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.

The increasing demand for tasks and dynamically changing loads in the Low Earth Orbit (LEO) satellite networks creates significant challenges in terms of computing and routing. Currently, LEO satellites primarily offload tasks to ground stations or satellites within their line of sight, failing to fully utilize the computational resources of the entire network. In addition, existing routing algorithms fail to consider on-satellite loads and computational capacities, leading to bottlenecks in network routing as some satellites with limited processing capacity become overwhelmed. In this paper, the tasks generated by the source satellite can be offloaded to either satellites or ground stations while routing to the destination satellite. The offloading computation and routing decision problems are investigated to minimize the maximum delay. To solve this challenging problem, we first convert the optimization variables, encompassing both routing and computation offloading, into a form that depends solely on the latter, and model the problem as the Markov Decision Process (MDP). Subsequently, the problem is addressed using an algorithm based on Multi-Agent Proximal Policy Optimization (MAPPO), where multiple agents cooperatively determine routing and offloading computation strategies. Simulation results show that the proposed scheme achieves better delay 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.

With the increasing data traffic in the fifth-generation (5G) communication system, the 5G new radio extended to unlicensed bands (5G NR-U) has become a promising approach to relieve the heavy pressure on the cellular system. To achieve the efficient coexistence with the WiFi system and improve the efficiency of temporal, spectral and spatial resource utilization, we first divide the transmission space into two subspaces by leveraging spatial reuse, where the data transmitted by cellular user equipments (UEs) falls into one subspace and the data transmitted by Internet of Things (IoT) devices coexisting with WiFi users through power control is in the other subspace. Then, the coexistence among cellular UEs, IoT devices, and WiFi users is formulated as an optimization model with the aim of maximizing the cellular system throughput via the joint power and subchannel allocation under the interference constraint. Although the resulting optimization problem is a mixed-integer nonlinear programmming, we decompose it into two subproblems and develop an alternating iterative approach to effectively solve them. Also, the closed-form allocations of the power and subchannels are obtained. Simulation results confirm that the proposed scheme can improve the cellular system performance and guarantee the coexistence in the unlicensed band.

Driven by the soaring traffic demand and the growing diversity of mobile services, wireless networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such large-scale and complicated wireless networks, optimal controlling is reaching unprecedented levels of complexity while its traditional solutions of handcrafted offline algorithms become inefficient due to high complexity, low robustness, and high overhead. Therefore, reinforcement learning (RL), which enables network entities to learn from their actions and consequences in the interactive network environment, attracts significant attention. In this article, we comprehensively review the applications of RL in wireless networks from a layering perspective. First, we present an overview of the principle, fundamentals, and several advanced models of RL. Then, we review the up-to-date applications of RL in various functionality blocks of different network layers, ranging from the low-level physical layer to the high-level application layer. Finally, we outline a broad spectrum of challenges, open issues, and future research directions of RL-empowered wireless networks.

With the rapid growth of mobile networks and smart devices, a large number of people prefer to visiting video services via mobile devices. This generates massive data traffic, and thus increases the load of cellular networks. To deal with it, we need to investigate the usage patterns in the video consumption. In this article, we take a data-driven analysis of mobile video services by classifying them into three major types: traditional video portals, user-generated video services and personalized livestreaming. We collect a large dataset of 25,937,758 logs from 455 thousand users, and we find that 1) for the same kind of video services, users exhibit high loyalty; 2) in consecutive days, the traffic peaks have differences among different service types; 3) more video traffic is generated from personalized livestreaming services, and it keeps increasing after midnight at weekdays in the downtown; 4) traffic consumption of user-generated service exhibits great differences under different functional regions; 5) individual users are prone to click within the same service type but possible different time gaps during the consecutive views. Lastly, we utilize these findings to discuss the potential applications in the improvements of cellular networks and video services.

With the explosive growth of mobile traffic, millimeter wave (mmWave) systems have gained considerable attention from both academia and industry. Although relaying and concurrent transmissions have been utilized to improve the system performance, dynamics due to human mobility are still challenges for mmWave systems. In this paper, we propose a high throughput service scheduling (HTSS) scheme, which exploits multi- hop relay and concurrent transmissions to enhance throughput with the consideration of human mobility. In HTSS, we develop a relay path planning algorithm to establish multi-hop relay paths, and then utilize a global time scheduling algorithm to compute transmission scheme to achieve mobility-aware optimization. Through extensive evaluations under realistic human mobility trajectories, we demonstrate the superior performance of HTSS in terms of the system throughput compared with the state-of-the-art schemes.

Fog computing is a promising solution for new emerging applications requiring intensive computation resources and low latency. Devices at the edge of network can share idle resources and collaboratively accomplish the computing tasks in fog computing. Thus, task publishers have heterogeneous options when offloading computing tasks considering the quality of transmission links, energy consumption and other hardware constraints of fog nodes. To incentivize these devices to participate in computation offloading, effective incentive mechanisms are needed. In this paper, utilizing the framework of contract theory, we formulate the negotiation between task publisher and fog nodes as an optimization problem. The optimal contract is the Nash equilibrium solution achieved by task publisher and fog nodes. Simulation results show that an optimal contract can maximize the utility of task publisher meanwhile guarantee the individual rationality and incentive compatibility of fog nodes. Therefore, edge devices can be incentivized effectively to involve in the computation offloading.

Due to the proliferation of mobile networks and services, accessing online video services via mobile devices becomes increasingly popular, which generates huge data traffic and increases the cellular network load. It is valuable to characterize the usage patterns of different video services for network optimization and user experience enhancement. In this paper, we adopt a data-driven approach to investigate the usage patterns of three newly popular and major kinds of mobile video services: personalized livestreaming, user- generated video service, and traditional video portals. Based on the empirical analysis of a large dataset including 0.45 million users and 25 million logs, we find that 1) users have high loyalty in the same type of video services; 2) difference on the traffic peaks in consecutive days exists among different kinds of services; 3) personalized livestreaming services contributes a larger portion of video traffic, which still increases after midnight at weekday in the downtown. Finally, based on these findings, we discuss their implications and insights for network optimization and video service enhancement.

Device-to-device communications are promising technology to enhance 5G cellular network. However, mobility greatly affects the transmission capacity of proximal devices. In this paper, we investigate the problem of mobility-assisted content transmission and resource allocation by leveraging the contact patterns determined by proximal users. We formulate the problem with the help of statistical properties of contact rate, and utilize convex optimization to solve the problem of content transmission and resource allocation for mobile users. We propose the optimal Resource Allocated Content Transmission (RACT) algorithm based on pseudo-polynomial time algorithm using dynamic programming. Extensive simulations are evaluated under realistic mobility factors, which indicates the efficiency of our proposed RACT algorithm.