Federated learning (FL) and reconfigurable intelligent surfaces (RIS) are pivotal technologies for future Internet of Things (IoT) networks, enhancing user privacy and system efficiency. However, realizing their full potential necessitates a cohesive and synergistic integration, challenging the traditional view of them as disparate components. This paper tackles the complex problem of maximizing energy efficiency (EE)—a critical yet under-explored metric insuch tightly coupled FL-RIS systems. We address this gap by formulating ajoint optimization problem that intrinsically links the FL process with physical layer resource allocation. Our framework maximizes the system’s global EE by concurrently designing the base station’s active beamforming and the RIS’s passive phase shifts,with an FL aggregation mechanism that is explicitly channel-aware and adaptive to the RIS-optimized wireless environment. This co-design ensures RIS actively facilitates FL by establishing robust communication, while FL intelligently leverages these improved channels for efficient and accelerated learning, all under practical FL performance constraints. Simulation results demonstrate that our proposed framework significantly enhances system energy efficiency compared to several benchmark schemes and exhibits robust convergence properties.

This paper investigates the mixed-timescale resource allocation problem in satellite-terrestrial integrated networks (STIN). Moreover, the multi-access edge computing (MEC) technology and millimeter wave (mmWave) with rich spectrum resource are merged into the STIN to improve the network performance. A network utility maximization problem characterized by the achievable rate and backhaul reduction is formulated under the constraints of the maximum caching capacity, transmission power of mmWave small-cell base stations (SBSs) and quality of service (QoS) for Internet of Things (IoT) devices, where the caching placement, power allocation and user-SBS association are jointly optimized. In order to tackle this mixed-integer nonlinear programming (MINLP) problem, we decompose the original problem into the long-term caching placement subproblem, and short-term power allocation and user-SBS association subproblems. Then, a multi-agent deep reinforcement learning (MADRL)-based independent proximal policy optimization (IPPO) algorithm is proposed to solve the short-term user-SBS association subproblem. Meanwhile, the linear programming (LP) is used to solve the long-term caching placement subproblem. Furthermore, we derive the closed-form solution of the short-term power allocation subproblem through the Karush-Kuhn-Tucker (KKT) conditions. Simulation results are carried out to validate the effectiveness and scalability of the proposed joint approach.

Semantic communication (SemCom) has recently emerged as a promising paradigm for enhancing the efficiency and intelligence of wireless networks. Nevertheless, device het erogeneity, resource constraints, and the vulnerability of deep neural networks in open environments pose significant challenges to its practical deployment. In this paper, we propose a task oriented and lightweight SemCom system with secure aggregation for ensuring efficient and privacy-preserving interactions in distributed networks. First, we design a multi-task SemCom framework that unifies semantic feature extraction from sample based datasets. To accommodate resource-constrained devices, we further introduce a feature distillation mechanism that derives lightweight local models without sacrificing inference accuracy. To preserve the privacy of local datasets while leveraging the generalization capability of distributed devices, we develop a secure model aggregation algorithm based on multiparty homomorphic encryption. Simulation results and comparative experiments validate the effectiveness of our system, which fully utilizes the knowledge embedded in existing high-performance models. Our results demonstrate that the proposed local semantic models outperform the baseline models under limited datasets and reduced parameters. We also analyze the trade-off between computational complexity and security in the proposed aggregation scheme, highlighting its applicability to distributed SemCom scenarios.

Semantic communications are expected to improve the transmission efficiency in Internet of Things (IoT) networks. However, the distributed nature of networks and heterogeneity of devices challenge the secure utilization of semantic communication systems. In this paper, we develop a distributed semantic communication system that achieves the security and efficiency during update and usage phases. A blockchain-based trust scheme for update is designed to continuously train and synchronize the system in dynamic IoT environments. To improve the updating efficiency, we propose a flexible semantic coding method base on compressive semantic knowledge bases. It greatly reduces the amount of data shared among devices for system update, and realizes the flexible adjustment of the size of knowledge bases and the number of transmitted signal symbols in model training and inference stages. In the usage phase, a signature mechanism for lossy semantics is introduced to guarantee the integrity and authenticity of the transmitted semantics in lossy semantic communications. We further design a noise-aware differential privacy mechanism, which introduces optimized noise based on the different channel information available to heterogeneous devices. Experiments on transmission tasks show that the proposed system defends against cross-phase attacks of compromising semantics integrity and reduces the data to be shared in the update phase by about 36% to 90%, and in the usage phase by 60% compared with related works.

To address the challenges of Internet of Things (IoT) device diversity and media service heterogeneity in human and machine-type communications, a predominant approach in sixth-generation (6G) networks and beyond is to serve diversified IoT devices by differentiated services. In this paper, a satellite-terrestrial IoT framework with multi-access edge computing (MEC) is investigated for two types of heterogeneous services, data-intensive and computation-intensive service. In our proposed framework, MEC and millimeter wave (mmWave) communication are jointly considered to optimize data- and computation-intensive services, guaranteeing the rate, delay and energy requirements of diversified IoT devices. From the viewpoint of heterogeneous services, we formulate a joint resource allocation problem, in which quality of experience (QoE) of diversified IoT devices are recognized as system utility. Specifically, service offloading, power allocation and computation resource allocation are jointly considered. Since the optimized problem is nonconvex, necessary problem reformulations are conducted to transfer the original problem to convex problems. Furthermore, an alternating iterative method based on deep reinforcement learning (DRL) and CVX technique is adopted to obtain the sub-optimal solution with low computation complexity. Finally, extensive simulations are conducted with different system parameter configurations to verify the effectiveness of our proposed scheme.

In this paper, we propose a semantic communication framework facilitated by multi-access edge computing (MEC)assisted satellite-air-ground integrated networks (SAGIN), which comprises of LEO satellites, unmanned aerial vehicles (UAVs), and macro-cell base stations (MBSs). Considering the limited wireless resources and diversified quality of service (QoS) requirements of semantic tasks, an optimization problem with the goal of minimizing system cost in terms of the task latency and energy consumption is formulated. In order to address the mixed-integer nonlinear programming (MINLP) problem, we propose an alternating optimization algorithm that tackles UAV deployment sub-problem with the successive convex approximation (SCA) method, task offloading, semantic compression, power allocation and computation resource allocation optimization with deep reinforcement learning (DRL)-based multi-agent proximal policy optimization (MAPPO) method. Simulation results demonstrate that our proposed algorithm outperformes other reinforcement learning algorithms, i.e., about 5.12%, 23.72% and 35.64% over PPO, DDPG, and A2C, respectively.

With the blossom of Internet of Things (IoT) services and applications, the big data volumes raised by the large number of IoT devices have posed great burden on the traditional terrestrial networks. Considering the advantages of multi-access edge computing (MEC) and reconfigurable intelligent surface (RIS), this paper investigates the MEC and RIS-assisted airground IoT networks, where the joint resource allocation problem is formulated to minimize the system energy consumption. To handle the proposed nonconvex optimization problem, we decompose it into three subproblems, i.e., the coded caching placement problem, the phase shift problem and the joint multi-user association and power allocation problem. Then, we propose a deep reinforcement learning (DRL)-based Proximal Policy Optimization (PPO) algorithm to solve the joint multiuser association and power allocation problem. Moreover, the CVX technique and exhaustive search method are respectively adopted to solve the coded caching placement problem and the phase shift problem. Simulation results demonstrate that our proposed algorithm outperforms the benchmark schemes.

With the rapid proliferation of intelligent internet of things (IoT) terminals, conventional terrestrial networks are increasingly strained by limited bandwidth, high latency, and constrained computation capabilities. Air-Ground integrated networks (AGIN) offer a promising solution through flexible deployment, including enhanced coverage and low latency. To enable intelligent services, we propose an air-ground multi-access edge computing (MEC) network for semantic communication. Within this network, users transmit compressed semantic task data to unmanned aerial vehicles (UAVs) and terrestrial small-cell base stations (SBSs) using a probabilistic semantic compression (PSC) technique. A joint resource optimization problem is developed to determine semantic compression, task allocation, computation resource allocation, power control, and user association, aiming to minimize the system cost. The optimization problem is then formulated as a Markov decision process (MDP) and solved by a proximal policy optimization (PPO) algorithm based on deep reinforcement learning (DRL). Simulation results show that the proposed method significantly outperforms three other DRL baselines, achieving a reduction up to 74.12% in overall latency-energy cost under diverse system configurations.

This study focuses on the latest research advancements in the field of semantic communication. Traditional communication systems prioritize the transmission of raw data, whilst semantic communication emphasizes conveying the meaning represented by the data. However, the extracted semantic information is often ambiguous and subject to subjective evaluation. To address this problem, this study proposes a model that combines a convolutional neural network (CNN) with a Transformer, called DeepSC‐CT. The model utilizes a CNN to extract semantic information from the data, followed by a Transformer model to capture spatial relationships and contextual information within the semantic content. We utilize federated learning to train the model and propose an adaptive aggregation algorithm to accelerate the convergence process. Moreover, we expand the single‐modality semantic communication model to encompass multiple modalities, such as texts, audio, and images. Furthermore, this study introduces a learnable position‐encoding method for the Transformer. The experimental results and visual effects of audio and image restoration demonstrate that the proposed method exhibits impressive performance and that the proposed model shows robust data restoration capabilities under various signal‐to‐noise ratio conditions.

Although multi-beam multiplexing can be implemented merely by phase shifters with hybrid beamforming configured by the sub-connected subarray architecture since all the antennas share the same magnitude, they cannot be set to a predetermined value. To tackle this issue, a non-convex constraint to enforce the magnitudes to a fixed value is first introduced in this design and then an iterative method is employed to relax it into a convex one. In doing so, the weighting magnitudes of all antennas can be preset in advance according to given requirements and a more flexible solution with phase-only excitation is obtained for multi-beam multiplexing. Numerical results are presented to verify the effectiveness of the proposed approaches.

To protect user privacy and improve the transmitting environment of wireless communication, federated learning (FL) and reconfigurable intelligent surface (RIS) are proposed as promising technologies for future communication. Meanwhile, studies have proved that the combination of FL and RIS guarantees better performance for system models. However, the combined model still has problems such as high communication overhead and slow convergence speed. Therefore, in this paper, we proposed a channel quality based device selection and weighted averaging algorithm in a RIS-assisted federated learning model. Simulation results proved that the proposed algorithm outperforms the classic federated averaging (FedAvg) algorithm in convergence speed, test accuracy, and training loss.

With the continuous growth in demand for intelligent services, future 6G networks need to support higher communication efficiency and efficient intelligent connections. Semantic communication technology integrates the meaning of information into data processing and transmission, making it a potential paradigm for 6G. Considering that current research on semantic communication systems mainly focuses on the extraction and encoding of semantic features, with less attention to the impact of channel coding during the communication transmission process on system performance. Therefore, based on the CNN-Transformer (CT) semantic feature extraction and encoding scheme, this paper introduces a polar encoder, designing the end-to-end semantic CT-Polar communication system model frame-work. Through simulation verification, the CT-Polar designed in this paper demonstrated excellent performance in signal recovery on different datasets.

Millimeter‐wave (mmWave) and massive multiple‐input multiple‐output (MIMO) technologies are critical in current and future communication research. They play an essential role in meeting the demands for high‐capacity, high‐speed, and low‐latency communication brought about by technological advancements. However, existing mmWave channel estimation schemes rely on idealized common sparse channel support assumptions, and their performance significantly degrades when encountering beam squint scenarios. To address this issue, this paper introduces a dynamic support detection window (DSDW) algorithm. This algorithm dynamically adjusts the position and size of the window based on the received signal strength, thereby better capturing signal strength variations and obtaining a more complete set of signal supports. The DSDW algorithm can better capture and utilize the sparsity of the channel, improving the efficiency and accuracy of the channel state information acquisition. By combining the beam‐split pattern (BSP) algorithm with the DSDW algorithm, this paper designs an effective method to address the inherent beam‐spreading problem in mmWave scenarios. Simulation results are proposed to demonstrate the effectiveness of the BSP‐DSDW algorithm.

In order to support colourful multimedia services with strict quality-of-service (QoS) requirements of user equipments (UEs), the space-air-ground integrated networks (SAGIN) can be taken as a promising approach to enhance network capacity. Among them, millimeter wave (mmWave) and edge caching promise to significantly improve the SAGIN performance due to the advantage in rich bandwidth resource and low latency, respectively. In this paper, we investigate the joint caching and resource allocation for multimedia services in SAGIN, where multimedia content requests can be simultaneously served by multiple access points (APs). Considering the delay-constraint of multimedia services, we then formulate a mixed-integer non-linear programming (MINLP) problem aiming at minimizing the service delay, which involves jointly optimizing coded caching (CC), power allocation (PA) and UEs-to-APs association (UA). We propose to find the optimal solution by employing an alternating iteration optimization framework. The optimal CC and PA problems are firstly addressed by utilizing convex optimization technology. Then, two many-to-many swap matching algorithms are developed to slove the UA subproblem effectively. Numerical results demonstrate that our proposed algorithms can substantially reduce the service delay over other benchmarks.

In this paper, we propose a novel vehicle positioning method for commodity multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) WIFI systems in indoor parking lots. To address the limitation of the small number of WIFI antennas, the proposed method first utilizes signal model rearrangement techniques, in which the abundant carrier frequency resources of WIFI are expanded into space resources. Then a rearranged off-grid sparse Bayesian learning (ROG-SBL) algorithm is developed for parameters estimation to achieve vehicle positioning. Specifically, by resorting to the Bayesian inference and Newton method, the position-related parameters are estimated iteratively by fitting the channel state information (CSI) measurement model, and thus the vehicle positioning is realized according to the geometric relationship. Moreover, we derive the Cramér-Rao bound (CRB) as a performance reference for the proposed algorithm. Compared with the existing algorithms, the proposed one improves the positioning performance of the vehicle with fewer carrier numbers and has more stable performance. Simulation results show that the performance curves of the proposed algorithm for parameters estimation are close to the corresponding CRBs, and the proposed algorithm can cope with more challenging cases when the line-of-sight (LOS) path does not exist.

Abstract With the intensive deployment of mobile networks and the vigorous development of new multimedia services, video has gradually become the mainstream of cultural consumption. The contradiction between the proliferation of video data services and the scarcity of spectrum resources has brought great challenges to the current network resource allocation. Non‐orthogonal multiple access (NOMA) can be used to solve this problem by signal superposition and spectrum multiplexing to improve system access capability. As a new type of joint optimization design of transmitter and receiver side, PDMA has high research value. In this paper, a framework of PDMA video transmission system based on H.264 video compression coding (HVC‐PDMA) is proposed. Poly complementary sequence (PCS) spread spectrum coding is performed on the transmission codebook in order to improve the transmission accuracy. Meanwhile, a low complexity serial sphere compensated Max‐log MPA (SSCM‐MPA) algorithm is proposed to reduce the complexity of the multi‐user detection algorithm. Simulation results show that the PCS spread spectrum can improve system throughput and peak signal‐to‐noise ratio (PSNR) while reducing bit error rate (BER). SSCM‐MPA algorithm can greatly reduce the complexity and improve the transmission efficiency.

In this paper we propose a novel connection admission control scheme named Rate Transition Area assisted Guaranteed Handover Scheme (GHS-RTA), which utilizes the geographical information, rate transition areas and overlap areas to intelligently decide when to block a new call. This scheme helps avoid possible inter-cell and intra-cell handover failures for HAP communications systems with adaptive modulation and coding in the physical layer. Simulation results show that the GHS-RTA can improve the average new call blocking probability greatly (by a minimum of 21.5% for the system model with the parameter values chosen) while maintaining zero inter-cell and intra-cell handover call dropping probabilities compared with Extended Time-based Channel Reservation Algorithm, and that the larger the rate transition area and the overlap area, the better the average new call blocking performance.