The advance of vehicle-to-everything (V2X) networks has led to many emerging data-intensive applications at the network edge. To meet the soaring data rate requirements of these applications, numerous coding schemes has been developed. However, the high heterogeneity of edge users bring challenges to these methods, including adaptation to performance requirements, coping with unknown or varying channels, as well as inefficient multicasting. In this paper, we address those problems by developing aratelessdeep joint source-channel coding scheme featuring fine-grained control over rate and informativeness at the user. Towards this end, we first design a novel class of variational information bottleneck (VIB) by employing the multinomial-Gaussian (MG) distribution, to achieve rateless transmission over an erasure channel. We derived important results on the statistical properties of this latent distribution to facilitate efficient training of MG-VIB. Then, we apply this framework to multicasting, proposing MG-VIB-M to enhance adaptability and scalability. Simulations show that our proposed method is more flexible regarding rate-relevance tradeoffs, has greater robustness against channel imperfections, and reduces bandwidth requirements for task-oriented multicasting.

Approximate message passing (AMP) iterative detection is recognized as a reliable and practical approach for multiple-input multiple-output (MIMO) systems. However, existing AMP detection algorithms face a critical challenge: high computational complexity due to redundant iterations, making them impractical for the coming 6G networks with increased data throughput demands. This paper addresses this challenge by investigating the mutual information (MI) update flow in AMP iterative MIMO detection and introducing a precise MI computation mechanism based on the error function, referred to as the EFA mechanism. Leveraging the EFA mechanism, we propose a convergence-based termination (CT) scheme to accurately track the convergent iteration number and eliminate redundant iterations in AMP iterative detection. Numerical results demonstrate that the MI flow calculated using the EFA mechanism is consistent with the convergence behavior of AMP iterative MIMO detection across different iterations and signal-to-noise ratios (SNRs). Specifically, the EFA mechanism can precisely identify the convergent iteration number and corresponding SNR. Additionally, the CT scheme achieves up to a 80% reduction in complexity compared to original AMP detection, while maintaining the expected BER performance.

To meet the growing demand for communication capacity and address the scarcity of wireless spectrum resources, we propose a novel orthogonal time frequency space (OTFS) channel estimation scheme enhanced by visual environment semantics. This approach establishes a theoretical foundation for semantic-assisted channel estimation by modeling the potential relationship between the wireless communication channel and its surrounding environment. Leveraging a computer vision-based adaptive environment semantic sensing framework, the system extracts and processes environment features to infer channel characteristics. To tackle the challenge of capturing small-scale fading solely through visual environment semantics, we design two new pilot structures and the corresponding channel estimation methods. These are tailored to maximize the utility of information derived from the environment while minimizing pilot overhead. The simulation results demonstrate that the proposed scheme outperforms the conventional channel estimation method in terms of spectral efficiency and robustness in high-mobility and low-SNR scenarios with fewer pilot symbols.

With the rapid development of the Internet of Things (IoT), the number of connected devices has increased exponentially, bringing significant convenience to various aspects of daily life and business operations. However, communication between IoT devices requires a significant amount of bandwidth, putting a strain on the communication system. To address this challenge, we introduce a classification-oriented semantic communication approach that transmits only essential information. We present a novel end-to-end task-oriented semantic communication model, which efficiently serves the classification task at the receiver. In particular, the proposed model first utilizes a neural network-based semantic encoder to extract classification-related semantic features. A transformer-based semantic decoder is used at the receiver to retrieve semantic features and generate classification results. We further introduce a channel encoder and decoder module to improve the ability of a single model to deal with various channel conditions. Simulation results show that, compared with the traditional method, the proposed scheme achieves higher classification accuracy on the ESC-50 dataset and UrbanSound8K dataset and has better performance for various channel conditions.

Rateless codes, also known as fountain codes, are very suitable for communication in unknown and complex channel environments. However, the overhead and complexity of rateless codes will increase sharply when the receiver’s buffer is limited. In this paper, we first present a transmission process for Luby Transform (LT) code with intermediate feedback. Subsequently, we propose a buffer-limited degree distribution optimization method based on deep reinforcement learning (DRL). The proposed method is applicable both with and without intermediate feedback. Furthermore, we analyze and verify the relationship between buffer capacity and the optimal feedback point under the condition of single intermediate feedback. Simulation results show that under the condition of limited buffer, the proposed method outperforms conventional schemes in terms of intermediate recovery rate, bit error rate and overhead performance.

Digital semantic communication has attracted considerable attention attributed to its potential for integration with modern digital communication systems, which has demonstrated significant performance gains. However, despite its ability to save transmission bandwidth, digital semantic communication can degrade the performance of tasks at the receiver, particularly in low-bitrate scenarios. In this article, we propose a novel low-bitrate digital semantic communication method based on a generative model for speech transmission to achieve high-quality reconstructed speech at low-bitrate transmission. In particular, we first investigate a multiscale semantic codec based on residual vector quantization with a generative adversary network (RVQGAN) model for extracting semantic information and obtaining high speech reconstruction quality while transmitting at a low bitrate. We then, design a channel noise suppression (CNS) module based on U-Net to alleviate the channel effect at low signal-to-noise ratio (SNR) by restoring high-quality semantic features, which is capable of improving the performance of the proposed method under challenging channel conditions. Moreover, a Transformer-based code predictor is utilized to further improve the robustness of the proposed method by accounting for both the channel impact and reconstruction quality. Finally, a three-stage training strategy is also presented in this article to ensure the effective operation of the proposed multiscale semantic codec, CNS module, and code predictor module. Experimental results demonstrate that the proposed method operating at 3 kb/s can save at least 50% of bandwidth while achieving higher speech restoration quality than the baseline method.

When applying integrated sensing and communications (ISAC) in future mobile networks, many sensing tasks have low latency requirements, preferably being implemented at terminals. However, terminals often have limited computing capabilities and energy supply. In this paper, we investigate the effectiveness of leveraging the advanced computing capabilities of mobile edge computing (MEC) servers and the cloud server to address the sensing tasks of ISAC terminals. Specifically, we propose a novel three-tier integrated sensing, communication, and computing (ISCC) framework composed of one cloud server, multiple MEC servers, and multiple terminals, where the terminals can optionally offload sensing data to the MEC server or the cloud server. The offload message is sent via the ISAC waveform, whose echo is used for sensing. We jointly optimize the computation offloading and beamforming strategies to minimize the average execution latency while satisfying sensing requirements. In particular, we propose a low-complexity distributed algorithm to solve the problem. Firstly, we use the alternating direction method of multipliers (ADMM) and derive the closed-form solution for offloading decision variables. Subsequently, we convert the beamforming optimization sub-problem into a weighted minimum mean-square error (WMMSE) problem and propose a fractional programming based algorithm. Numerical results demonstrate that the proposed ISCC framework and distributed algorithm significantly reduce the execution latency and the energy consumption of sensing tasks at a lower computational complexity compared to existing schemes.

Designing short Luby transformation (LT) codes with low overhead and good error performance is crucial and challenging for the deployment of vehicle-to-everything networks, which require high reliability, high spectral efficiency, and low latency. In this paper, we investigate the design of globally optimal transmission strategies that consider interactions between feedback for short LT codes using reinforcement learning (RL), where traditional asymptotic analysis based on random graph theory is known to be inaccurate in this context. First, in order to reduce the decoding overhead of short LT codes, we derive the gradient expression for optimizing the degree distribution of LT codes, and propose a RL-based distribution optimization (RL-DO) algorithm for designing short LT codes. Then, to improve the reliability and overhead of LT codes under limited feedback, we model the feedback optimization problem as a Markov decision process, and propose the RL-based joint feedback and distribution optimization (RL-JFDO) algorithm, which aims to design globally-optimal feedback schemes. Simulations show that our methods have lower decoding overhead, error rate, and decoding complexity compared to existing feedback fountain codes.

Deep learning-based semantic communication is an emerging communication method that achieves cooperative transmission between source and channel. The primary objectives of semantic communication are to enhance the efficiency of information transmission and ensure the accurate restoration of semantic content. Recent studies have shown that semantic communication performs well in enhancing transmission rates, especially in low signal-to-noise ratio environments. However, existing speech semantic communication methods neglect to account for speech perception at the receiver and the complexity of the method, which limits the practical implementation of semantic communication methods. In this paper, we propose a perceptually-motivated, low-complexity speech semantic communication method. Specifically, we employ an end-to-end communication approach to transmit the source speech and obtain the reconstructed speech at the receiver. To ensure the accurate extraction of semantic information, we present a low-complexity fully convolutional semantic encoder, which increases the accuracy of semantic information extraction and improves transmission efficiency. Considering the sensitivity of human perception, a multi-resolution joint loss function has been implemented to enhance the model’s performance and guarantee that the reconstructed speech aligns with the human ear’s auditory perception. Experimental results show that the proposed method performs better on objective and subjective metrics than existing speech transmission methods. Compared with existing neural semantic transmission methods, we improve the transmission efficiency, and the number of symbols needed for transmission is decreased by 60% without compromising the quality of speech. Furthermore, the proposed semantic communication method has a lower complexity and consumes less time to transmit.

Integrated sensing and communications (ISACs) technique is being considered as a promising technique for future networks. Facing the diverse services required for different nodes, in this article, we investigate the ISAC systems with simultaneous public and confidential transmission, where an ISAC base station simultaneously provides integrated public and confidential services for different nodes and performs target tracking using the echo of communication signals. We study the optimization of public, confidential signals, and artificial noise (AN) in both the time-invariant and time-varying channels. Our primary goal is to minimize the differences between the actual and desired beampatterns, while meeting the constraints of the public message rate (PMR) and confidential message secrecy rate (CMSR). For time-invariant channels with typically negligible estimation error of the channel state information (CSI), we aim to synthesize the target beampattern while satisfying the PMR and CMSR constraints. To this end, we first propose a successive convex approximation-based algorithm to jointly design the transmit covariance matrices and the AN covariance matrix; we then propose a low-complexity two-stage algorithm that is more suitable for the practical implementation. The proposed algorithms are further extended to the time-varying channels where the estimated may contain large errors. Simulation results are provided and verify the effectiveness of the proposed algorithms.

Vehicle-to-infrastructure (V2I) communications have been regarded as an emerging application in next-generation wireless networks. However, guaranteeing high-quality wireless communications in high-mobility scenarios remains a major challenge. In this paper, we investigate the deployment of reconfigurable intelligent surface (RIS) for improving the communication performance of V2I systems. In particular, integrated sensing and communication (ISAC) signals are exploited to facilitate sensing-assisted beamforming. Aiming at maximizing the achievable rate, two deep learning-based predictive beamforming mechanisms are proposed. First, a two-stage beamforming design is devised, where the channel state information (CSI) is estimated based on the echo signals and predicted by a dedicated neural network for time-varying channels. Then, the transmit beamforming vector at the base station (BS) and the reflect beamforming matrix at the RIS are jointly optimized. To further reduce the computational complexities, we develop an end-to-end beamforming design by employing the parameter sharing mechanism and weighted loss function. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate that approaches the upper bound exploiting perfect CSI. In particular, the end-to-end design exhibits remarkable robustness against the impact of noise and achieves outstanding sensing-assisted beamforming performance, especially at the low signal-to-noise ratio region.

We investigate the application of reinforcement learning (RL) on online fountain codes, and propose two schemes to reduce the full-recovery overhead with limited feedback. First, we use RL in determining the optimal degree of coded symbols for a given number of feedback, and propose the RL-based degree determination (RL-DD), with the help of theoretical analysis of the relationship between recovery rate and buffer occupancy. Then we propose online fountain codes with no build-up phase using sectioned distribution (OFCNB-SD), where the encoder sends symbols whose degrees are sampled from different sections of an overall distribution, and the decoder is improved to utilize coded symbols that are not immediately decodable. We present theoretical analysis of OFCNB-SD, and introduce RL-based sectioned distribution (RL-SD) scheme where the sectioning of the overall distribution is optimized with RL. Simulation results show that our proposed schemes could achieve lower full-recovery overhead with limited feedback compared to existing schemes.

Recently, online fountain codes attract much attention as the online property is enhanced by introducing feedback. In this paper, we propose two improvements to Heuristic Table Lookup based on Overhead (HTLO), i.e., Integration based Overhead Calculation (IO) and Flexible Selection Strategy (FSS), and introduce our new feedback strategy IO-FSS-HTLO. In the proposed scheme, the degree of coded symbols and corresponding feedback points are selected to achieve lower overhead when the number of feedback transmissions is limited. Results show that IO-FSS-HTLO could achieve better overhead performance under limited feedback scenarios compared to HTLO.

Online fountain codes (OFC) have attracted much attention for their good intermediate performance, which is important for receivers with low-complexity requirement. However, low-complexity receivers generally have limited buffer size to store coded symbols that have not been fully decoded yet, as well as limited power budget for feedback transmissions. In this paper, we propose improved transmission schemes for online fountain codes to reduce the buffer occupancy and feedback transmissions. Firstly, we analyze the relationship between buffer occupancy and overhead as well as the relationship between recovery rate and overhead for online fountain codes. Motivated by the analysis, we propose the weighted online fountain codes (WOFC) which can adapt to various buffer sizes by adjusting the weight to control the probability that a coded symbol can be fully processed immediately, and analyze its performance. Then we further propose weighted online fountain codes with low feedback (WOFC-LF), which utilize the proposed analysis to estimate the recovery rate, and reduce feedback transmissions. Simulation results verify the effectiveness of the analysis for both OFC and WOFC, and demonstrate the superior performance of WOFC-LF with limited buffer size and feedback transmissions.

In this paper, we investigate the network utility maximization problem in a dual-functional radar-communication multi-unmanned aerial vehicle (multi-UAV) network where multiple UAVs serve a group of communication users and cooperatively sense the target simultaneously. To balance the communication and sensing performance, we formulate a joint UAV location, user association, and UAV transmission power control problem to maximize the total network utility under the constraint of localization accuracy. We then propose a computationally practical method to solve this NP-hard problem by decomposing it into three sub-problems, i.e., UAV location optimization, user association and transmission power control. Three mechanisms are then introduced to solve the three sub-problems based on spectral clustering, coalition game, and successive convex approximation, respectively. The spectral clustering result provides an initial solution for user association. Based on the three mechanisms, an overall algorithm is proposed to iteratively solve the whole problem. We demonstrate that the proposed algorithm improves the minimum user data rate significantly, as well as the fairness of the network. Moreover, the proposed algorithm increases the network utility with a lower power consumption and similar localization accuracy, compared to conventional techniques.

In recent years, fog computing has emerged as a new paradigm for the future Internet-of-Things (IoT) applications, but at the same time, ensuing new challenges. The geographically vast-distributed architecture in fog computing renders us almost infinite choices in terms of service orchestration. How to properly arrange the service replicas (or service instances) among the nodes remains a critical problem. To be specific, in this article, we investigate a generalized service replicas placement problem that has the potential to be applied to various industrial scenarios. We formulate the problem into a multiobjective model with two scheduling objectives, involving deployment cost and service latency. For problem solving, we propose an ant colony optimization-based solution, called multireplicas Pareto ant colony optimization (MRPACO). We have conducted extensive experiments on MRPACO. The experimental results show that the solutions obtained by our strategy are qualified in terms of both diversity and accuracy, which are the main evaluation metrics of a multiobjective algorithm.

For the benefit of improved intermediate performance, recently online fountain codes attract much research attention. However, there is a trade-off between the intermediate performance and the full recovery overhead for online fountain codes, which prevents them to be improved simultaneously. We analyze this trade-off, and propose to improve both of these two performance. We first propose a method called Online Fountain Codes without Build-up phase (OFCNB) where the degree-1 coded symbols are transmitted at first and the build-up phase is removed to improve the intermediate performance. Then we analyze the performance of OFCNB theoretically. Motivated by the analysis results, we propose Systematic Online Fountain Codes (SOFC) to further reduce the full recovery overhead. Theoretical analysis shows that SOFC has better intermediate performance, and it also requires lower full recovery overhead when the channel erasure rate is lower than a constant. Simulation results verify the analysis and demonstrate the superior performance of OFCNB and SOFC in comparison to other online fountain codes.

Modulation classification of communication signal is one of the key technologies for realizing non-cooperative communication tasks, multi system communication interconnection and software radio. Therefore, when the decision process cannot wait for more data to increase certainty, how to effectively classify the modulation type in a short time is an unavoidable and challenging topic. In this paper, we make a performance comparison of traditional feature-based neural network and deep neural network (DNN) with complex digital modulation signal datasets. The results indicate that DNN has a stronger ability to extract classification features. Then we demonstrate two novel architectures based on DNN, which disentangle more meaning hidden features from the short-time signal and perform superiorly under limited signal length. Finally, we test the generalization ability of neural network models to signal-to-noise radio (SNR).

The online property of fountain codes enables the encoder to efficiently find the optimal encoding strategy that minimizes the encoding overhead based on the instantaneous decoding state. Therefore, the receiver is able to optimally recover data from losses that differ significantly from the initial expectation. In this paper, we propose a framework to analyze the relationship between overhead and the number of recovered source symbols for online fountain codes based on random graph theory. Motivated by the analysis, we propose improved online fountain codes (IOFCs) by introducing a designated selection of source symbols. Theoretical analysis shows that IOFC has lower overhead compared with the conventional online fountain codes. We verify the proposed analysis via simulation results and demonstrate the tradeoff between full recovery and intermediate performance in comparison to other online fountain codes.