP (CEP) method for an approximate solution. Finally, the bit error rate (BER) simulation results demonstrate the superiority of our proposed JCEE-EP over the existing approaches.

In this paper, the problem of neural compression based image transmission over wireless channels is studied. Since all procedures are considered over wireless links, the quality of training is affected by wireless factors such as packet errors. In the considered model, compressed data given by the neural source encoder (NSE) are fed into an error-control channel encoder and modulated as discrete symbols sent over a memoryless channel. In the receiving end, the channel decoder and the neural source decoder (NSD) forms an iterative structure to reconstruct the original image. Since all neural compressed data are transmitted over wireless channels, the training of NSD is affected by wireless channel factors such as residual bit errors given by the channel decoder. Meanwhile, during outer-loop iterations, the NSD needs to match the variant of information reliability output by the channel decoder so as to build a global optimal receiver. To this end, a refiner neural network is first attached after the NSD to adjust its output as the format of a priori information sent into the channel decoder. Then, the extrinsic information transfer (EXIT) functions of channel decoder and NSD are derived. At each iteration, the reliability of messages sent into the NSD is explicitly predicted by using the EXIT chart. By this means, the NSD can be trained in a residual bit error aware manner, and we realize a joint learning and iterative decoding framework to ensure the quality of neural image transmission over realistic wireless channels.

Multilevel polar-coded modulation with multistage decoding is a capacity-achieving coded modulation scheme. In this paper, we propose a general formulation for analyzing the performance of multilevel polar-coded modulation and then derive the error probability upper bounds. The analysis explicitly reveals the effect of the modulation scheme on the performance of component polar codes. Based on the derived upper bounds, we also propose two construction methods for multilevel polar-coded modulation. Compared with conventional methods, such as density evolution and Gaussian approximation which involve complicated recursive calculations, the proposed methods have a linear computational complexity. Simulation results also show that the proposed construction methods can achieve comparable performance to existing methods under SC decoding, and even better performance under SC list decoding.

In this paper, layered min-sum (MS) iterative decoding is formulated as a customized neural network following the sequential scheduling of check node (CN) updates. By virtue of the lifting structure of protograph low-density parity-check (LDPC) codes, identical network parameters are shared among all derived edges originating from the same edge in the protograph, which makes the number of learn- able parameters manageable. The proposed neural layered MS decoder can support arbitrary codelengths consequently. Moreover, an iteration-wise greedy training method is proposed to tune the parameters such that it avoids the vanishing gradient problem and accelerates the decoding convergence.

We propose a rate-filling method as a framework to study fast construction of bit-interleaved polar-coded modulation (BIPCM). In particular, the proposed method makes full use of the nesting structure of polar codes. We show that BIPCM can be constructed by appropriately allocating the rates of each polar subcode whose information set is easily extracted by a predetermined index sequence, e.g., the Polar sequence in the 5G standard. By this means, the reliability calculation and ranking operation of bit channels can be discarded, which is a desired feature for low-complex construction. Simulation results show the proposed fast construction method performs independently of the actual channel condition and is robust to diverse modulation and coding schemes in the 5G standard.

Outdated channel information degrade capacity performance in limited feedback precoded MIMO-OFDM system. Channel prediction is a flexible way to combat channel aging without the cost of scarce time-frequency resources. Long short term memory (LSTM) is a class of neural networks, capable of processing data that has long-term dependence in time series. In this paper, an LSTM-based channel predictor is designed to achieve a longer range of predictions. Precoding matrix indicator (PMI) and rank indicator (RI) are two indicators related to precoding and the number of precoding matrices forming the codebook increased with the number of antenna ports. Furthermore, we try to calculate PMI/RI with the aid of the neural network. Through the simulation experiment, we test the performance of the LSTM channel predictor and the PMI/RI selector. The result illustrates that our predictor achieves considerable channel capacity gain and our selector shows high accuracy. Moreover, the generalization ability of the LSTM predictor for different antenna configurations is studied.

In this paper, a novel polar-coded MIMO (PC-MIMO) framework is proposed to enhance the system polarization effect so that the transmission reliability is improved. The key idea is asynchronously transmitting coded bits within one block and then spatially coupling multiple blocks by joint antenna mapping. This consequently leads to the enhancement of the polarization diversity among antenna subchannels under finite block length. Combining with binary polar coding, the gain achieved by the antenna subchannel polarization is finally propagated to bit polarized channels. The proposed asynchronous PC-MIMO is proved to be capacity-achieving under infinite block length and to realize performance gain with respect to synchronous PC-MIMO under finite block length.

Recently, the indoor positioning system based on the angle of arrival (AOA) has been widely concerned. The positioning system often contains two steps. The first step is the AOA estimation algorithm and the second step is the positioning algorithm using the estimated AOAs. However, due to the indoor signal multipath propagation, the AOA estimation errors can lower the positioning performance. To solve this problem, this paper proposes a novel angle of arrival (AOA) positioning algorithm which selects multiple candidate AOAs and selects the most reliable one from them. When multiple candidate AOAs are selected and passed into the positioning algorithm, multiple estimated locations can be obtained. To select the most reliable AOA, the location reliability prior information related to these estimated locations is introduced. The AOA with the maximum location reliability prior information is the final AOA estimation result and its corresponding estimated location is the final positioning result. The simulation results show that compared with the positioning algorithm which directly uses one AOA for positioning, the proposed positioning algorithm provides better positioning performance.

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.

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.

Code construction is a critical issue for polar coding. The expected construction method is of an accurate estimate of the reliability of bit-channels, but the current methods usually require high computational complexity. In this paper, we concern the input-output weight distribution of each bit-channel and derive its recursive calculation algorithm for systematic coding. The union bound and union-Bhattacharyya bound on the bit error probability are also derived to evaluate the reliability of bit-channels. Furthermore, by calculating the logarithmic form of the union-Bhattacharyya bound, we also propose two novel construction methods named the union-Bhattacharyya bound weight of the bit error probability (UBWB) and the simplified UBWB (SUBWB). Numerical results show that the proposed UBWB/SUBWB construction methods can achieve comparable performance to current methods under successive cancellation (SC) decoding and obtain obvious performance gain under SC list (SCL) decoding.

A good quantization scheme plays a tremendously important role in the hardware implementation of the decoder for polar codes. In this paper, a non-uniform quantization scheme for cyclic redundancy check aided successive-cancellation list (CA-SCL) decoder of polar codes is proposed to save memory resources. In the proposed non-uniform quantization scheme, we introduce a simple compression function to decrease the truncation threshold so that the soft information can be quantized with fewer quantization bits. The scaling factor in the compression function is a negative integer power of two, which can be efficiently implemented by shift operations. Then a compressed-domain CA-SCL decoder is derived, alleviating the need to decompress samples one-by-one. Simulation results show that on the condition of approaching floating-point performance, the internal log-likelihood ratio (LLR) memory resource consumption can be saved by 20% compared with uniform quantization.

The positioning algorithm currently in wireless networks is a separate estimation problem. The first step is to estimate the delay of each link to obtain the time-of-arrival (TOA) information. The second step is to pass the TOA into the positioning algorithm to get position estimation. However, the transmission of the signal between the user equipment (UE) and the base station (BS) is inevitably affected by non-line-of-sight (NLOS) transmission. At this time, the estimation of delay may not be the true TOA, resulting in the positioning performance is greatly reduced. To solve this problem, this paper proposes a Joint Approximate Maximum Likelihood (JAML) positioning algorithm, which combines delay estimation and position estimation, in 5G New Radio (NR) Systems. When the delay estimation is inaccurate, JAML reselects the correct delay estimation as TOA information for position estimation. We use a channel model of 5G NR in the simulation, and simulation results show that the JAML positioning algorithm improves the positioning performance.

Puncturing and shortening are two general ways to obtain an arbitrary code length and code rate for polar codes. When some of the coded bits are punctured or shortened, it is equivalent to a situation in which the underlying channels of polar codes are different. This fact calls for a general polar code construction, which is not yet available. In this article, a general construction of polar codes is studied in two aspects: 1) the theoretical foundation of the general construction and 2) the hardware implementation of general polar codes encoders. In contrast to the original identical and independent binary-input, memoryless, symmetric (BMS) channels, these underlying BMS channels can be different. The proposed general construction of polar codes is based on the existing Tal-Vardy's procedure. The symmetric property and the degradation relationship are shown to be preserved under the general setting, rendering the possibility of a modification of Tal-Vardy's procedure. Simulation results clearly show improved error performance with reordering using the proposed new procedures. Also, a novel encoding hardware architecture is proposed, which supports puncturing and shortening modes. Implementation results show the proposed encoder achieves approximately 30% throughput improvement when one quarter of bits are punctured/shortened.

Impulsive noise can greatly affect the performance of underwater acoustic (UA) orthogonal frequency-division multiplexing (OFDM) systems. In this paper, by utilizing the sparsity of the UA channel impulse response and impulsive noise, we first propose a novel sparse Bayesian learning (SBL) based expectation maximization (EM) algorithm for joint channel estimation and impulsive noise mitigation in UA OFDM systems. Secondly, considering that the UA channel and impulsive noise are fast time-varying, we develop a new approach which combines the SBL with the forward-backward Kalman filtering to track the UA channel and impulsive noise. To further improve the system performance, we utilize the information available on data subcarriers for joint time-varying channel estimation and data detection, based on the SBL algorithm and the Kalman filter. The performance of our proposed algorithms is verified through both numerical simulations and by data collected during a UA communication experiment conducted in the estuary of the Swan River, Perth, Australia. The results demonstrate that compared with existing approaches, the proposed algorithms achieve a better system bit-error-rate and frame-error-rate performance.

Orthogonal time frequency space modulation (OTFS) which is a novel modulation scheme, aims at offering reliable communications to users under high mobility conditions. Since each symbol in OTFS experiences the same fading over the doubly selective channel, OTFS is able to exploit full time-frequency (TF) diversity when coupled with a proper equalizer. In this paper, we study the low complexity iterative equalizer design for the coded OTFS system. In order to jointly combat the two-dimensional interference, we adopt linear minimum mean squared error based parallel interference cancellation (LMMSE-PIC) as the equalizer for OTFS. The first order Neumann series is used to approximate the matrix inversion involved in LMMSE-PIC for OTFS, which reduces the equalizer complexity to quasi-linear to the total number of transmitted symbols. Extrinsic information transfer (EXIT) charts and coded bit error rate (BER) simulation results show that our proposed LMMSE-PIC equalizer outperforms the recently proposed PIC approach for OTFS and also achieves considerable gain compared to orthogonal frequency division multiplexing (OFDM).

In this paper, a weighted successive cancellation (WSC) algorithm is proposed to improve the decoding performance of polar codes with the quantized log-likelihood ratio (LLR). The weights used in the WSC are automatically learned by a neural network (NN). A novel NN model and its simplified architecture are build to select the optimal weights of the WSC, and all-zero codewords can train the NN. Besides, we impose the constraints on weights to direct the learning process. The small number of trainable parameters lead to faster learning without performance loss. Simulation results show that the WSC algorithm is valid to various codewords and the trained weights make it outperform SC algorithm with the same quantization precision. Notably, the WSC with 3-bit quantization precision achieves a near floating point performance for short length.

In massive multiple-input multiple-output (MIMO) scenario, in order to adapt to channel features and ensure the high-reliability and high-rate of communication, the channel state information (CSI) should be sent back to the base station (BS). As the number of antennas increases, the amount of CSI feedback increases dramatically. Ensuring communication performance and reducing CSI feedback is a challenging research direction. Some deep learning (DL) based models have been proposed to solve this problem. This paper aims to improve recovery performance and decrease time complexity. First, in encoder network, long short-term memory (LSTM) network have been introduced; second, in decoder network, attention mechanism has been added; third, early stopping have been used in training step. The simulation results show that the structure proposed in this paper is not only better than the performance of the traditional compressed sensing algorithm, but also better than the existing DL-based feedback network and achieve state-of-the-art.

For short to moderate finite block-lengths, due to incompletely polarized bit-channels, polar codes fail to deliver competitive performance under successive cancellation (SC) decoding. In this paper, we first define ω-order medium-level bit-channels (MBC) sets, which are used to quantitatively identify the incompletely polarized bit-channels of polar codes. And then, using the reliability ordering of bit-channels, which is calculated and used for encoding and decoding of polar codes, an algorithm for finding the MBC sets is given. Finally, we propose a multi-bit-flipping SC decoding algorithm, which builds a list of candidate bit-flips with 1 up to ω flipping positions within the ω-order MBC sets. The iteration trajectory of bit-flipping positions is a fixed subsequence of reliability ordering, which avoids the sorting operation of log likelihood rates in each codeword frame. Simulation results show that, with a code rate 0.5, the search scope and decoding latency of the proposed algorithm can be reduced about 1/3 to 1/6 without loss of performance compared with the standard SCFlip decoding.

As an improved version of successive cancellation (SC) polar decoder, an SC stack (SCS) decoder has been proposed for performance improvement. However, the existing SCS polar decoder suffers a lot from high time complexity at low signal-to-noise ratio (SNR) region and space complexity compared with the SC decoder. To this end, two improved decoders are proposed to reduce time and space complexity in both low and high SNR regions. The first one is the segmented cyclic redundancy check (CRC)-aided SCS (SCA-SCS) decoder, which is based on segmented parity checkers. The second one is the adaptive SCS (ASCS) decoder, which has the flexibility of stack depth and searching width. Furthermore, a channel condition estimator is proposed to select appropriate decision criteria for different SNR scenarios. Results have shown that for the polar code of length 1024 and rate 1/2, two improved SCS decoders can perform better than the traditional SCS decoder. The proposed SCA-SCS decoder and the ASCS decoder can achieve 10.8% and 11.42% time complexity reduction and 31.68% and 60.85% space complexity reduction on average over binary-input additive white Gaussian noise channels (BI-AWGNCs), respectively. Efficient parallel hardware architecture of the SCS polar decoder is first proposed and implemented with 90- and 65-nm technologies. Results have verified its advantages over the state of the art (SOA).

In vehicular ad-hoc network (VANET), due to its unique characteristics such as high nodes' speed, dynamic network topology and variable nodes' density, end-to-end data transmission faces many challenges. To address these challenges, many routing protocols specially for VANET have been proposed. But a chief part of them are designed only in network layer and independently of other layers. Besides, few of them consider the influence of interference. In this paper, we propose a new cross-layer routing with interference constraint for VANET which combines technologies of network layer and MAC layer. First, we adopt a location-based resource allocation algorithm to constrain interference between vehicles. Then on the basis of it, relay is selected depending on their geographical zones. In addition, we also use the relative distance among vehicles and the reception Signal to Interference plus Noise Ratio (SINR) of hello messages to optimize the relay selection scheme. By simulation, we compare it to other protocols and experimental results show that our routing protocol outperforms existing solutions in terms of packet delivery ratio (PDR).

In the conventional bit interleaved polar coded modulation (BIPCM), there exist some mutual information losses due to neglecting the correlations between the bit levels by using the bit interleaver. Based on this, the delayed BIPCM (DBIPCM) architecture is proposed to reduce the information losses in this paper. In the DBIPCM scheme, only one encoder is adopted and the transmitted symbols convey bits from the codewords encoded at different time for each bit level determined by the preset delays, which is different from the BIPCM. As we know, the variance of the bit channel capacities is used to measure the polarization effect under the multilevel polar coded modulation (MLC-PCM) scheme. It is found that the DBIPCM can achieve a more significant polarization effect compared to the MLC-PCM with the same set partitioning (SP) mapping way due to an extra log2m polarization transform process for 2m-ary constellations. From the simulation results by using the 16PAM over the additive white Gaussian noise channels, it is shown that the DBIPCM with Gray mapping can achieve a better performance compared to the conventional BIPCM. In addition to this, the DBIPCM with SP mapping outperforms the MLC-PCM by up to more than 0.6dB.

An efficient codebook optimization algorithm is proposed to maximize mutual information in sparse code multiple access (SCMA). At first, SCMA signal model is given according to superposition modulation structure, in which the channel matrix is column‐extended. The superposition model can well describe the relationship between the codebook matrix and received signal. Based on the above model, an iterative codebook optimization algorithm is proposed to maximize mutual information between discrete input and continuous output. This algorithm can efficiently adapt to multiuser channels with arbitrary channel coefficients. The simulation results show that the proposed algorithm has good performance in both AWGN and non‐AWGN channels. In addition, message passing algorithm (MPA) works well with the codebook optimized according to the proposed algorithm.

In this paper, a three‐dimensional polar code (3D‐PC) scheme is proposed to improve the error floor performance of parallel concatenated systematic polar code (PCSPC). The proposed 3D‐PC is constructed by serially concatenating the PCSPC with a rate‐1 third dimension, where only a fraction λ of parity bits of PCSPC are extracted to participate in the subsequent encoding. It takes full advantage of the characteristics of parallel concatenation and serial concatenation. In addition, the convergence behavior of 3D‐PC is analyzed by the extrinsic information transfer (EXIT) chart. The convergence loss between PCSPC (λ = 0) and different λ provides the reference for choosing the value of λ for 3D‐PC. Finally, the simulation results confirm that the proposed 3D‐PC scheme lowers the error floor.

Sparse code multiple access (SCMA) is a promising non-orthogonal multiple access scheme for 5G systems. In this paper, based on the sphere decoding (SD), we design an optimal receiver for SCMA, which provides much lower complexity than the maximum likelihood (ML) detection without sacrifice of the optimal performance. Regarding the coded SCMA system, we propose a list sphere decoding (LSD) method to output each user's bit soft decisions to the corresponding channel decoders. In addition, the existing lattice points in the list are employed to set the original radius so as to further reduce the complexity of LSD. The performance and complexity analyses indicate that the proposed optimal receiver provides notable gain compared to the most popular SCMA receiver equipped with the message passing algorithm.

Polar coded modulation (PCM) is one of the most promising approaches towards high spectral efficiency. It is able to provide excellent performance comparing to the conventional turbo coded modulation schemes. In this paper, we consider the design of the PCM schemes with high modulation order, e.g., 256QAM or 1024QAM etc. Different with the legacy PCM of low modulation order, the coded bits in high-order PCM with all-zero frozen-sequence will demonstrate obvious correlations which lead to catastrophic performance loss. In order to evaluate this loss, we introduce a quantitative metric, named the bit conditional mutual information (BCMI), to analyze the correlation. Then a new PCM scheme based on the constrained frozen-sequence is proposed to eliminate the correlations among the coded bits. Theoretical analysis and simulation results show that the proposed scheme can ensure the performance of PCM and significantly outperform that of the turbo coded modulation schemes.

In this paper, polar coding is combined with the generalized frequency division multiplexing (GFDM) and the channel polarization idea is extended to the GFDM system, which is a flexible multicarrier modulation scheme proposed for future waveform. First, the framework of polar coded GFDM (PC-GFDM) is constructed based on a two-stage channel polarization transform. In the first stage, on the basis of bit-interleaved code modulation, GFDM channel is divided into a set of parallel binary-input GFDM synthesized channels. In the second stage, by using the binary channel polarization, the GFDM synthesized channels are further polarized into a set of bit polarized channels. The polar codes are efficiently constructed by calculating the reliabilities of the two-stage polarized channels. Then, an empirically optimal interleaver is designed for PC-GFDM. Furthermore, regarding the features of GFDM synthesized channels, a simplified block interleaving scheme is proposed to approach the optimal interleaver and reduce the implementation complexity. Compared with the conventional turbo coded GFDM (TC-GFDM), simulation results indicate that our proposed PC-GFDM system can outperform the TC-GFDM system by at least 1dB for different scenarios.

Sparse code multiple access (SCMA) has recently emerged as one of the most favorable multiple access schemes for 5G networks, which allows overloading with a large number of users so as to enable massive connectivity. In this paper, we design a hardware framework of a uplink system for SCMA. First, we propose a unified quantization scheme based on density evolution optimization which is independent of signal-to-noise ratios (SNR). Second, we apply a fast convergence message passing algorithm (FC-MPA) in SCMA multiuser detection, in which the function nodes updating and variable nodes updating are processed synchronously so as to make the FC-MPA converge about 2 times faster than standard MPA. Finally, based on FC- MPA, we design a pipelined decoding structure for SCMA so as to increase throughput. FPGA results demonstrate that the fix- point performance achieve a near floating-point performance for different SNR and the pipelined hardware structure we design is feasible.

In orthogonal frequency-division multiplexing access (OFDMA) system, a distributed Bayesian compressive sensing (DBCS) based blind carrier frequency offset (CFO) estimator has been proposed, which offers a significant improvement on the performance of multiple-parameter estimation, compared with the existing subspace theory based method. However, the analysis for theoretical performance and computational complexity is absent. In this paper, we conduct a further study on this method, and derive the Cramer-Rao Bound to evaluate the performance. Then we highlight our work on the complexity reduction, for which a multi-layer DBCS based algorithm is presented. Additionally, we analyze the computational complexity of this new method, and derive an effective solution for determining the optimal layer number. Simulation results demonstrate our analysis, and show that our proposed algorithm can reduce the complexity by approximate two orders of magnitude with only a slight loss in performance at low signal-to-noise ratio (SNR).

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.

In this paper, an architecture of constellation shaping for bit-interleaved polar coded modulation (BIPCM) is proposed. Theoretically, the probability distribution of constellation points should be selected according to a continuous Gaussian distribution over Additive White Gaussian Noise (AWGN) channels, which will help to obtain shaping gain. Based on this probabilistic shaping idea, unequiprobable constellations are designed to increase the mutual information. Under the proposed BIPCM architecture, the mapping method has a larger diversity of bit channels that can help to improve the system performance. The mapping way is performed based on the prefix code, which can meet the above property. Simulation results of 16-ary pulse amplitude modulation (16-PAM) over AWGN channels show that the nonequiprobable constellations provide 0.5dB shaping gain compared to the traditional equiprobable constellations.

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.

Polar codes are proven to achieve the rate distortion bound of Gaussian sources under the condition that the size of reconstruction alphabet and code length grow to infinity. However, this condition cannot be satisfied in practice. In this paper, we propose a practical source coding scheme based on multilevel polar codes, called polar coded quantization (PCQ). In this scheme, extended reconstruction alphabet, set-partition (SP) labeling and successive cancellation (SC) (or its improved) encoding algorithm are combined to approach the rate distortion bound. Furthermore, an efficient upper bound of encoding rate for a given distortion is derived to guide the practical design of PCQ. Simulation results show that PCQ provides a flexible and constructive framework to efficiently approximate the rate distortion bound of Gaussian sources.

In this letter, cooperative multicast in cognitive relay systems is investigated, where multiple multi-antenna relay nodes help forward the transmitted message from different sources to the corresponding destinations. Our objective is to maximize the global transmission rate scaling factor by cooperatively optimizing the forwarding matrix for each relay node. To solve the problem effectively, we first prove that the optimal forwarding matrix at each relay node is the combination of a multi-stream matched-filter receiver and a multi-stream adaptive beamformer, and then demonstrate that the beamformer design problem can be further transformed into the cognitive multi-group multicast beamforming problem. Finally, a bisection-based algorithm is proposed to find the optimal beamforming vector. Compared to the existing cooperative multicast schemes, the proposed scheme can achieve higher transmission rate and provide better protection for primary nodes.

In this paper, we investigate the achievable throughput of cognitive radio ad hoc networks. The current research on throughput evaluations focus on the average value over spatial distribution of random nodes. However, these average models neglect performance discrepancies resulting from different spatial nodes. In order to overcome this limitation, we derive the achievable rate coverage probability (equivalent to complementary cumulative distribution function (CCDF)) of the secondary network according to the random locations of secondary users. Based on the achievable rate coverage probability, the achievable throughput is derived for the secondary network under the primary outage constraint. Through stochastic geometric analysis, it is shown that the achievable throughput is mainly dominated by the densities of primary and secondary nodes, as well as the quality of service (QoS) of the primary network and the rate coverage probability of the secondary network. Besides, the achievable rate coverage probability is optimized to maximize the achievable throughput of the secondary network.

Polar codes have a regular-recursive structure, which can efficiently be mapped to hardware for practical applications. A good quantization scheme plays a great important role in hardware implementation. In this paper, three uniform quantizers are designed for successive cancellation (SC) decoding of polar codes based on either optimizing the equivalent channel capacity, cutoff rate or mean-squared error (MSE). Moreover, exploiting the cutoff rate maximizing criterion, a modified Gaussian approximation (GA) method is proposed to construct polar codes and estimate frame error rate (FER) performance under the quantized decoding algorithms. Simulation results have shown that a 6-bit uniform quantized SC decoder can achieve a near floating point performance and the upperbound of FER can be estimated precisely using the modified GA method under quantized decoding algorithms.

We propose a parallel and distributed algorithm for the problem of beamformer design and power allocation in multi-cell massive multiple-input multiple-output (MIMO) networks. Inter-cell coordination and overlapping clusters are considered to enhance the system performance. To overcome the tight coupling induced by inter-cell coordination and overlapping clusters, we formulate the system into a two-layered framework. In this framework, the problem is decomposed into independent subproblems which are naturally solvable in a parallel and distributed manner. For each subproblem, an efficient parallel algorithm is proposed to speed up the convergence rate. The simulation results show that our inter-cell coordination strategy can approach the performance achieved by a high-quality limited inter-cell coordination strategy at low signal-to-noise ratio (SNR), and outperform it at high SNR. Moreover, the proposed parallel algorithm enjoys great parallelization speedup with moderate transmit power consumption.

Polar codes are a class of codes discovered recently, which are proved to have capacity-achieving performance. The hardware implementations of successive cancellation (SC) decoding and List SC decoding are increasing dramatically now. However, throughput of these hardware structures remain to be improved on the one hand. On the other hand, stochastic computation is now wildly used because it uses simple structures to implement complicated mathematical operations and thus improves the throughput. This paper extends the application of stochastic computation to the decoding of polar codes. A family of SC decoders based on stochastic computation are proposed, including decoders based on bipolar stochastic computation(BSC-SC), low bits stochastic computation(LBSC-SC) and improved stochastic computation(ISC-SC). These methods have high generality and improve the throughput over SC decoder while having 0.5dB loss in decoding performance. The proposed 32-bit ISC-SC decoder has a better decoding accuracy and improves throughput over BSC-SC. It also reduces the hardware cost and latency compared to LBSC-SC decoder.

In this paper, a cognitive radio network with multiple licensed users is considered. A dirty paper coding (DPC) based cooperation scheme for the Gaussian multiple-in-multiple-out (MIMO) cognitive radio network (CRN) with partial transmitter side information is studied. The problem of maximizing the sum-rate of MIMO CRN with multiple primary users over the transmitter covariance matrices, which is formulated as an optimization problem, is dealt with. Such an optimization, which needs to be performed jointly with the inflator factors under the DPC-based strategy, is solved by an iterative suboptimal solutions. Simulation results show that the proposed scheme is able to greatly improve the achievable rate performance in CRN.

Polar codes have a regular recursive structure which is feasible for hardware implementation in practice. Quantization is a critical issue in hardware implementation. In this paper, a quantization scheme for successive-cancellation (SC) decoding of polar codes is proposed based on the equivalent channel cutoff rate optimization. Moreover, a modified Gaussian approximation (GA) method exploiting the cutoff rate maximizing criterion is brought forward to construct polar codes and estimate frame error rate (FER) performance under the quantized decoding algorithms. Utilizing the modified GA, the optimal quantization bits can be determined. Simulation results demonstrate that a 6-bit uniform quantized SC decoder could achieve a near floating point performance. Compared with the existing results in the published literature, the proposed method consumes no more than 4.5 quantization bits and performs better.

In this paper, we consider an extension of the cognitive radio channel model in which the secondary transmitter has to obtain (learn) the primary message rather than having non-causal knowledge of it. We formulate an achievable rate region that combines elements of compress-and-forward relaying with coding for the pure cognitive radio channel model. Moreover, we provide parameters design that maximizes the secondary rate without affecting the primary rate in the Rician channel with partial channel state information at the transmitter (CSIT). Simulation results demonstrate that the proposed compress-and-forward based strategy achieves good performance.

In this paper, we consider the sensor network, where multiple sensors communicate with a single fusion center via their respective direct links and relays. We propose schemes exploiting both quantize-and- forward (QF) and compressive sensing (CS) in relay networks, aiming to take full use of the diversity to recover the original sparse signals through the collection of a small number of samples. Such sub- sampling QF framework not only inherits the advantage of QF, but also enjoys the merits of reduced sampling rate by exploiting CS technique. The proposed two sub-sampling QF schemes place emphasis on: i) diversity gain in CS domain by employing different projection matrices in different sensors; ii) diversity gain in coding domain due to the correlations, respectively. We assess and compare the performance of the proposed schemes under AWGN channel. Simulation results demonstrate that the proposed schemes achieve excellent recovery performance with reduced communication overhead.

A hybrid automatic repeat request scheme with Chase combing (HARQ-CC) of polar codes is proposed. The existing analysis tools of the underlying rate-compatible punctured polar (RCPP) codes for additive white Gaussian noise (AWGN) channels are extended to Rayleigh fading channels. Then, an approximation bound of the throughput efficiency for the polar coded HARQ-CC scheme is derived. Utilizing this bound, the parameter configurations of the proposed scheme can be optimized. Simulation results show that, the proposed HARQ-CC scheme under a low-complexity SC decoding is only about 1.0dB away from the existing schemes with incremental redundancy (HARQ-IR). Compared with the polar coded HARQ-IR scheme, the proposed HARQ-CC scheme requires less retransmissions and has the advantage of good compatibility to other communication techniques.

The distance spectrum is used to estimate the maximum likelihood (ML) performance of block codes. A practical method which can run on a memory-constrained computer is proposed to calculate the distance spectrum of polar codes. Utilizing the distance spectrum, the frame error rate (FER) and bit error rate (BER) performances of non-systematic polar codes (NSPCs) and systematic polar codes (SPCs) are analyzed. The union bounds as well as the simulation results illustrate that, under ML decoding, SPCs are superior to NSPCs in BER performance while NSPCs and SPCs have the same FER performance.

Recent work in the area of compressed sensing mainly focuses on the perfect recovery of the entire support for sparse signals. However, partial support recovery, where a part of the signal support is correctly recovered, may be adequate in many practical scenarios. In this study, in the high‐dimensional and noisy setting, the authors develop the probability of partial support recovery of the optimal maximum‐likelihood (ML) algorithm. When a large part of the support is available, the asymptotic mean‐square‐error (MSE) of the reconstructed signal is further developed. The simulation results characterise the asymptotic performance of the ML algorithm for partial support recovery, and show that there exists a signal‐to‐noise ratio (SNR) threshold, beyond which the increase of SNR cannot bring any obvious MSE gain.

CRC (cyclic redundancy check) concatenated polar codes are superior to the turbo codes under the successive cancellation list (SCL) or successive cancellation stack (SCS) decoding algorithms. But the code length of polar codes is limited to the power of two. In this paper, a family of rate-compatible punctured polar (RCPP) codes is proposed to satisfy the construction with arbitrary code length. We propose a simple quasi-uniform puncturing algorithm to generate the puncturing table. And we prove that this method has better row-weight property than that of the random puncturing. Simulation results under the binary input additive white Gaussian noise channels (BI-AWGNs) show that these RCPP codes outperform the performance of turbo codes in WCDMA (Wideband Code Division Multiple Access) or LTE (Long Term Evolution) wireless communication systems in the large range of code lengths. Especially, the RCPP code with CRC-aided SCL/SCS algorithm can provide over 0.7dB performance gain at the block error rate (BLER) of 10-4with short code length M = 512 and code rate R = 0.5.

A new bit-interleaved polar coded modulation (BIPCM) scheme is proposed. To reduce the construction complexity, the simplest kernel matrix for channel polarization is adopted. And auxiliary virtual channels with zero-capacities are introduced to adapt different modulation orders. The underlying polar codes are efficiently constructed by calculating the Bhattacharyya parameters of equivalent binary-input erasure channels (BECs) rather than computing-expensive density evolution. Further, to avoid exhaustive searching, an empirically good channel mapping scheme is provided. Compared with the existing BIPCM schemes, the proposed scheme has a lower construction complexity and can achieve a better performance. With cyclic redundancy check (CRC) aided decoding algorithms, the block error rate (BLER) performance of our proposed BIPCM scheme can outperform the turbo coded modulation scheme used in WCDMA (Wideband Code Division Multiple Access) wireless communication system by up to 0.5dB.

Base station cooperation is a promising technique to address the serious co-channel interference issue, but dramatic signaling overhead and high computational complexity are inherent in this advanced coordination technology. Limited form of cooperation among base stations becomes an practical method to deploy this multiple cell processing in a realistic network. After analyzing the multi-cell sum rate maximum problem subjected to cluster size and per base station transmission power, a heuristic clustering algorithm is proposed in this paper to facilitate the cluster formation to adapt to the dynamic interference condition in cellular network. Distributed beamforming is further applied among those transmitters in the same cluster. Both of these two interference management strategies are built on the concept of transmitter's leakage channel. According to this interference generating channel state information, each transmitter locally and independently calculates the leakage power to receivers in neighboring cells, and this information is utilized to design the heuristic search utility function and the distributive beamforming vector. Through simulation it is observed that the proposed framework leads to significant gain over static clustering policy and harvests majority of performance gain improved by optimal clustering scheme at less complexity cost.

This paper investigates the energy efficient resource allocation strategy for OFDM-based cognitive radio (CR) networks with imperfect spectrum sensing. The interference model taking the sensing errors into account is formulated at first. And the objective is to maximize the energy efficiency of the multiuser CR system subject to the total transmission power budget and each primary user's (PU) interference constraints. As the primal problem is a mixed integer nonlinear programming issue, we will separate the resource allocation scheme into two steps, i.e., subcarrier assignment and power allocation. After the suboptimal subcarrier assignment, an optimal power allocation algorithm is proposed based on fractional programming and sub-gradient method. The simulation results show that the proposed resource allocation scheme can achieve higher energy efficiency than the one maximizing the capacity of the CR networks. Meanwhile, it can protect the normal communication of each PU compared to the scheme without considering sensing errors.

In this paper, cooperative multicast with the help of short-range data sharing is studied in the cognitive radio networks (CRNs). The original multicast data is split into many segments, and the transmission of each segment is divided into two stages. In the first stage, cognitive base station transmits each segment to the corresponding cooperative user, and in the second stage, the cooperative user decodes the received data and broadcasts it to other multicast users. Based on this transmission model, cooperative multicast is formulated as an optimization problem with the aim of maximizing the total transmission rate. Afterwards, we first propose a cooperative user selection strategy, which is meaningful when the multicast size is large, and then implement the resource allocation with dual translation and subgradient updating. The simulation results show that the proposed cooperative multicast scheme can achieve much higher spectrum efficiency than both conventional multicast scheme and multiple description coding multicast scheme.

Scheduling with single carrier property restriction has a significant impact on system performance in Long Term Evolution (LTE) uplink (UL). Due to the unavoidable delay of control signaling, the scheduling decisions have great influence on the variation of inter-cell interference (ICI) which affects the performance of adaptive modulation and coding (AMC). Many studies have been carried out on the topic of resource allocation in single-carrier frequency division multiple access (SC-FDMA) system. Some of them deal with the ICI variation with inter-cell measurements or coordination. In this paper, we present the problem of frequency domain packet scheduling (FDPS), analyze the negative effects of ICI variation and develop our improved proportional fair (PF) scheduling algorithm based on the traditional one for LTE UL. Our proposed algorithm decreases the ICI variation and improves AMC accuracy without any inter-cell coordination. Compared with the traditional way, system level performance shows that at least 29% gain can be obtained at both the cell user average spectral efficiency and the cell edge user spectral efficiency with comparable proportional fairness in typical interference-limited scenario.

Polar codes are the first constructive and provable capacity-achieving codes. In finite code length cases, successive cancellation list (SCL) decoding algorithm is reported to have performance very close to maximum-likelihood (ML) decoding. In this paper, a reduced-complexity version of SCL decoding algorithm is proposed to boost the finite- length performance of polar codes. By regarding the SCL decoding algorithm as a path searching procedure in a code tree representation, a tree-pruning technique is used to avoid unnecessary path searching operations. With only a negligible loss of performance, the computational complexity of pruned SCL decoder can be very close to that of the successive cancellation (SC) decoder in the moderate and high signal-to- noise ratio (SNR) regime.

The transmission capacity (TC) of the secondary (SR) network in cognitive radio networks (CRNs) is investigated in this paper. Mathematically, the TC of the SR network is defined as the maximum transmitter density of the SR network that satisfies the outage probability constraints of the primary (PR) network and the SR network, multiplied by the communication rate and the successful reception probability. In order to effectively control interference in CRNs to obtain a higher TC, we propose a spectrum sharing scheme with preservation regions to avoid excessive interference from the secondary users to the primary users. Closed-form expression for the TC of the SR network is derived under such a scheme. We optimize the transmitter density, the transmit power and the preservation range, which maximize the TC of the SR network. Numerical results show that our scheme outperforms traditional schemes.

Wavelet transform has proved to be an attractive tool in terms of analyzing singularities and irregular structures, which can characterize irregular edges of signals. Thus, it is well motivated to apply the wavelet transform approach to wideband spectrum sensing. But the existing wavelet-based spectrum sensing schemes work under the assumption that the frequency response of the analog signal input at the sensing receiver is real. To make this method work for more types of signals, this paper develops a novel wavelet-based approach to compressive wide-band spectrum sensing. In the proposed scheme, the wide-band time domain signal is fed into a number of filters and the sub-Nyquist sampled outputs are utilized to detect the occupancy of spectrum via a wavelet-based edge detector. The filters' outputs are real and nonnegative, regardless of the style of the analog signal. Furthermore, through simply adding the measurement vectors at the fusion center, this scheme can be applied to the case of multiple cognitive radios (CRs), reducing the complexity compared with traditional joint recovery algorithms.

With the continuous growth of the wireless communication business, energy issues and environmental problems are becoming increasingly grim. Therefore, this paper investigates the energy efficient transmission scheme with cooperative sensing in cognitive radio networks, in which AND fusion rule is introduced to determine the presence of the primary user. It is proved that the energy efficiency is a quasi-concave function with sensing time when the number of cooperative users satisfies certain constraints. Aiming at maximizing the energy efficiency, the transmission power is selected at first, then a scheme of jointly optimizing sensing time, energy detector threshold and the number of cooperative users is proposed based on the related theory analysis. From the simulations, it can be found that the optimal sensing time is only about half of that consumed in single user sensing, and the proposed scheme has significant improvement in energy efficiency.

The transport capacity of cognitive radio ad hoc networks is investigated in this paper. Compared with the related works in the literature which mainly give order sense results like scaling laws, we derive an expression of the single-hop transport capacity for the secondary (SR) network under the outage constraint of the primary (PR) network. The Aloha medium access control (MAC) protocol and the nearest neighbor routing protocol are considered. Through stochastic geometric analysis, it is shown that the outage probability of the PR network and the transport capacity of the SR network are mainly dominated by the node density of the SR network and the medium access probability p of the MAC protocol. Besides, the value p is optimized to maximize the transport capacity of the SR network.

In this paper, resource allocation problem with imperfect channel information in cognitive radio networks (CRNs) is studied concerning the multiple description coding (MDC) multicast transmission. The traditional unicast model is extended to MDC multicast in CRNs, which aims to maximize the total received rate of all cognitive radio (CR) users. Primarily, a new auxiliary variable, named as normalized channel power gain in this paper, is introduced to substitute the transmission rate as the optimization variable. Then a two-stage method is proposed to conduct the resource allocation: first the multicast group (MG) selection and the normalized channel power gain setting, second the optimal power allocation. Meanwhile, as only estimated channel gain for the interference channel gain is obtained, the primary users' interference can not be accurately estimated when we carry out the power allocation. Consequently, we suitably enlarge the estimated channel gain for the purpose of interference control. It has been verified in the simulation results that the proposed scheme can improve the system performance apparently in terms of both throughput maximization and interference control.

Channel polarisation results are extended to the case of communications over parallel channels, where the channel state information is known to both the encoder and decoder. Given a set of parallel binary‐input discrete memoryless channels (B‐DMCs), by performing the channel polarising transformation over independent copies of these component channels, we obtain a second set of synthesised binary‐input channels. Similar to the single‐channel case, we prove that as the size of the transformation goes infinity, some of the resulting channels tend to completely noised, and the others tend to noise‐free, where the fraction of the latter approaches the average symmetric capacity of the underlying component channels. For finite‐length polar coding over parallel channels, performance is found to be relied heavily on the specific channel‐mapping scheme. To avoid exhaustive searching, an empirically good scheme that is called equal‐capacity partition channel mapping is proposed and numerical results show that the proposed scheme significantly outperforms random mapping. Further, utilising the above results, a polar coding method for arbitrary code length is proposed, which has potential applications in practical systems.

We propose an opportunistic scheduling method to achieve DoF (degrees of freedom) gain by BIA (blind interference alignment) in block fading K-user 2 × 1 MISO broadcast channel. The optimal scheduling method is obtained by solving a general model of linear integer program. All the users are divided into user pairs to form a 2-user 2 × 1 BIA. Each pair has the same opportunity to be scheduled. When K ≥ 10, the expectation of the achieved DoF can be very close to4/3.

As improved versions of the successive cancellation (SC) decoding algorithm, the successive cancellation list (SCL) decoding and the successive cancellation stack (SCS) decoding are used to improve the finite-length performance of polar codes. In this paper, unified descriptions of the SC, SCL, and SCS decoding algorithms are given as path search procedures on the code tree of polar codes. Combining the principles of SCL and SCS, a new decoding algorithm called the successive cancellation hybrid (SCH) is proposed. This proposed algorithm can provide a flexible configuration when the time and space complexities are limited. Furthermore, a pruning technique is also proposed to lower the complexity by reducing unnecessary path searching operations. Performance and complexity analysis based on simulations shows that under proper configurations, all the three improved successive cancellation (ISC) decoding algorithms can approach the performance of the maximum likelihood (ML) decoding but with acceptable complexity. With the help of the proposed pruning technique, the time and space complexities of ISC decoders can be significantly reduced and be made very close to those of the SC decoder in the high signal-to-noise ratio regime.

We study a repeated auction for the resource allocation problem in OFDM-based cognitive radio networks (CRNs), in which secondary users (SUs) share the primary spectrum under the interference constraints of primary users (PUs). With the inter-cell interference and mutual interference between PUs and SUs, the resource allocation problem is formulated as a non-convex optimization problem. Auction performs well in solving non-convex problems, therefore the interference auction with cooperative bidding is proposed. Moreover, with the theoretical analysis of equilibrium, an implementation algorithm for the auction is developed and the convergence is proved. Simulation results show that the interference auction obtains a good spectrum efficiency improvement and a rapid convergence rate.

In this paper, we propose a distributed call admission control scheme for multiservice OFDMA system. In our proposed call admission control (CAC) scheme, a cell dynamically adjusts optimal new call and handoff acceptance ratios by three steps. First, exchange numbers of calls in handoff area with its adjacent cells. Then, dynamically allocate resource for different types of traffic. Finally, use a Markov queue model and a bidirectional iterative search method (BIS) to calculate the new call and handoff acceptance ratios to obtain a near-maximal network utility which depends on both packets delay and the number of ongoing calls. Simulation results show the performance gain of the proposed scheme.

This paper is concerned with a simplified hard decision feedback equalizer (S-HDFE) for single carrier modulation with cyclic prefix. In this paper, we focus on the coded system in which the feedback symbols are reproduced from hard decisions of channel decoding results. Cholesky decomposition is the key technique for simplifying the feedback filter calculation of S-HDFE. With the same matched filter bound (MFB), the maximum number of the feedback filter taps in S-HDFE is smaller than that in the existing hard decision feedback equalizer with noise prediction (DFE-NP). Therefore, the computational complexity of S-HDFE is lower than DFE-NP with the same MFB. Furthermore, the simulation result shows that if the feedback filter tap number is the same for both equalizers, the BER performance of S-HDFE is better than DFE-NP.

Distributed video coding (DVC) has recently been proposed to reduce the complexity of the encoder, whereas it suffers from the sampling cost of huge amount of image data. To relax such sampling burden, this paper develops a novel sub-sampling distributed video coding (SuDVC) by utilizing compressive sensing (CS) technique. Due to the inherent sparsity in video sources, the video frames are compressively sampled at the encoder. On the other hand, by exploiting the correlation between CS measurements and side information and by performing sparsity recovery, the video frames are recovered at the decoder. When compared with the traditional fully-sampling equivalence, SuDVC enjoys the reduction of transmission rate, the reduction of implementation complexity and the robustness to channel losses, which are verified in the simulations.

Channel estimation is important for coherent detection in orthogonal frequency-division multiplexing (OFDM) systems. Current frequency-domain Kalman filtering (FDKF) channel tracking method requires a large number of pilots, which reduces the spectral efficiency of the system and increases the complexity. In this paper, in order to solve this problem, a new channel estimation method based on the recent methodology of distributed compressed sensing (DCS) and FDKF is proposed. By exploiting the sparse attribute of OFDM channels and introducing DCS, the number of pilots could be reduced greatly, which means more resources are saved for data transmission. Moreover, simulations indicate the proposed method achieves a better performance than conventional FDKF and least square (LS) method.

In this paper, we develop a distributed sub-carrier allocation algorithm with a low complexity in OFDMA multi-cell system , which is decomposed into two sub-problems: inter-cell and intra-cell sub-carrier allocation. During inter-cell process, based on time-variant characteristics and performance differ-ences among cells of sub-carriers, each base station applies Ant Colony Optimization (ACO) to choose available sub-carriers dynamically, which contributes to reduce co-channel interference. According to pheromone associated with channel capacity, the probability of choosing sub-carrier with better capability is higher. Then during intra-cell process, each base station sufficiently uses multi-user diversity to satisfy all users' QoS requirements and greatly increase system throughput. Simulation results show that this algorithm exhibits substantial gains over existing frequency reuse schemes.

We study in this paper a sub-optimal beamforming algorithm for the MISO interference channel based on interference pricing, defined as user's marginal decrease in its utility due to interference. An iterative approach is considered given a set of interference prices and beams. Combining the interference price from other users and channel state information, the transmitter can update the beams to maximize its pure utility, which is defined as its own utility minus loss from other users' utility caused by its interference. Meanwhile, the receiver can update its interference price according to the total received interference. Our results from comprehensive simulations show that this algorithm is vastly superior to the existing beamforming algorithms, e.g. the maximum-ratio transmission (MRT) beamforming scheme, the zero-forcing (ZF) beamforming scheme and the algorithm in in terms of efficiency, convergence and robustness, which can get close to the Pareto edge of the available rate region in.

In this paper, we propose a channel estimation scheme in orthogonal frequency-division multiplexing (OFDM) systems based on the recent methodology of distributed compressed sensing (DCS). The joint sparsity model 2 (JSM-2) in DCS theory and simultaneous orthogonal matching pursuit (SOMP) are both introduced to improve the estimation performance and increase the spectral efficiency. Simulation results indicate that compared to current compressed sensing (CS) methods, the estimation error of our proposed scheme is reduced dramatically at high SNR while the pilot number is still kept small.

This letter considers the relay-assisted multiuser downlink transmission with limited feedback in a wireless cellular network. Taking into account the channel quantization errors, we propose two robust linear beamforming schemes at the relay station based on zero forcing (ZF) criterion and minimum mean square error (MMSE) criterion, respectively. It is noted that the channel direction information (CDI) feedback is sufficient for determining the beamforming vectors in our proposed schemes, which reduces the feedback load. Simulation results show that compared with the conventional ZF beamforming and MMSE beamforming in limited feedback systems, the proposed precoding schemes achieve an improvement in the average bit error rate (BER) performance of the system.

We present the end-to-end performance of dual-hop wireless communication systems with non-regenerative fixed gain relays operating over independent not necessarily identically generalized-K (KG) fading channels. New closed-form expressions are derived for the moments of the end-to-end signal-to-noise ratio (SNR), while the corresponding moment- generating function (MGF) is accurately approximated with the aid of Pade approximants theory. Useful performance criteria are studied; the average end-to-end SNR and the amount of fading, which are expressed in closed form, the average bit-error probability for several coherent, noncoherent, and multilevel modulation schemes, and the outage probability, which are both accurately approximated using the well-known MGF approach. Furthermore, novel closed-form expression is obtained for the gain of semi-blind relays over KGfading channels. The proposed mathematical analysis is complemented by various performance evaluation results, which demonstrate the accuracy of the theoretical approach.

End-to-end performance of multihop wireless communication systems with non-regenerative channel state information (CSI)-assisted relays operating over independent not necessarily identically Weibull fading channels is presented. With the aid of the inequality between harmonic and geometric means, the end-to-end signal-to-noise ratio (SNR) is bounded. By considering the product of rational powers of N Weibull random variables, novel expressions are derived for the moment-generating, probability density, and cumulative distribution functions in closed form. Using these results, convenient closed-form bounds are obtained for the average end-to-end SNR, the channel capacity and the average bit-error probability of multihop wireless communication systems. The tightness of the proposed bounds is verified by performing comparisons between numerical evaluation and computer simulations results.

In spite of the significant performance gain that multiple-input multiple-output (MIMO) transmission can provide in wireless communication, the use of MIMO in realistic systems will lead to large control signaling overhead. Performance comparison of different transmission schemes should consider the control channel (CC) constraints. In this paper, we first build the model of CC for downlink MIMO transmission, which is an extension of previous work in, and then we introduce the concept of CC operation point and propose a principle that performance comparison should be with same CC operation point. Three transmission schemes defined in 3GPP long term evolution (LTE), 1 × 2 maximum ratio combining (MRC), 2 × 2 open-loop transmit diversity (OLTD), and 2 × 2 closed-loop spatial multiplexing (CLSM), are compared through system level simulations, and results show that to keep CC operation point constant, the requirements on CC resource are different, which will further influence the spectral efficiency of the transmission schemes.

In radio resource management of multimedia broadcast/multicast service (MBMS), user equipment (UE) feedback can be used by evolved Node B (eNB) to improve the radio spectral efficiency. As UE feedback is costly we here suggest schemes to reduce the uplink feedback while maintaining the spectral efficiency. For the 3 schemes analyzed in this paper we utilize the special characteristic of a broadcast channel, that spectral efficiency is mainly determined by the UE with lowest channel quality. The performance of the suggested schemes are evaluated with long term evolution (LTE) system simulation and we conclude that using geometry for selecting feedback UEs is the optimal solution and 4 feedback UEs are enough to meet the coverage requirement.

Multimedia Broadcast/Multicast Service (MBMS) based on point-to-multipoint (p-t-m) radio transmission was introduced by 3GPP to deliver broadcast service to a group of user equipments (UEs) in a cellular system. As a broadcast system with uplink feedback, its performance is limited by UEs in poor connections. To utilize the limited radio resource more efficiently, a simple but effective mechanism is proposed to improve the system throughput by sacrificing those UEs that are expensive to cover, which means the cost of this improvement is coverage degradation. We set a threshold for the SINR of UEs, and uplink feedback from UEs with G-factor lower than the threshold will be discarded in packet scheduling and link adaptation. Compared with other methods to increase the throughput, this mechanism has two advantages. First, the coverage can be exactly controlled by adjusting the threshold. Second, much higher throughput gain can be achieved for the same cost. Simulation results show that throughput will be increased by 22% with reasonable coverage degradation when there are 10 UEs per sector. Larger gains can be achieved with more UEs per sector.

Discrete Fourier transform (DFT)-based least square (LS) channel estimation with phase shifted pilots (PSP) is a practical method in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. In this paper, a real number pilot sequence is presented to replace PSP in time-domain LS channel estimation algorithm, which can reduce computation and save memory while keeping the precision. Furthermore, using discrete Hartley transform (DHT) in LS channel estimation algorithm, a simpler estimator is derived. The simulation results demonstrate the effectiveness of the proposed pilot sequence and the DHT-based channel estimation algorithm.

Based on interference temperature model, the problem of secondary spectrum sharing can be formulated as a power optimization problem at physical layer. In this paper, we consider decentralized cognitive radio networks, and we especially focus on the spectrum sharing scenario where multiple measurement points are located in the licensed system. Game theory is used to investigate the distributed power control for providing the maximum throughput in cognitive radio networks. There are two aspects that should be considered in the design of the payoff function for each player, one is counteracting negative externalities in cognitive radio networks, and the other is satisfying all the interference temperature limits from measurement points. A tax-based power control game algorithm is introduced to implement power allocation optimization in a distributed mode, and guarantees the convergence to globally optimal power allocations.

Conventional heterogeneous-traffic scheduling schemes utilize zero-delay constraint for real-time services, which aims to minimize the average packet delay among real-time users. However, in light or moderate load networks this strategy is unnecessary and leads to low data throughput for non-real-time users. In this paper, we propose a heuristic scheduling scheme to solve this problem. The scheme measures and assigns scheduling priorities to both real-time and non-real-time users, and schedules the radio resources for the two user classes simultaneously. Simulation results show that the proposed scheme efficiently handles the heterogeneous-traffic scheduling with diverse QoS requirements and alleviates the unfairness between real-time and non-real-time services under various traffic loads.

In this paper, we proposed a revenue-based low-delay and efficient (R-LDE) algorithm for downlink packet scheduling in OFDMA systems. In actual operation of the network, telecommunication operators always expect the most revenue. Therefore, a simple revenue model is introduced and integrated into our proposed packet scheduling algorithm. The significances of R-LDE algorithm are threefold: firstly, it can guarantee the delay requirement of certain traffic; secondly, it can keep the efficiency of the system; thirdly, it considers the revenue of the telecommunication operators. The performance of R-LDE is evaluated in terms of packet delay, throughput and revenue with actual traffic models. It is then compared with conventional multi-carrier proportional fairness (MC-PF) algorithm and max SNR algorithm with mixed traffics such as FTP and video traffics. Simulation results show that the R-LDE algorithm can achieve the most scheduling revenue and performs much better than the algorithms stated before in terms of packet delay and throughput with video traffic at the cost of slight deterioration of FTP traffic performance.

In this paper, a fair and simple resource allocation scheme is proposed for orthogonal frequency-division multiple-access distributed wireless communication systems (OFDMA-DWCS). Instead of finding global optimal solution, we hierarchically decouple the subcarrier and power allocation problem into two subproblems. A distributed scheduling is employed on two levels: access-point-level (AP-level) and user-level. In AP-level scheduling, the subcarriers are roughly allocated by the central unit (CU) to different APs. In user-level scheduling, a further subcarrier and power allocation is performed by each AP for its underlying users. We adopt Nash bargain solution fairness criterion in our scheme, which ensures a good tradeoff between fairness and efficiency on both the AP-level and the user-level. Simulation results demonstrate that the distributed scheme can achieve comparable overall system rate to that of centralized algorithms with much reduced complexity.

A novel resource allocation problem for min-rate guaranteed services is studied in orthogonal frequency division multiplexing (OFDM) distributed antenna systems (DAS) by this paper. In addition to multiuser diversity as well as conventional space diversity (such as multiple-input multiple-output (MIMO) -OFDM), distributed multiantenna diversity and time diversity are also exploited by the proposed algorithm. The proposed allocation algorithm iteratively assigns subcarriers to users to maximize rate-sum capacity subject to min-rate constraints. Simulated results show the proposed algorithm can fully utilize all degrees of freedom including antenna, user, time, and frequency to ensure users' min-rate transmission. Consequently, OFDM DAS outperforms OFDM co-located antenna systems (CAS) in terms of rate-sum capacity due to distributed antenna deployment.

Multiuser downlink beamforming methods have been studied recently. As known, the MIMO broadcast channel capacity can be achieved by dirty paper coding (DPC). However, the high complexity of DPC is hard to implement. Linear zero-forcing beamforming strategy can achieve the same asymptotic sum-rate as that of DPC, when the number of users goes to infinity. For the linear zero-forcing system, (D. Bartolome et al., 2004) gives several power allocation methods among users for different objectives. The above power allocation has an assumption of sum power constraint (SPC), however, recently some researcher study a more practical power constraint name per-antenna power constraint (PAPC). The optimum power allocation aims for maximize the sum-rate under the PAPC is given in (F. Boccardi et al., 2006). In this paper, we find that if the transmit antenna number M is larger than the user number K, a multi-level zero-forcing method can be adopted, and a multi-layer structure is proposed for realizing the method. A sub-optimal power allocation algorithm is also proposed to find the power allocation matrix. Simulation result shows that the proposed multi-level zero-forcing method can achieve a higher sum-capacity than that of (F. Boccardi et al., 2006) under PAPC.

We propose a new connection admission control algorithm called DAC for real-time services in packet-switched OFDM wireless cellular networks. The main idea of DAC is to use maximum acceptance ratio for real-time services according to the desired packet-level and call-level QoS requirements. The control mechanism is periodical. At the beginning of the control period, the admission controller gathers the loading information from its own and neighboring cells. Then, according to the information, the maximum acceptance ratio is calculated by using a Markovian queueing model and time-dependent distribution of the number of connections. Finally the admission controller uses the computed ratio to admit connection requests in this control period. Extensive simulation results show that our algorithm satisfies packet-level and call-level QoS requirements for real-time services while maintaining high bandwidth utilization.

This paper deals with the problem of downlink scheduling in SDMA packet access networks. The performance of the SDMA system is predominantly affected by the mutual interference between users. In order to ensure the quality of service (QoS) and enhance the resource utilization, the scheduler needs to jointly consider the constraints from the physical layer (spatial separability for instance) and the data link layer (such as QoS requirements). In this paper, we propose a simple cross-layer downlink scheduler for SDMA based systems. As an example, it is implemented on an OFDMA/SDMA system. Analysis and simulations are given to show the effectiveness of our proposal.

This paper formulates resource allocation problem in orthogonal frequency division multiplexing (OFDM) distributed antenna systems (DAS) by extending conventional one in OFDM co-located antenna systems (CAS). The algorithm to achieve maximum rate-sum capacity is derived, and the achievable capacity of a single user is also given by theoretical analysis. Simulated and theoretical results show that OFDM DAS can achieve much more rate-sum capacity than OFDM CAS, and the performance gain increases with the number of antennas in systems.

In this paper, we propose a joint admission control and bandwidth allocation scheme for packet-switched multiservices cellular networks. The main idea of the scheme is to adjust the acceptance ratios and the bandwidth allocated for different types of services in order to maintain maximum bandwidth utilization while guaranteeing quality of service (QoS) requirements for users. First, we define a total user utility function as the measure of how efficiently the bandwidth resource is used. Then, by using a Markovian queueing model, we obtain the optimal connection acceptance ratios and bandwidth allocation strategy that maximize total user utility. Simulation results demonstrate that using our scheme, bandwidth resource is most efficiently utilized and at the same time, the call-level and packet-level QoS requirements for both voice and data users are guaranteed.

In this paper, list detection based on lattice reduction (LDLR) in MIMO systems is proposed. By generating a list of candidate transmit symbol vectors we can improve the performance of linear detection based on lattice reduction and successive interference cancellation based on lattice reduction significantly. The performance improvement is demonstrated by computer simulations, which also show that a list with two entries is enough for the MMSE based detection to approach the maximum likelihood (ML) detection at high signal-to-noise ratio (SNR) in case of (4,4) MIMO

Iterative list sphere decoding (LSD) can achieve near capacity on a multiple-antenna channel, however, with a rather high complexity. In order to reduce the complexity, in this paper, linear detection-based iterative soft interference cancellation (ISIC) instead of LSD is applied at the second and later iterations. We propose to exploit extrinsic information transfer (EXIT) chart analysis to select a specific ISIC to alleviate large performance degradation. Here MF-based and MMSE-base iterative soft interference cancellations (ISIC) are selected to be combined with the LSD. Simulation shows that the iterative combined detections have little signal-to-noise ratio (SNR) loss and a much lower complexity in comparison with iterative LSD

In this paper, a novel MIMO detection named decision feedback aided detection based on lattice reduction (DFDLR) is proposed. Lattice reduction has been proposed to be exploited in signal detection in multiple antenna systems. However, lattice reduction transforms the QAM constellation cube to be a parallelotope, and it is difficult to determine the transformed QAM boundaries, which results in sub-optimal performance with simple quantization. We propose to exploit decision feedback to enhance the QAM boundary control. Simulations show that decision feedback can improve the performance of lattice reduction-based linear detection (LD) and lattice reduction-based successive interference cancellation (SIC) significantly especially with zero forcing (ZF) criterion. It is also shown by simulation that the proposed scheme in conjunction with SIC can approach maximum likelihood (ML) detection with MMSE criterion at high signal-to-noise ratio (SNR)

The timing relationship between base stations is asynchronous in a W-CDMA system. So the three-step cell search algorithm is introduced in 3GPP protocols in order to fast identify the special base station. The first step, primary synchronous channel (P-SCH) acquisition, is very important, but the conventional matched filter consumes much large hardware resources. Because the P-SCH is composed of the generalised hierarchical Golay (GHG) sequence, we propose a novel GHG matched filter according to the its structural property, which extremely reduces the hardware complexity. Generally speaking, such a GHGMF structure is significant for engineering applications.