In wideband near-field arrays, frequency-dependent array responses cause wavefronts at different frequencies to deviate from that at the center frequency, producing beam squint and degrading multi-user performance. True-time-delay (TTD) circuits can realign the frequency dependence but require large delay ranges and intricate calibration, limiting scalability. Another line of work explores one- and two-dimensional array geometries, including linear, circular, and concentric circular, that exhibit distinct broadband behaviors such as different beam-squint sensitivities and focusing characteristics. These observations motivate adapting the array layout to enable wideband-friendly focusing and enhance multi-user performance without TTD networks. We propose a movable antenna (MA) aided architecture based on hierarchical sub-connected hybrid beamforming (HSC-HBF) in which antennas are grouped into tiles and only the tile centers are repositioned, providing slow geometric degrees of freedom that emulate TTD-like broadband focusing while keeping hardware and optimization complexity low. We show that the steering vector is inherently frequency dependent and that reconfiguring tile locations improves broadband focusing. Simulations across wideband near-field scenarios demonstrate robust squint suppression and consistent gains over fixed-layout arrays, achieving up to 5\% higher sum rate, with the maximum improvement exceeding 140\%.

Affine frequency division multiplexing (AFDM), an emerging multi-carrier modulation scheme, has garnered significant attention due to its resilience to Doppler shifts and capability to achieve full diversity in doubly dispersive channels. However, existing data detection algorithms for AFDM systems face a significant trade-off between computational complexity and accuracy. In this paper, a novel low-complexity data detection scheme, termed the soft-feedback detector (SFD), is proposed. Particularly, building upon a maximum ratio combining (MRC) estimator framework, the SFD leverages the a priori symbol distribution to mitigate error propagation during iterative detection. Specifically, soft-decision feedback is incorporated as extrinsic information derived from the log-likelihood ratios of the transmitted symbols. As a result, the proposed detector significantly enhances detection accuracy while maintaining low computational complexity. Simulation results demonstrate that the SFD consistently outperforms benchmark decision-feedback detectors. In particular, compared with the conventional MRC detector, the proposed scheme achieves approximately a 3 dB signal-to-noise ratio (SNR) gain at the bit error rate (BER) of $10^{-3}$.

The integration of reconfigurable intelligent surfaces (RIS) with relays can enhance the quality and coverage of wireless communication. However, relays introduce extra energy consumption, and the transmission quality of RIS-aided relay systems can be further improved. Polarization-adjusted convolutional (PAC) codes exhibit superior error-correction performance at short code lengths, demonstrating potential to enhance the performance of RIS-aided relay systems. In this paper, we consider an energy-efficient RIS-aided coded cooperation (RIS-CC) system based on PAC codes to achieve wide coverage transmission with high reliability and low latency. First, a parity-check (PC) aided PAC-CC (PC-PAC-CC) scheme is designed to build the considered RIS-CC system for efficient relay forwarding. Using PC, the proposed PC-PAC-CC scheme avoids the unnecessary energy consumption on relays at high signal-to-noise ratio (SNR). Second, we propose a PC-aided low-complexity list (PC-LCL) decoding algorithm to reduce decoding complexity at receivers, thus decreasing transmission latency. Moreover, the proposed PC-LCL decoding algorithm is implemented by software to demonstrate its practicability. Third, we derive closed-form expressions for the tight upper bound of ergodic channel capacity (ECC) in the considered RIS-CC system under Nakagami-mfading, and conduct its asymptotic analysis at high SNR. Test results show that 1) the proposed PC-PAC-CC scheme has better error-correction performance than state-of-the-art (SOTA) work, and reduces energy consumption on relays by up to 81.02%; 2) the proposed PC-LCL software decoder has a 35.29% reduction on latency compared with the SOTA PAC software decoder. Besides, the derived closed-form expressions are verified by simulations.

The soft-output successive cancellation list (SOSCL) decoder provides a methodology for estimating the aposteriori probability log-likelihood ratios by only leveraging the conventional SCL decoder of polar codes. However, the sequential decoding nature of SCL introduces high decoding latency to SOSCL. In this paper, we incorporate node-based fast decoding into the SO-SCL framework. After addressing the challenge of soft output extraction in special node decoding, we proposed the soft-output fast SCL (SO-FSCL) decoding algorithm, along with its log-domain implementation and hardware-friendly version. The proposed SO-FSCL decoder can be regarded as an addon extension to FSCL decoder, enabling us to autonomously choose whether to output only hard decisions like FSCL or to provide additional soft outputs. Latency and complexity analyses demonstrate that SO-FSCL can significantly reduce, for example, decoding time steps by 81.8% (with unlimited resources), the number of additions by 41.3%, and the number of comparisons by 46.4%. Meanwhile, simulation results indicate that SO-FSCL delivers almost the same soft-output performance as SO-SCL, outperforming other soft-output polar decoders, especially in scenarios involving iterative decoding.

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems provide enhanced coverage and capacity for next-generation wireless networks. However, CF-mMIMO systems face significant challenges in downlink power allocation (PA) due to imperfect channel state information (CSI), severe multi-user interference (MUI), and high computational complexity. To address these issues, rate-splitting multiple access (RSMA) is adopted as a robust interference management strategy. Accordingly, this paper proposes an unsupervised and scalable graph neural network (GNN) framework for PA in rate-splitting CF-mMIMO (RS-CF-mMIMO) systems, relying exclusively on large-scale fading (LSF) coefficients without instantaneous CSI. To resolve the dimensionality mismatch in dynamic networks, we introduce a slice-based adaptive layer that projects variable-dimension features into a fixed latent space. This mechanism enables a unified model to generalize across diverse topologies without retraining. Within this architecture, the sum spectral efficiency (SE) is maximized under per-AP power constraints, assuming maximum-ratio precoding for common streams and regularized zero-forcing precoding for private streams. We also derive a weighted minimum mean-square error-alternating direction method of multipliers (WMMSE-ADMM) algorithm as a performance upper bound. Extensive simulations verify that the proposed GNN framework achieves near-optimal SE and outperforms unsupervised deep neural networks (DNNs) across diverse system sizes and pilot assignment schemes. Furthermore, the scalable variant maintains robust performance while reducing the trainable parameter count by over 57% relative to DNNs and decreasing inference latency by up to three orders of magnitude compared with WMMSE-ADMM.

Mild cognitive impairment (MCI) is the prodromal stage of dementia involving complex interactions between the brain and peripheral organs. Emerging evidence indicates that heart dysfunction and gut microbiota dysbiosis can contribute to MCI pathogenesis. Yet, these discoveries of cross-organ interactions have not been applied to assist MCI diagnosis. In this work, we propose a novel diagnostic framework that exploits the interactions of brain, heart, and gut using whole-body PET images to guide MCI diagnosis for scenarios when only brain MRI, PET, or PET&MRI are available. Specifically, we collected a multi-cohort, multi-modal dataset comprising 1,545 whole-body PET images, 6,010 brain MR images, and 2,446 brain PET images from eight data centers. Organ-specific image encoders are first pretrained for the brain, heart, and gut individually. Then, to effectively align and integrate brain, heart, and gut features, we introduce positional prompts to act as anatomical-level attention to highlight disease-relevant spatial regions, and further develop hierarchical Transformers to model brain-heart, brain-gut, and brain-heart-gut interactions. Finally, to achieve MCI diagnosis using only brain images, we transfer the above brain-heart-gut model to a brain-only model via an introduced multi-level knowledge distillation scheme, including sample-level contrastive distillation, group-level distribution alignment, and response-level supervision. Extensive experiments on multi-center data demonstrate the superiority of our method over the state-of-the-art methods by resorting to effective integration of heart and gut interactions for MCI diagnosis.

Modeling the wireless radiance field (WRF) is fundamental to modern communication systems, enabling key tasks such as localization, sensing, and channel estimation. Traditional approaches, which rely on empirical formulas or physical simulations, often suffer from limited accuracy or require strong scene priors. Recent neural radiance field (NeRF)-based methods improve reconstruction fidelity through differentiable volumetric rendering, but their reliance on computationally expensive multilayer perceptron (MLP) queries hinders real-time deployment. To overcome these challenges, we introduce Gaussian splatting (GS) to the wireless domain, leveraging its efficiency in modeling optical radiance fields to enable compact and accurate WRF reconstruction. Specifically, we propose SwiftWRF, a deformable 2D Gaussian splatting framework that synthesizes WRF spectra at arbitrary positions under single-sided transceiver mobility. SwiftWRF employs CUDA-accelerated rasterization to render spectra at over 100 k FPS and uses the lightweight MLP to model the deformation of 2D Gaussians, effectively capturing mobility-induced WRF variations. In addition to novel spectrum synthesis, the efficacy of SwiftWRF is further underscored in its applications in angle-of-arrival (AoA) and received signal strength indicator (RSSI) prediction. Experiments conducted on both real-world and synthetic indoor scenes demonstrate that SwiftWRF can reconstruct WRF spectra up to 500x faster than existing state-of-the-art methods, while significantly enhancing its signal quality.

In this work, we introduce the integration of generalized low-density parity-check (GLDPC) codes with short polar component codes, termed GLDPC codes with polar component codes (GLDPC-PC). A recently proposed soft-input soft-output (SISO) decoder for polar-like codes enables effective iterative belief propagation decoding for GLDPC-PC. This SISO decoder after a post-processing exhibits little performance loss to the optimal SISO decoder when all the variable nodes have relatively low degrees. A three-step method is introduced to design protograph-based GLDPC codes. The constructed GLDPC codes are compared with 5G LDPC codes. They exhibit little performance loss in the waterfall region and possess better error floor with less iterations.

The growing demand for high-quality point cloud transmission over wireless networks presents significant challenges, primarily due to the large data sizes and the need for efficient encoding techniques. In response to these challenges, we introduce a novel system named Deep Point Cloud Semantic Transmission (PCST), designed for end-to-end wireless point cloud transmission. Our approach employs a progressive resampling framework using sparse convolution to project point cloud data into a semantic latent space. These semantic features are subsequently encoded through a deep joint source-channel (JSCC) encoder, generating the channel-input sequence. To enhance transmission efficiency, we use an adaptive entropy-based approach to assess the importance of each semantic feature, allowing transmission lengths to vary according to their predicted entropy. PCST is robust across diverse Signal-to-Noise Ratio (SNR) levels and supports an adjustable rate-distortion (RD) trade-off, ensuring flexible and efficient transmission. Experimental results indicate that PCST significantly outperforms traditional separate source-channel coding (SSCC) schemes, delivering superior reconstruction quality while achieving over a 50% reduction in bandwidth usage.

Rate splitting multiple access (RSMA) is regarded as a crucial and powerful physical layer (PHY) paradigm for nextgeneration communication systems. Particularly, users employ successive interference cancellation (SIC) to decode part of the interference while treating the remainder as noise. However, conventional RSMA systems rely on fixed-position antenna arrays, limiting their ability to fully exploit spatial diversity. This constraint reduces beamforming gain and significantly impairs RSMA performance. To address this problem, we propose a movable antenna (MA)-aided RSMA scheme that allows the antennas at the base station (BS) to dynamically adjust their positions. Our objective is to maximize the system sum rate of common and private messages by jointly optimizing the MA positions, beamforming matrix, and common rate allocation. To tackle the formulated non-convex problem, we apply fractional programming (FP) and develop an efficient two-stage, coarse-to-fine-grained searching (CFGS) algorithm to obtain high-quality solutions. Numerical results demonstrate that, with optimized antenna adjustments, the MA-enabled system achieves substantial performance and reliability improvements in RSMA over fixedposition antenna setups.

Integrated sensing and communication (ISAC) is a very promising technology designed to provide both high rate communication capabilities and sensing capabilities. However, in Massive Multi User Multiple-Input Multiple-Output (Massive MU MIMO-ISAC) systems, the dense user access creates a serious multi-user interference (MUI) problem, leading to degradation of communication performance. To alleviate this problem, we propose a decentralized baseband processing (DBP) precoding method. We first model the MUI of dense user scenarios with minimizing Cramér-Rao bound (CRB) as an objective function. Hybrid precoding is an attractive ISAC technique, and hybrid precoding using Partially Connected Structures (PCS) can effectively reduce hardware cost and power consumption. We mitigate the MUI between dense users based on Thomlinson-Harashima Precoding (THP). We demonstrate the effectiveness of the proposed method through simulation experiments. Compared with the existing methods, it can effectively improve the communication data rates and energy efficiency in dense user access scenario, and reduce the hardware complexity of Massive MU MIMO-ISAC systems. The experimental results demonstrate the usefulness of our method for improving the MUI problem in ISAC systems for dense user access scenarios.

In this paper, we propose a novel multi-task, multi-link relay semantic communications (MTML-RSC) scheme that enables the destination node to simultaneously perform image reconstruction and classification with one transmission from the source node. In the MTML-RSC scheme, the source node broadcasts a signal using semantic communications, and the relay node forwards the signal to the destination. We analyze the coupling relationship between the two tasks and the two links (source-to-relay and source-to-destination) and design a semantic-focused forward method for the relay node, where it selectively forwards only the semantics of the relevant class while ignoring others. At the destination, the node combines signals from both the source node and the relay node to perform classification, and then uses the classification result to assist in decoding the signal from the relay node for image reconstructing. Experimental results demonstrate that the proposed MTML-RSC scheme achieves significant performance gains, e.g., 1.73 dB improvement in peak-signal-to-noise ratio (PSNR) for image reconstruction and increasing the accuracy from 64.89% to 70.31 % for classification.

The fluid antenna (FA)-enabled multiple-input multiple-output (MIMO) system based on index modulation (IM), referred to as FA-IM, significantly enhances spectral efficiency (SE) compared to the conventional FA-assisted MIMO system. To improve the performance in addressing the high spatial correlations between multiple activated ports, this paper proposes an innovative FA grouping-based IM (FAG-IM) system. Specifically, considering the characteristics of the FA two-dimensional (2D) surface structure and the spatially correlated channel model in FA-assisted MIMO systems, a block grouping method is adopted, where adjacent ports are assigned to the same group. Consequently, different groups independently perform port index selection and constellation symbol mapping, with only one port being activated within each group during each transmission interval. Then, a closed-form average bit error probability (ABEP) upper bound is derived for the proposed system. Numerical results show that, compared to state-of-the-art systems, the FAG-IM system consistently achieves substantial performance gains.

Movable antennas (MAs) have shown significant potential in improving the performance of integrated sensing and communication (ISAC) systems. However, their application in integrated and cost-effective full-duplex (FD) monostatic systems remains underexplored. To bridge this research gap, we develop an MA-ISAC model within an FD monostatic framework, where the self-interference channel is modeled as a function of the antenna position vectors under the near-field channel condition. This model enables antenna position optimization for maximizing the weighted sum of communication capacity and sensing mutual information. The resulting optimization problem is non-convex making it challenging to solve optimally. To address this, we employ the fractional programming (FP) method and propose an alternating optimization (AO) algorithm that jointly optimizes the beamforming and antenna positions at the transceivers. Specifically, closed-form solutions for the transmit and receive beamforming matrices are derived using the Karush-Kuhn-Tucker (KKT) conditions, and a novel coarse-to-fine grained searching (CFGS) approach is used to determine high-quality sub-optimal antenna positions. Numerical results demonstrate that with strong self-interference cancellation (SIC) capabilities, MAs significantly enhance the overall performance and reliability of the ISAC system when utilizing our proposed algorithm, compared to conventional fixed-position antenna designs.

The soft-output successive cancellation list (SO-SCL) decoder provides a methodology for estimating the a-posteriori probability log-likelihood ratios by only leveraging the conventional SCL decoder for polar codes. However, the sequential nature of SCL decoding leads to a high decoding latency for the SO-SCL decoder. In this paper, we propose a soft-output fast SCL (SO-FSCL) decoder by incorporating node-based fast decoding into the SO-SCL framework. Simulation results demonstrate that the proposed SO-FSCL decoder significantly reduces the decoding latency without loss of performance compared with the SO-SCL decoder.

Federated semi-supervised learning (FSSL) is primarily challenged by two factors: the scarcity of labeled data across clients and the non-independent and identically distribution (non-IID) nature of data among clients. In this paper, we propose a novel approach, diffusion model-based data synthesis aided FSSL (DDSA-FSSL), which utilizes a diffusion model (DM) to generate synthetic data, bridging the gap between heterogeneous local data distributions and the global data distribution. In DDSA-FSSL, clients address the challenge of the scarcity of labeled data by employing a federated learning-trained classifier to perform pseudo labeling for unlabeled data. The DM is then collaboratively trained using both labeled and precision-optimized pseudo-labeled data, enabling clients to generate synthetic samples for classes that are absent in their labeled datasets. This process allows clients to generate more comprehensive synthetic datasets aligned with the global distribution. Extensive experiments conducted on multiple datasets and varying non-IID distributions demonstrate the effectiveness of DDSA-FSSL, e.g., it improves accuracy from 38.46% to 52.14% on CIFAR-10 datasets with 10% labeled data.

Rapid expansion of the Internet of Unmanned Agents (IUAs) has led to a dramatic increase in demand for network capacity. Multicast-broadcast transmission, as an one-to-many communication paradigm, efficiently alleviates resource consumption by delivering common content to massive users over shared time-frequency resources. To address the flexible and large-scale networking requirements under IUA scenarios, this article introduces the multicast broadcast single frequency network (MBSFN) framework deploying aerial base stations (ABSs) based on autonomous aerial vehicles (AAVs). To deal with energy consumption challenges under AAVs’ coupled operational constraints, this article proposes a multiagent deep reinforcement learning (MADRL) approach, grounded in the multiagent deep deterministic policy gradient (MADDPG) algorithm, to optimize energy efficiency (EE) while ensure user Quality of Service (QoS) in ABS MBSFN systems. By modeling ABS as cooperative agents, the MADDPG algorithm dynamically adjusts discrete power levels, integrating straight-through estimators (STEs), reparameterization techniques, and Gumbel distribution sampling to resolve challenges in discrete action space optimization. The simulation results demonstrate that the proposed scheme achieves an average EE of 88.7% of the theoretical optimum and outperforms the single-agent DDPG method by up to 30%. Further analysis in diverse UE distributions validates the robustness and practicality of the proposed approach, highlighting its potential for energy-efficient multicast-broadcast deployments in AAV-enabled networks.

Fluid antenna multiple access (FAMA), enabled by the fluid antenna system (FAS), offers a new and straightforward solution to massive connectivity. Previous results on FAMA were primarily based on narrowband channels. This paper studies the adoption of FAMA within the fifth-generation (5G) orthogonal frequency division multiplexing (OFDM) framework, referred to as OFDM-FAMA, and evaluate its performance in broadband multipath channels. We first design the OFDM-FAMA system, taking into account 5G channel coding and OFDM modulation. Then the system’s achievable rate is analyzed, and an algorithm to approximate the FAS configuration at each user is proposed based on the rate. Extensive link-level simulation results reveal that OFDM-FAMA can significantly improve the multiplexing gain over the OFDM system with fixed-position antenna (FPA) users, especially when robust channel coding is applied and the number of radio-frequency (RF) chains at each user is small.

Existing works on machine learning (ML)-empowered wireless communication primarily focus on monolithic scenarios and single tasks. However, with the blooming growth of communication task classes coupled with various task requirements in future 6G systems, this working pattern is obviously unsustainable. Therefore, identifying a groundbreaking paradigm that enables a universal model to solve multiple tasks in the physical layer within diverse scenarios is crucial for future system evolution. This paper aims to fundamentally address the curse of ML model generalization across diverse scenarios and tasks by unleashing multi-modal feature integration capabilities in future systems. Given the universality of electromagnetic propagation theory, the communication process is determined by the scattering environment, which can be more comprehensively characterized by cross-modal perception, thus providing sufficient information for all communication tasks across varied environments. This fact motivates us to propose a transformative two-stage multi-modal pre-training and downstream task adaptation paradigm. In the pre-training stage, we introduce a multi-modal two-tower model and a corresponding contrastive learning method to integrate the explicit description of the scattering environment and implicit channel state information (CSI) into a universal representation, which encapsulates rich high-level knowledge and can be leveraged for all downstream tasks in different scenarios. Additionally, we present two specially designed model structures to enhance the interaction of communication modalities. In the second stage, based on the frozen pre-trained model, we propose a direct method and a pluggable method for flexible and low-cost task adaptation. Experimental results demonstrate that our proposed approach significantly outperforms benchmarks in both task performance and tuning parameter size for exemplary sub-tasks in unseen scenarios.

Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios with doubly selective channels. However, the existing channel estimation schemes for OTFS usually rely on high-power pilots, which cause the issue of high peak-to-average power ratio (PAPR). In this paper, a channel estimation scheme for OTFS is designed, utilizing the Zadoff-Chu (ZC) sequence as the pilot without any guard symbols to reduce the PAPR effectively. Furthermore, an iterative ZC-sequence-based estimation algorithm is proposed. It can accurately estimate channels with integer and fractional Doppler shifts, irrespective of whether ideal or rectangular waveforms are employed. The proposed scheme performs the channel estimation using a correlation-based method. The correlation’s interference, caused by the data, pilot, and noise, is mitigated by performing channel estimation and data detection alternately. Simulation results show that it has a significantly lower PAPR in the time domain and superior channel estimation performance with both integer and fractional Doppler.

Spatial modulation (SM) transmits additional information bits by the selection of antennas. Generalized spatial modulation (GSM), as an advanced type of SM, can be divided into diversity and multiplexing (MUX) schemes according to the symbols carried on the selected antennas are identical or different. Recently, reconfigurable intelligent surface (RIS) assisted SM exhibits better reception performance compared to conventional SM. To overcome the limitations of SM, this paper combines GSM with RIS and proposes the RIS-aided receive generalized spatial modulation (RIS-RGSM) scheme. The RIS-RGSM diversity scheme is realized via a simple improvement based on the state-of-the-art scheme. To further increase the transmission rate, a novel RIS-RGSM MUX scheme is proposed, where the reflection phase shifts and on/off states of RIS elements are configured to achieve bit mapping. The theoretical bit error rate (BER) of the proposed scheme is derived and agrees well with the simulation results. Numerical simulations show that the RIS-RGSM MUX scheme has better BER performance than the diversity scheme. The proposed scheme can significantly increase the transmission rate and maintain good performance compared to the existing scheme under a limited number of antennas.

Spatial Modulation (SM), as a type of index modulation (IM), can utilize the index of the transmit antenna (TA) to transmit additional information. In this paper, to improve the performance of SM, a non-uniform constellation (NUC) and pre-scaling coefficients optimization design scheme is proposed. The bit-interleaved coded modulation (BICM) capacity calculation formula of SM system is firstly derived. The constellation and pre-scaling coefficients are optimized by maximizing the BICM capacity without channel state information (CSI) feedback. Optimization results are given for the multiple-input-single-output (MISO) system with Rayleigh channel. Simulation result shows the proposed scheme provides a meaningful performance gain compared to conventional SM system without CSI feedback. The proposed optimization design scheme is a general scheme that can be used as a reference and easily extended to more scenarios with various SM schemes, so it is a promising design paradigm for future wireless communication to achieve high-efficiency.

The symbol-level precoding (SLP) technique can enhance the performance in multi-user wireless communication systems because of its ability to convert harmful multi-user interference (MUI) into beneficial ones. However, the tremendous computational complexity of conventional symbol-level precoding designs severely hinders practical implementations. This paper proposes an SLP design scheme based on unsupervised learning in a multiple-input multiple-output (MIMO) downlink system. In the SLP design scheme, the loss function is first designed to improve performance by pushing the received signal further away from the decision boundaries into a constructive region. An efficient symbol-level precoding network (SLP-Net) is introduced to optimize the SLP under the power constraint and adapt amplitude phase modulation. Numerical results highlight that the optimized SLP design scheme provides meaningful performance gain in the MIMO downlink system. The proposed SLP design scheme can be an efficient technology to perform better in the future 6G.

Multi-User Multiple-Input Multiple-Output (MU-MIMO) antenna arrays are considered a crucial technology for future wireless communication systems. However, precoding for MU-MIMO meets significant challenges. To tackle this issue, this paper introduces a novel star decentralized precoding algorithm, aiming to decentralize part of the precoded calculations from the central unit (CU) to the decentralized units (DUs) and reduce the computational complexity of the CU. Then, we apply the Zero Forcing Tomlinson-Harashima precoding (ZF-THP) algorithm to star decentralized baseband processing (DBP) for enhanced transmission rate, and this algorithm has the same performance as centralized precoding but with reduced CU complexity. Furthermore, we propose the star decentralized minimum mean square error THP (sDMMSE-THP) algorithm to enhance system performance further. Extensive simulation data validate the effectiveness of our proposed scheme.

Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for eliminating noise leads us to wonder whether DM can be applied to wireless communications to help the receiver mitigate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for semantic communications over wireless channels in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We derive corresponding training and sampling algorithms of CDDM according to the forward diffusion process specially designed to adapt the channel models and theoretically prove that the well-trained CDDM can effectively reduce the conditional entropy of the received signal under small sampling steps. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC) for image transmission and design a three-stage training algorithm for combining them. Extensive experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system, the traditional JPEG2000 with low-density parity-check (LDPC) code approach and other benchmarks in diverse scenarios.

Diffusion models (DM) can gradually learn to re-move noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for removing noise leads us to wonder whether DM can be applied to wireless communications to help the receiver eliminate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for wireless communications in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We design corresponding training and sampling algorithms for the forward diffusion process and the reverse sampling process of CDDM. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC). Experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system and the traditional JPEG2000 with low-density parity-check (LDPC) code approach.

For low earth orbit (LEO) satellite communications, the robustness to high mobility and the low peak-to-average power ratio (PAPR) are two essential requirements. Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios with doubly selective channels. However, the existing channel estimation schemes for OTFS usually contain high-power pilots, which cause high PAPR, while the current PAPR reduction schemes for OTFS usually ignore the impacts on channel estimation. This work proposes a novel channel estimation scheme utilizing a Zadoff-Chu (ZC) sequence as the pilot, with which the PAPR can be effectively reduced by proper pilot alignment. Then, an iterative ZC-sequence-based estimation algorithm is proposed, which adopts a correlation-based algorithm and a message passing (MP) algorithm to detect the pilot and data alternately. Simulation results show that the proposed scheme has a significantly lower PAPR in the time domain and superior channel estimation performance.

Recently, deep learning has been adopted to improve the performance of the belief propagation algorithm in polar codes, namely, the neural belief propagation decoder. By attaching trainable parameters to each edge on a tanner graph, this decoder effectively weakens the effect of short cycles. In this paper, we will show that the decoder can be further improved with our newly-designed labeling scheme. Instead of groundtruth transmitted codewords, our scheme utilizes codewords generated from a high-performance decoder (i.e., noise-aided belief propagation list decoder) to construct loss functions. Besides, to reduce the labeling complexity, the ground-truth transmitted codewords are integrated into the decoder as a list and compared with the other lists with the principle of minimum Euclidean distance. Numerical simulation results demonstrate that the proposed labeling scheme can improve the decoding performance by up to 0.2 dB with the same run time complexity and model size.

Low-density parity-check(LDPC) code has been selected as the channel coding method by 5G NR because of its excellent error-correcting performance. To further improve the performance of LDPC decoding, this paper proposes a neural normalized min-sum(NNMS) algorithm based on multi-edge-type(MET). Based on the LLR convergence analysis of the protograph matrix of 5G NR, the base matrix is divided into several independent regions. Each part is assigned a unique scaling factor at different iterations. To verify the effectiveness of the proposed algorithm, We use two parity-check matrixes(PCM) derived from different base graphs in simulations. The results show that the proposed algorithm performs at most 0.45dB better than BP, 0.37dB better than NMS, and 0.25dB better than OMS, respectively, when the frame error rate (FER) is at 1$0^{-5}$ level over additive white Gaussian noise (AWGN) channels using BPSK modulation.

Non-Terrestrial Networks (NTNs) are widely recognized as a potential solution to achieve ubiquitous connections of Narrow Bandwidth Internet of Things (NB-IoT). In order to adopt NTNs in NB-IoT, one of the main challenges is the uplink synchronization of Narrowband Physical Random Access procedure which refers to the estimation of time of arrival (ToA) and carrier frequency offset (CFO). Due to the large propagation delay and Doppler shift in NTNs, traditional estimation methods for Terrestrial Networks (TNs) can not be applied in NTNs directly. In this context, we design a two stage ToA and CFO estimation scheme including coarse estimation and fine estimation based on abrupt change point detection (CPD) of phase series with machine learning. Our method achieves high estimation accuracy of ToA and CFO under the low signal-noise ratio (SNR) and large Doppler shift conditions and extends the estimation range without enhancing Random Access preambles.

Integrating reconfigurable intelligent surface (RIS) into multi-access edge computing (MEC) systems to assist computation offloading from mobile devices (MDs) to edge servers has been increasingly considered. However, most resource allocation strategies in the current RIS-assisted MEC systems are based on perfect channel state information (CSI). Due to the passive characteristics of RIS and the large number of reflecting elements in the RIS, it is difficult to obtain the accurate CSI in RIS-assisted wireless systems. In this paper, we aim to minimize the expected energy consumption of MDs in a RIS-assisted multi-user MEC system with imperfect CSI and probabilistic latency constraints. The formulated problem is non-trivial to tackle due to the tightly coupled optimization variables, non-closed-form expression of the objective function, and the probabilistic latency constraints. To deal with the problem, we propose a constrained stochastic successive convex approximation (CSSCA) framework-based algorithm to approximate the problem into a sequence of convex surrogate problems and solve them iteratively. Extensive numerical results validate the superiority of introducing RIS into MEC systems and demonstrate that our proposed algorithm is effective when the CSI is imperfect.

The development of non-orthogonal multiplexing (NOM) techniques such as layered-division-multiplexing (LDM) brings opportunities to the evolution of the fifth generation mobile networks (5G). In order to improve the performance of 5G multicast and beyond, this paper proposes an LDM-based hybrid multicast system to take advantage of both Multicast/Broadcast Single Frequency Network (MBSFN) and Single Cell Point to Multipoint (SC-PTM). Specifically, each cell in the network transmits the upper layer LDM signal in the MBSFN mode, while transmits the lower layer LDM signal in the SC-PTM mode. With the aim of maximizing system throughput, we formulate an optimization problem to allocate the transmit power between these LDM signal layers in each cell. Then, to solve the formulated problem, an algorithm based on the concave-convex procedure is proposed, which guarantees the convergence. Numerical simulation evaluates the performance of the proposed algorithm and LDM-based hybrid multicast, and demonstrates the improvement in system throughput and stream data rate.

The performance of sub-TeraHertz(sub-THz) system is severely degraded by strong oscillator phase noise. High-order constellation is one of the methods in which transmission rates can be increased and non-uniform constellation (NUC) is considered to be an effective way of increasing block error rate (BLER) performance. In this paper, we design a series of constellations for single-carrier and multi-carrier systems under phase noise (PN). In order to improve the BLER performance under PN channel, we formulate an optimization problem to maximize the channel capacity by changing the complex plane coordinates of the constellation points. To calculate the channel capacity, single-carrier PN and multi-carrier PN are modeled separately in this paper. In particular, a series of derivations are carried out for multi-carrier PN. Numerical results demonstrate that NUC can be considered as a commitment technique for channels with PN, and a series of NUCs with different code rates are obtained, with a maximum gain of 4.10 dB in the single-carrier system and 1.65 dB in the multi-carrier system.

The recently proposed orthogonal time-frequency space (OTFS) modulation scheme is able to provide significant performance gain over orthogonal frequency division multiplexing (OFDM) in high Doppler spread scenarios. Qualified channel estimation in delay-Doppler domain is the prerequisite for such good performance, but it requires a large number of guard and pilot symbols, which degrades channel capacity significantly. In this paper, a prior channel statistics based scheme is proposed to maximize the system ergodic capacity by optimizing the channel estimation overhead while ensuring the high-quality performance of the OTFS over delay-Doppler channels. We first investigate the signal-to-interference-plus-noise ratio (SINR) performance of the proposed scheme and derive the closed-form ergodic capacity of the OTFS system on its basis. And then, the capacity maximization problem is studied given the root-mean-square (RMS) delay spread rather than a request for instantaneous perfect channel state information (CSI). In addition, we reveal that the additive white Gaussian noise (AWGN) power has an impact on the solution to the optimization problem, so optimization instances are proposed to treat differently according to SNR levels. Extensive simulations are carried out to evaluate the performance of the proposed overhead reduction scheme. Numerical results demonstrate the superiority of the proposed scheme, and the effect of channel characteristics and system parameters are also revealed.

Vehicle-to-infrastructure (V2I) communication enables vehicles to acquire surrounding traffic information in real time, which significantly improves the driving safety and comfort. The cable-connected roadside unit (c-RSU) with high communication capability and large communication range plays an indispensable role in V2I communication. Meanwhile, unmanned aerial vehicle (UAV) technology has developed rapidly. In terms of its unique mobility and flexibility, the UAV-assisted RSU (u-RSU) can dynamically adjust its position according to the traffic density and emergencies. So the u-RSU can be a reliable supplement to the ground RSU in V2I communication. In this paper, a combined c-RSU and u-RSU deployment strategy is proposed to achieve the maximal effective traffic coverage ratio (ETCR) under a given tough budget bound. To solve this problem, we introduce a two layer improved greedy algorithm (TLIGA). Within TLIGA, the first layer greedy algorithm embedded with the improved Kruskal algorithm is used to deploy c-RSUs and cable, while the second layer improved greedy algorithm is used to determine the optimal number of u-RSUs and their flight strategy. Simulation results show that compared with the existing methods the proposed algorithm TLIGA can significantly increase ETCR.

Recently, many researches based on millimeter wave (mmWave) together with analog beamforming technology have been done to provide higher transmission throughput in vehicle-to-everything (V2X) communication. While hybrid beamforming technology, which can generate multi radio frequency (RF) chains with reduced hardware complexity, continues attracting attention. Hence, this paper focuses on a hybrid-beamforming-based roadside unit (RSU) beam resource scheduling problem in vehicle-to-infrastructure (V2I) communication and intends to improve transmission fairness. Then, this paper proposes an indicator Q to evaluate transmission fairness. Furthermore, a Power-allocated-based Beam resource Scheduling strategy (PBS) and a Time-allocated-based Beam resource Scheduling strategy (TBS) are designed for the multi-RF-chain based V2I communication to enhance transmission fairness. Simulation results prove that, the two proposed beam resource scheduling strategies can effectively improve the transmission fairness in V2I communication.

In this paper, a Contour-based Cluster Identification (CCI) algorithm is proposed to identify the clusters in Concatenated Power Delay Profiles (CPDPs). To predict the characteristics of clusters, we introduce four methods to the three-fold CCI algorithm. Our new methods increase identification accuracy with greatly decreased computational complexity from 2-D to 1-D Hough-transform searching. The accuracy of prediction methods and the efficiency of the CCI algorithm are both confirmed by simulation results.

In 5G era, the base stations are capable of providing multiple services in various scenarios (e.g. vehicle network, Internet of Things), which provides fine opportunity to enhance spectrum efficiency. Base stations can flexibly utilize the idle frequency band for spatiotemporal low-demand services and guarantee services with high priority (e.g. urgent broadcasting), which construct a distributed architecture for spectrum allocation. In this paper, we provide a dynamic spectrum allocation scheme in base station, which can flexibly rearrange spectrum considering service priority, energy consumption and renting cost. We use Lyapunov optimization method to solve the problem. Moreover, we propose online Lyapunov optimization algorithm (OLOA) to figure out the optimal solution of penalty-and-drift function and show mathematical proofs on the performance of the algorithm. The simulation results show that the superiority and stability of our algorithm, which corroborates theoretical analysis.

This work provides the first link level performance evaluation of the Rate-Splitting (RS) based precoding scheme in a downlink multi-user multiple input single output (MU-MISO) system. Contrary to the existing works on the RS precoding that mostly focused on the sum rate or minimum rate maximization, this work bridges the optimization results with the bit error rate (BER) performance, initiating the RS software implementation. We demonstrate that, in an overloaded scenario, the conventional precoding schemes suffer from the BER error floor that corresponds to their rate saturation, which can be overcome by the RS-based strategy that adding the message decodability of certain users with the interference-limited message rate.

Service placement is a significant issue in mobile edge computing (MEC) system. Many works have proposed efficient offline approaches for service placement problems in MEC system. However, because of the randomness and uncertainty of mobile networks, it is impractical for these approaches to be implemented. Facing these uncertainty, we propose an online service placement scheme for MEC system without knowing service demand and network states in advance. In order to maximize the long-term accumulated reward obtained by service placement with limited resource constraint, we analyse this problem by a combinatorial multi-armed bandit (MAB) framework. In addition, because we simultaneously consider the service placement and resource allocation among services, it can be formulated as a multiple choice knapsack problem (MCKP) in each time slot. To solve this long-term reward maximization problem, we first propose a combinatorial upper bound confidence(CUCB)-based online service placement and resource allocation scheme. Then, we analyse the performance of this algorithm theoretically. Finally, simulation results show the efficiency of the algorithm.

Vehicle-to-infrastructure (V2I) communication enables vehicles to acquire surrounding traffic information in real time, which significantly improves the driving safety and comfort. The roadside unit (RSU) which transfers information among vehicles plays an indispensable role in V2I communication. For a long time, researchers have been focusing on choosing proper RSU deployment locations to deploy fixed RSUs. Meanwhile, unmanned aerial vehicle (UAV) technology has developed rapidly. In terms of its unique mobility and flexibility, the UAV-assisted RSU (u-RSU) is able to cover a larger area. If the flight trajectory is designed properly, fewer u-RSUs can solve the RSU deployment problem and ensure the network performance. Therefore, the large-scale u-RSU collaborative trajectories design problem is investigated in this paper. We propose a u-RSU flight trajectory strategy and solve the problem in three steps. An improved greedy algorithm and an ant colony optimization (ACO) algorithm are used in the solution. Under different conditions, the proposed u-RSU flight strategy is analyzed and compared with other deployment strategies. Simulation results show that our strategy always has better network performance and deploys fewer u-RSUs. Thus, the proposed strategy is more economical and effective compared with the traditional deployment strategy.

Mobile-edge computing (MEC) which employs cloud computing platforms at the network edge is an emerging paradigm for 5G networks. Users can be provided with lower latency and energy consumption by offloading computation tasks to the edge cloud. However, it is hard for MEC to guarantee low latency when large amount of users share the limited spectrum resource to offload computation tasks or download computation results because of high transmission delay. Full-duplex (FD) communication that allows simultaneous transmission and reception of signals over the same frequency band, is a promising solution to the shortage of spectrum resource. In this paper, we investigate a novel multi-user FD-MEC system involving both offloading and downloading processes. With the help of the FD capable BS, two half-duplex (HD) users can form a FD pair which can share the same time slots and frequency band for uplink and downlink transmission. To minimize the completion time of all users in the system, we formulate a joint optimization problem of time, power and user pairing scheme, which is a mixed-integer nonlinear programing (MINLP). This problem is further divided into two layers. For the inner layer problem, we obtain the optimal solution by a bisection method. While for the outer layer problem, we use concave-convex procedure (CCCP) to transform it into a tractable form and obtain a stationary point. Finally, numerical results show that with our proposed resource allocation scheme, the overall latency can be significantly reduced by introducing FD to MEC system.

In this paper, a layered-division multiplexing (LDM) based non-orthogonal transmission framework is proposed to enhance the spectral efficiency of Multicast- Broadcast Single Frequency Network (MBSFN). In this framework, different ranges of MBSFN areas are incorporated into a two-layer LDM system, one layer is for small scale local services, the other layer is for large scale global service. To optimize the proposed transmission framework, we design a cooperative beamforming scheme and abstract it as a max-min fair (MMF) problem. We transform the problem into the difference of convex (DC) structure and design a concave-convex procedure (CCCP) based algorithm to find a local optimal of the problem. In addition, performance upper bounds and baselines are formed through semidefinite relaxation (SDR). The results show that the proposed CCCP-based algorithm performs close to upper bounds and better than the SDR-based approach. And this LDM-based non-orthogonal transmission framework also acquires better spectral efficiency than the orthogonal transmission frameworks.

In order to achieve high-quality entertainment services and large-capacity sensor sharing, it is imperative to improve the throughput of V2I communication systems. Millimeter wave communication is a promising technology, which is generally combined with beamforming techniques. When it comes to beam design, we need to focus on the tradeoff between system throughput and alignment overhead. However, conventional optimization schemes are limited by uniform beamwidth design. Considering the characteristics of V2I communication on the highway, this paper proposes a non-uniform beamwidth design idea. Firstly, an average throughput model based on beam training is established. Then, a recursive algorithm is used to achieve non-uniform beamwidth optimization. Finally, the simulation results prove that the non-uniform beamwidth scheme can significantly improve the throughput of the V2I system.

In this paper, A low complexity decoding scheme of raptor-like low-density parity-check code (LDPC) in sufficient SNR scenarios such as in an over-engineered network or core layer of layered-division multiplexing (LDM), is proposed. This scheme is meaningful to emerging power-limited devices, such as IoT and handheld devices. Based on the proof of boundedness and convergence properties of raptor-like LDPC code, the proposed decoding scheme selectively skips some check node operations during iterative decoding. Furthermore, we propose a corresponding algorithm to choose a proper set of skipped check nodes, which ensures an acceptable performance with much lower complexity. Simulation results show that the proposed scheme is good enough to meet the performance requirements, while yielding attractive power benefits over conventional scheme. The decoding complexity can be reduced by up to 80% in the best case while the SNR is sufficient.

Multimedia Broadcast Multicast Service (MBMS) will be expected to be added into 5G system in the coming 5G release of 3GPP. TV White Space (TVWS) are expected to be effectively used in MBMS mode to solve the problem of spectrum shortage. TV White Space (TVWS) can be organized to provide great help by auction to 5G service providers (SPs) in various scenarios, e.g. mobile wireless communication, Internet of Things (IOT) and Vehicular Network. We investigate imperfect information dynamic Stackelberg Game to allocate the idle TVWS spectrum resources using auction scheme, and, accordingly, the service transaction platform is constructed. First, Hidden Markov Model (HMM) is used to predict the service volume required by Service Providers (SPs). Then, the Nash Equilibrium is achieved by Service Providers, who make strategies based on the forecasting results. Next, Broadcast Operator (BO) allocates the idle spectrum by auction. The simulation results show that the predicted prices are close to the actual transaction prices and the profits of broadcasting operators and services providers are increased simultaneously. This provides solution for the unified regulation of TVWS, incenting the usage of TVWS and providing the feasible scheme that broadcasting can provide the services in 5G.

In vehicular network, high reliability and low latency of communication ensure the driving safety. In order to avoid traffic accidents, it is important to warn surrounding vehicles before a potential traffic accident occurs as soon as possible. In the case of short spectrum resource, a reasonable resource allocation for alarm information is critical in V2V (Vehicle to Vehicle) communication. In this paper, a modified scheme is introduced by taking account of vehicle's priority level and spectrum availability to allocate the frequency resources to upcoming cars. The connection among priority level vehicles will be ensured and channel congestion caused by the surrounding vehicles will be drastically reduced while spectrum resources are insufficient. Simulation results show that the combinative use of sharing vehicle level and available spectrum resource improves the efficiency of spectrum resources, shortens the access-time of alarm information on the premise of successful communication, and improves the safety of vehicle driving.

Low-density parity-check (LDPC) codes are playing an important role in modern wireless communication systems such as WiMAX due to their Shannon limit approaching error correction performance. To lower the decoding threshold of LDPC codes, this paper develops a novel multi-layer iterative decoding scheme using deterministic bits for multimedia communication systems. These deterministic bits serve as known information in the LDPC decoding process to reduce the redundancy during data transmission. Unlike the existing work, our proposed scheme addresses the controllable deterministic bits, such as MPEG null packets, rather than the widely investigated protocol headers. Simulation results show that our proposed scheme can achieve considerable gain in WiMAX networks.

In this paper, we propose a modification to Belief Propagation (BP) decoding algorithm for LDPC codes. The modification is to attenuate the check to bit extrinsic logarithm likelihood ratio by a factor α, when sudden sign change happens. This modification can be applied to both the standard BP algorithm and the joint row and column (JRC) BP algorithm. Simulation results show that the BER and WER performance of both traditional BP and JRC BP algorithms is improved by this method. The expense of the proposed modification is a slight increase in the average number of decoding iterations.

This paper investigates the design of LDPC codes over mobile DTV multipath channels. Most of the existing LDPC codes are optimized for additive white Gaussian noise (AWGN) channel, and not feasible to encounter the long burst error occurring in mobile DTV channel. Accordingly, we study the error propagation statistics of decision feedback equalizer (DFE) and formulate it into a gated noise model. To achieve good error correction in burst error channel, we proposed a class of dual-degree IRA codes to balance the metrics of decoding threshold and robustness. Extensive simulation results are presented in this paper to justify the performance of dual-degree IRA codes over gated noise model.