This paper studies the problem of jamming decision-making for dynamic multiple communication links in wireless communication networks (WCNs). We propose a novel jamming channel allocation and power decision-making (JCAPD) approach based on multi-agent deep reinforcement learning (MADRL). In high-dynamic and multi-target aviation communication environments, the rapid changes in channels make it difficult for sensors to accurately capture instantaneous channel state information. This poses a challenge to make centralized jamming decisions with single-agent deep reinforcement learning (DRL) approaches. In response, we design a distributed multi-agent decision architecture (DMADA). We formulate multi-jammer resource allocation as a multi-agent Markov decision process (MDP) and propose a fingerprint-based double deep Q-Network (FBDDQN) algorithm for solving it. Each jammer functions as an agent that interacts with the environment in this framework. Through the design of a reasonable reward and training mechanism, our approach enables jammers to achieve distributed cooperation, significantly improving the jamming success rate while considering jamming power cost, and reducing the transmission rate of links. Our experimental results show the FBDDQN algorithm is superior to the baseline methods.

Quantum computing is a promising technology that has the potential to revolutionize many areas of science and technology, including communication. In this review, we discuss the current state of quantum computing in communication and its potential applications in various areas such as network optimization, signal processing, and machine learning for communication. First, the basic principle of quantum computing, quantum physics systems, and quantum algorithms are analyzed. Then, based on the classification of quantum algorithms, several important basic quantum algorithms, quantum optimization algorithms, and quantum machine learning algorithms are discussed in detail. Finally, the basic ideas and feasibility of introducing quantum algorithms into communications are emphatically analyzed, which provides a reference to address computational bottlenecks in communication networks.

This paper proposes a novel blended hyper-cellular architecture for low-altitude aerial intelligent networks (LAINs) to provide agile coverage tailored to active air routes and takeoff/landing spots. Traditional cellular networks struggle to meet the dynamic demands of low-altitude UAV communications due to their rigid structures. The hyper-cellular network (HCN) architecture separates control and traffic coverage, enabling flexible and energy-efficient operations. The key components include control base stations (CBSs) for wide-area signaling coverage and traffic base stations (TBSs) that can be dynamically activated based on traffic demands. The proposed solution also integrates space information networks (SINs) to enhance the coverage efficiency. Key technologies such as all-G CBS using RISC-V architecture, AI-powered radio maps for low-altitude environments, and agile TBS coverage adaptation are introduced with some preliminary studies. These designs aim to address challenges like mobility management, interference coordination, and the need for real-time spectrum sharing in blended satellite-terrestrial networks. The proposed solution offers a scalable and agile framework to support the rapidly growing demand for reliable, low-latency, and high-capacity UAV communications in urban environments.

Network architectures assisted by Generative Artificial Intelligence (GAI) are envisioned as foundational elements of sixth-generation (6G) communication system. To deliver ubiquitous intelligent services and meet diverse service requirements, 6G network architecture should offer personalized services to various mobile devices. Federated learning (FL) with personalized local training, as a privacy-preserving machine learning (ML) approach, can be applied to address these challenges. In this paper, we propose a meta-learning-based personalized FL (PFL) method that improves both communication and computation efficiency by utilizing over-the-air computations. Its "pretraining-and-fine-tuning" principle makes it particularly suitable for enabling edge nodes to access personalized GAI services while preserving local privacy. Experiment results demonstrate the outperformance and efficacy of the proposed algorithm, and notably indicate enhanced communication efficiency without compromising accuracy.

In recent years, deep learning-based semantic communications have shown great potential to enhance the performance of communication systems. This has led to the belief that semantic communications represent a breakthrough beyond the Shannon paradigm and will play an essential role in future communications. To narrow the gap between current research and future vision, after an overview of semantic communications, this article presents and discusses ten fundamental and critical challenges in today's semantic communication field. These challenges are divided into theory foundation, system design, and practical implementation. Challenges related to the theory foundation including semantic capacity, entropy, and rate-distortion are discussed first. Then, the system design challenges encompassing architecture, knowledge base, joint semantic-channel coding, tailored transmission scheme, and impairment are posed. The last two challenges associated with the practical implementation lie in cross-layer optimization for networks and standardization. For each challenge, efforts to date and thoughtful insights are provided.

In this paper, we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface (ARIS)-assisted multi-antenna jamming (MAJ) scheme denoted by ARIS-MAJ to interfere with the illegal signal transmission. In order to strike a balance between the jamming performance and the energy consumption, we consider a so-called jamming energy efficiency (JEE) which is defined as the ratio of achievable rate reduced by the jamming system to the corresponding power consumption. We formulate an optimization problem to maximize the JEE for the proposed ARIS-MAJ scheme by jointly optimizing the jammer's beamforming vector and ARIS's reflecting coefficients under the constraint that the jamming power received at the illegal user is lower than the illegal user's detection threshold. To address the non-convex optimization problem, we propose the Dinkelbach-based alternating optimization (AO) algorithm by applying the semidefinite relaxation (SDR) algorithm with Gaussian randomization method. Numerical results validate that the proposed ARIS-MAJ scheme outperforms the passive reconfigurable intelligent surface (PRIS)-assisted multi-antenna jamming (PRIS-MAJ) scheme and the conventional multi-antenna jamming scheme without RIS (NRIS-MAJ) in terms of the JEE.

Low earth orbit (LEO) satellites with wide coverage can carry the mobile edge computing (MEC) servers with powerful computing capabilities to form the LEO satellite edge computing system, providing computing services for the global ground users. In this paper, the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program (MINLP) problem. This paper proposes a computation offloading algorithm based on deep deterministic policy gradient (DDPG) to obtain the user offloading decisions and user uplink transmission power. This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme. In addition, the expression of suboptimal user local CPU cycles is derived by relaxation method. Simulation results show that the proposed algorithm can achieve excellent convergence effect, and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.

Protocol Reverse Engineering (PRE) is of great practical importance in Internet security-related fields such as intrusion detection, vulnerability mining, and protocol fuzzing. For unknown binary protocols having fixed-length fields, and the accurate identification of field boundaries has a great impact on the subsequent analysis and final performance. Hence, this paper proposes a new protocol segmentation method based on Information-theoretic statistical analysis for binary protocols by formulating the field segmentation of unsupervised binary protocols as a probabilistic inference problem and modeling its uncertainty. Specifically, we design four related constructions between entropy changes and protocol field segmentation, introduce random variables, and construct joint probability distributions with traffic sample observations. Probabilistic inference is then performed to identify the possible protocol segmentation points. Extensive trials on nine common public and industrial control protocols show that the proposed method yields higher-quality protocol segmentation results.

This paper presents an algorithm named the dependency-aware offloading framework (DeAOff), which is designed to optimize the deployment of Gen-AI decoder models in mobile edge computing (MEC) environments. These models, such as decoders, pose significant challenges due to their inter-layer dependencies and high computational demands, especially under edge resource constraints. To address these challenges, we propose a two-phase optimization algorithm that first handles dependency-aware task allocation and subsequently optimizes energy consumption. By modeling the inference process using directed acyclic graphs (DAGs) and applying constraint relaxation techniques, our approach effectively reduces execution latency and energy usage. Experimental results demonstrate that our method achieves a reduction of up to 20% in task completion time and approximately 30% savings in energy consumption compared to traditional methods. These outcomes underscore our solution's robustness in managing complex sequential dependencies and dynamic MEC conditions, enhancing quality of service. Thus, our work presents a practical and efficient resource optimization strategy for deploying models in resource-constrained MEC scenarios.

This paper investigates the uplink spectral efficiency of distributed cell-free (CF) massive multiple-input multiple-output (mMIMO) networks with correlated Rayleigh fading channels based on three different channel estimation schemes. Specifically, each access point (AP) first uses embedded pilots to estimate the channels of all users based on minimum mean-squared error (MMSE) estimation. Given the high computational cost of MMSE estimation, the low-complexity element-wise MMSE (EW-MMSE) channel estimator and the least-squares (LS) channel estimator without prior statistical information are also analyzed. To reduce non-coherent and coherent interference during uplink payload data transmission, simple centralized decoding (SCD) and large-scale fading decoding (LSFD) are examined. Then, the closed-form expressions for uplink spectral efficiency (SE) using MMSE, EW-MMSE, and LS estimators are developed for maximum ratio (MR) combining under LSFD, where each AP may have any number of antennas. The sum SE maximization problem with uplink power control is formulated. Since the maximization problem is non-convex and challenging, a block coordinate descent approach based on the weighted MMSE method is used to get the optimal local solution. Numerical studies demonstrate that LSFD and efficient uplink power control can considerably increase SE in distributed CF mMIMO networks.

Abstract: Sparse code multiple access (SCMA) is a non-orthogonal multiple access (NOMA) scheme based on joint modulation and spread spectrum coding. It is ideal for future communication networks with a massive number of nodes due to its ability to handle user overload. Introducing SCMA into visible light communication (VLC) systems can improve the data transmission capability of the system. However, designing a suitable codebook becomes a challenging problem when addressing the demands of massive connectivity scenarios. Therefore, this paper proposes a low-complexity design method for high-overload codebooks based on the minimum bit error rate (BER) criterion. Firstly, this paper constructs a new codebook with parameters based on the symmetric mother codebook structure by allocating the codeword power so that the power of each user codebook is unbalanced; then, the BER performance in the visible light communication system is optimized to obtain specific parameters; finally, the successive interference cancellation (SIC) detection algorithm is used at the receiver side. Simulation results show that the method proposed in this paper can converge quickly by utilizing a relatively small number of detection iterations. This can simultaneously reduce the complexity of design and detection, outperforming existing design methods for massive SCMA codebooks.% so as to reduce the out-of-band (OOB) radiation as much as possible. Parameters of the proposed scheme are solved under joint con-straints of constant power and unity cumulative distribution. A new receiving method is also proposed to improve the bit error rate (BER) performance of OFDM systems. Simulation results indicate the proposed scheme can achieve better OOB radiation and BER performance at same PAPR levels, compared with existing similar companding algorithms.

Multimedia semantic communication has been receiving increasing attention due to its significant enhancement of communication efficiency. Semantic coding, which is oriented towards extracting and encoding the key semantics of video for transmission, is a key aspect in the framework of multimedia semantic communication. In this paper, we propose a facial video semantic coding method with low bitrate based on the temporal continuity of video semantics. At the sender's end, we selectively transmit facial keypoints and deformation information, allocating distinct bitrates to different keypoints across frames. Compressive techniques involving sampling and quantization are employed to reduce the bitrate while retaining facial key semantic information. At the receiver's end, a GAN-based generative network is utilized for reconstruction, effectively mitigating block artifacts and buffering problems present in traditional codec algorithms under low bitrates. The performance of the proposed approach is validated on multiple datasets, such as VoxCeleb and TalkingHead-1kH, employing metrics such as LPIPS, DISTS, and AKD for assessment. Experimental results demonstrate significant advantages over traditional codec methods, achieving up to approximately 10-fold bitrate reduction in prolonged, stable head pose scenarios across diverse conversational video settings.

Memory-unsafe programming languages, such as C/C++, are often used to develop system programs, rendering the programs susceptible to a variety of memory corruption attacks. Among these threats, just-in-time return-oriented programming (JIT-ROP) stands out as an advanced method for conducting code-reuse attacks, effectively circumventing code randomization safeguards. JIT-ROP leverages memory disclosure vulnerabilities to obtain reusable code fragments dynamically and assemble malicious payloads dynamically. In response to JIT-ROP attacks, several re-randomization implementations have been developed to prevent the use of disclosed code. However, existing re-randomization methods require recurrent re-randomization during program runtime according to fixed time windows or specific events such as system calls, incurring significant runtime overhead.
In this paper, we present the design and implementation of \mytool, an efficient re-randomization approach on the AArch64 platform. Unlike previous methods that necessitate frequent runtime re-randomization or reply on unreliable triggering conditions, this approach triggers the re-randomization process by detecting the code page harvest operation, which is a fundamental operation of the JIT-ROP attacks, making our method more efficient and reliable than previous approaches. We evaluate \mytool\ on benchmarks and real-world applications. The evaluation results show that our approach can effectively protect programs from JIT-ROP attacks while introducing marginal runtime overhead.

The low Earth orbit (LEO) satellite networks have outstanding advantages such as wide coverage area and not being limited by geographic environment, which can provide a broader range of communication services and has become an essential supplement to the terrestrial network. However, the dynamic changes and uneven distribution of satellite network traffic inevitably bring challenges to multipath routing. Even worse, the harsh space environment often leads to incomplete collection of network state data for routing decision-making, which further complicates this challenge. To address this problem, this paper proposes a state-incomplete intelligent dynamic multipath routing algorithm (SIDMRA) to maximize network efficiency even with incomplete state data as input. Specifically, we model the multipath routing problem as a markov decision process (MDP) and then combine the deep deterministic policy gradient (DDPG) and the $K$ shortest paths (KSP) algorithm to solve the optimal multipath routing policy. We use the temporal correlation of the satellite network state to fit the incomplete state data and then use the message passing neuron network (MPNN) for data enhancement. Simulation results show that the proposed algorithm outperforms baseline algorithms regarding average end-to-end delay and packet loss rate and performs stably under certain missing rates of state data.

The large-scale development of the low-altitude economy imposes increasingly stringent requirements on the supporting information infrastructure, necessitating the establishment of a low-altitude intelligent network (LAIN) with wide-area communication, high-precision navigation, and efficient supervision capabilities. Benefiting from its broad coverage, high reliability, and large bandwidth, the 5G cellular network serves as a critical foundation for LAIN construction. However, conventional cellular networks are primarily designed for two-dimensional terrestrial scenarios, and thus face significant limitations in coverage and interference resistance within complex three-dimensional low-altitude environments. To address the unique demands of LAIN applications, key challenges must be tackled, including achieving seamless three-dimensional coverage, mitigating interference in multi-dimensional network deployments, and ensuring stringent requirements for service quality and security supervision. This paper proposes an integrated LAIN architecture characterized by the convergence of communication, navigation, sensing, and management, enhanced with artificial intelligence and security mechanisms to improve overall system intelligence and resilience. Furthermore, this paper conducts an in-depth analysis of the critical challenges in LAIN deployment, explores enabling technologies to address these issues, and offers insights into the future development direction of low-altitude intelligent networks.

The advent of the internet-of-everything era has led to the increased use of mobile edge computing. The rise of artificial intelligence has provided many possibilities for the low-latency task-offloading demands of users, but existing technologies rigidly assume that there is only one task to be offloaded in each time slot at the terminal. In practical scenarios, there are often numerous computing tasks to be executed at the terminal, leading to a cumulative delay for subsequent task offloading. Therefore, the efficient processing of multiple computing tasks on the terminal has become highly challenging. To address the low-latency offloading requirements for multiple computational tasks on terminal devices, we propose a terminal multitask parallel offloading algorithm based on deep reinforcement learning. Specifically, we first establish a mobile edge computing system model consisting of a single edge server and multiple terminal users. We then model the task offloading decision problem as a Markov decision process, and solve this problem using the Dueling Deep-Q Network algorithm to obtain the optimal offloading strategy. Experimental results demonstrate that, under the same constraints, our proposed algorithm reduces the average system latency.

The Internet of Unmanned Aerial Vehicles (I-UAVs) is expected to execute latency-sensitive tasks, but limited by co-channel interference and malicious jamming. In the face of unknown prior environmental knowledge, defending against jamming and interference through spectrum allocation becomes challenging, especially when each UAV pair makes decisions independently. In this paper, we propose a cooperative multi-agent reinforcement learning (MARL)-based anti-jamming framework for I-UAVs, enabling UAV pairs to learn their own policies cooperatively. Specifically, we first model the problem as a model-free multi-agent Markov decision process (MAMDP) to maximize the long-term expected system throughput. Then, for improving the exploration of the optimal policy, we resort to optimizing a MARL objective function with a mutual-information (MI) regularizer between states and actions, which can dynamically assign the probability for actions frequently used by the optimal policy. Next, through sharing their current channel selections and local learning experience (their soft Q-values), the UAV pairs can learn their own policies cooperatively relying on only preceding observed information and predicting others' actions. Our simulation results show that for both sweep jamming and Markov jamming patterns, the proposed scheme outperforms the benchmarkers in terms of throughput, convergence and stability for different numbers of jammers, channels and UAV pairs.

Recently, a new worldwide race has emerged to achieve a breakthrough in designing and deploying massive ultra-dense low-Earth orbit (LEO) satellite constellation (SatCon) networks with the vision of providing everywhere Internet coverage from space. Several players have started the deployment phase with different scales. However, the implementation is in its infancy, and many investigations are needed. This work provides an overview of the state-of-the-art architectures, orbital patterns, top players, and potential applications of SatCon networks. Moreover, we discuss new open research directions and challenges for improving network performance. Finally, a case study highlights the benefits of integrating SatCon network and non-orthogonal multiple access (NOMA) technologies for improving the achievable capacity of satellite end-users.

Federated semi-supervised learning (FSSL) faces two major challenges: the scarcity of labeled data across clients and the non-independent and identically distributed (Non-IID) nature of data among clients. To address these issues, we propose diffusion model-based data synthesis aided FSSL (DDSA-FSSL), a novel approach that leverages diffusion model (DM) to generate synthetic data, thereby bridging the gap between heterogeneous local data distributions and the global data distribution. In the proposed DDSA-FSSL, each client addresses the scarcity of labeled data by utilizing 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. As a result, the disparity between local and global distributions is reduced and clients can create enriched synthetic datasets that better align with the global data distribution. Extensive experiments on various datasets and Non-IID scenarios demonstrate the effectiveness of DDSA-FSSL, achieving significant performance improvements, such as increasing accuracy from 38.46% to 52.14% on CIFAR-10 datasets with 10% labeled data.

The development of communication networks is currently undergoing a period of transformation. This paper illustrates this transformation from the growth rate of communication users, network bandwidth, and service revenue. We also analyze the shift in the focus of network technology development from aspects such as information sources, mobile terminals, wireless channels, core networks, edge clouds, data perception, and artificial intelligence. Finally, we briefly outline the new paradigm for network research and development (R&D) in the intelligent era.

In this paper, we present a novel particle filter (PF)-based direct position tracking method utilizing multiple distributed observation stations. Traditional passive tracking methods are anchored on repetitive position estimation, where the set of consecutive estimates provides the tracking trajectory, such as Two-step and direct position determination methods. However, duplicate estimates can be computationally expensive. In addition, these techniques suffer from data association problems. The PF algorithm is a tracking method that avoids these drawbacks, but the conventional PF algorithm is unable to construct a likelihood function from the received signals of multiple observatories to determine the weights of particles. Therefore, we developed an improved PF algorithm with the likelihood function modified by the projection approximation subspace tracking with deflation (PASTd) algorithm. The proposed algorithm uses the projection subspace and spectral function to replace the likelihood function of PF. Then, the weights of particles are calculated jointly by multiple likelihood functions. Finally, the tracking problem of multiple targets is solved by multiple sets of particles. Simulations demonstrate the effectiveness of the proposed method in terms of computational complexity and tracking accuracy.

In this paper, we focus on the channel estimation for multi-user MIMO-OFDM systems in rich scattering environments. We find that channel sparsity in the delay-angle domain is severely compromised in rich scattering environments, so that most existing compressed sensing (CS) based techniques can harvest a very limited gain (if any) in reducing the channel estimation overhead. To address the problem, we propose the learning-based turbo message passing (LTMP) algorithm. Instead of exploiting the channel sparsity, LTMP is able to efficiently extract the channel feature via deep learning as well as to exploit the channel continuity in the frequency domain via block-wise linear modelling. More specifically, as a component of LTMP, we develop a multi-scale parallel dilated convolutional neural network (MPDCNN), which leverages frequency-space channel correlation in different scales for channel denoising. We evaluate the LTMP's performance in MIMO-OFDM channels using the 3rd generation partnership project (3GPP) clustered delay line (CDL) channel models. Simulation results show that the proposed channel estimation method has more than 5 dB power gain than the existing algorithms when the normalized mean-square error of the channel estimation is -20 dB. The proposed algorithm also exhibits strong robustness in various environments.

In this paper, a novel wideband 8-element multiple-input and multiple-output (MIMO) antenna based on Booker’s relation is proposed for the fifth generation (5G) handset applications. The 8 antenna elements are arranged symmetrically along the two longer vertical side-edge frames of the handset. Each antenna element is composed of a monopole and a slot radiation structure, in which wideband characteristic covering 3140-5620MHz can be obtained. Note that the L-shaped monopole and the slot can be deemed as complementary counterparts approximatively. Furthermore, the \textit{Z}-parameter of the proposed wideband antenna element is equivalent to the shunt impedance of monopole as well as slot radiator. Based on Booker’s relation, the wideband input impedance characteristic is therein achieved compared with conventional wideband technique such as multi-resonance. Four L-shaped stubs as well as two slots etched on the ground plane are utilized to achieve acceptable isolation performance better than 13 dB, with total efficiency higher than 60\% and envelope correlation coefficients (ECCs) lower than 0.1. The proposed antenna scheme can be a good candidate for 5G handset applications with the advantages of wideband, simple structure, high efficiency, and acceptable isolation performance. Also, the scheme might be a rewarding attempt to promote the Booker’s relation in the application of 5G terminal MIMO antenna designs.

To address the contradiction between the explosive growth of wireless data and the limited spectrum resources, semantic communication has been emerging as a promising communication paradigm. In this paper, we thus design a speech semantic coded communication system, referred to as Deep-STS (i.e., Deep-learning based Speech To Speech), for the low-bandwidth speech communication. Specifically, we first deeply compress the speech data through extracting the textual information from the speech based on the conformer encoder and connectionist temporal classification decoder at the transmitter side of Deep-STS system. In order to facilitate the final speech timbre recovery, we also extract the short-term timbre feature of speech signals only for the starting 2s duration by the long short-term memory network. Then, the Reed-Solomon coding and hybrid automatic repeat request protocol are applied to improve the reliability of transmitting the extracted text and timbre feature over the wireless channel. Third, we reconstruct the speech signal by the mel spectrogram prediction network and vocoder, when the extracted text is received along with the timbre feature at the receiver of Deep-STS system. Finally, we develop the demo system based on the USRP and GNU radio for the performance evaluation of Deep-STS. Numerical results show that the accuracy of text extraction approaches 95%, and the mel cepstral distortion between the recovered speech signal and the original one in the spectrum domain is less than 10. Furthermore, the experimental results show that the proposed Deep-STS system can reduce the total delay of speech communication by 85% on average compared to the G.723 coding at the transmission rate of 5.4 kbps. More importantly, the coding rate of the proposed Deep-STS system is extremely low, only 0.2 kbps for continuous speech communication. It is worth noting that the Deep-STS with lower coding rate can support the low-zero-power speech communication, unveiling a new era in ultra-efficient coded communications.

In response to the current gaps in effective proactive defense methods within application security and the limited integration of security components with applications, this paper proposes a biomimetic security model, called NeuroShield, specifically designed for web applications. Inspired by the "perception-strategy-effect-feedback" mechanism of the human nervous control system, the model integrates biomimetic elements akin of neural receptors and effectors into applications. This integration facilitates a multifaceted approach to security: enabling data introspection for detailed perception and regulation of application behavior, providing proactive defense capabilities to detect and block security risks in real-time, and incorporating feedback optimization to continuously adjust and enhance security strategies based on prevailing conditions. Experimental results affirm the efficacy of this neural control mechanism-based biomimetic security model, demonstrating a proactive defense success rate exceeding 95%, thereby offering a theoretical and structural foundation for biomimetic immunity in web applications.

As emerging services continue to be explored, indoor communications geared towards different user requirements will face severe challenges such as larger penetration losses and more critical multipath issues, leading to difficulties in achieving flexible coverage. In this paper, we introduce transmissive reconfigurable intelligent surfaces (RISs) as intelligent passive auxiliary devices into indoor scenes, replacing conventional ultra-dense small cell and relay forwarding approaches to address these issues at low deployment and operation costs. Specifically, we study the optimization design of active and passive beamforming for the transmissive RISs-aided indoor multi-user downlink communication systems. This involves considering more realistic indoor congestion modeling and near-field propagation characteristics. The goal of our optimization is to minimize the total transmit power at the access point (AP) for different user service requirements, including quality-of-service (QoS) and wireless power transfer (WPT). Due to the non-convex nature of the optimization problem, adaptive penalty coefficients are imported to solve it alternatively with closed-form solutions for both active and passive beamforming. Simulation results demonstrate that the use of transmissive RISs is indeed an efficient way to achieve flexible coverage in indoor scenarios. Furthermore, the proposed optimization algorithm has been proven to be effective and robust in achieving energy-saving transmission.

Physical layer security is an important method to improve the secrecy performance of wireless communication systems. In this paper, we analyze the effect of employing channel correlation to improve security performance in multiple-input multiple-output (MIMO) scenario with antenna selection (AS) scheme. We first derive the analytical expressions of average secrecy capacity (ASC) and secrecy outage probability (SOP) by the first order Marcum Q function. Then, the asymptotic expressions of ASC and SOP in two specific scenarios are further derived. The correctness of analytical and asymptotic expressions is verified by Monte Carlo simulations. The conclusions suggest that the analytical expressions of ASC and SOP are related to the product of transmitting and receiving antennas; increasing the number of antennas is beneficial to ASC and SOP. Besides, when the target rate is set at a low level, strong channel correlation is bad for ASC, but is beneficial to SOP.

New communication systems require high spectral and energy efficiencies to meet the growing demand for services in future networks. In this paper, an efficient multiple parallel reconfigurable intelligent surfaces (RIS)-assisted multiuser (MU) multiple input-multiple output (MIMO) double quadrature spatial modulation (DQSM) downlink transmission system is presented. In the transmitter, the proposed N-RIS-MU-MIMO-DQSM system uses a modified block diagonalization technique and a genetic algorithm (GA) to jointly design the precoding signals required at the base station (BS) and the optimal phase changes required at multiple RISs. A reduced detection complexity and improved bit error rate (BER) performance are achieved by incorporating spatial modulation. The proposed system is compared under the same conditions and parameters with two reference systems, considering blind and optimized RISs approaches over correlated Rayleigh fading channels. Results show that compared with a similar system that does not use RISs, the proposed system has up to 30 dB gain in BER performance. Compared with a similar system based on conventional quadrature amplitude modulation (QAM), the proposed system has gains of up to 2-3 dB in BER performance and up to 55.8% lower detection complexity for the analyzed cases.

Codebooks have been indispensable for wireless communication standard since the first release of the Long-Term Evolution in 2009. They offer an efficient way to acquire the channel state information (CSI) for multiple antenna systems. Nowadays, a codebook is not limited to a set of pre-defined precoders, it refers to a CSI feedback framework, which is more and more sophisticated. In this paper, we review the codebooks in 5G New Radio (NR) standards. The codebook timeline and the evolution trend are shown. Each codebook is elaborated with its motivation, the corresponding feedback mechanism, and the format of the precoding matrix indicator. Some insights are given to help grasp the underlying reasons and intuitions of these codebooks. Finally, we point out some unresolved challenges of the codebooks for future evolution of the standards. In general, this paper provides a comprehensive review of the codebooks in 5G NR and aims to help researchers understand the CSI feedback schemes from a standard and industrial perspective.

In this paper, an index modulation (IM) aided uplink orthogonal time frequency space modulation (OTFS) structure for sparse code multiple access (SCMA) is proposed. To be more specific, the information bits are firstly partitioned for transmit antenna (TA) selection and sparse codeword mapping, respectively. Subsequently, the codewords deployed on the 2-dimensional (2D) delay-Doppler (DD) plane are transmitted by the selected TA, and the superimposed signals are jointly detected at the receiver. Furthermore, a low-complexity zero-embedded expectation propagation (ZE-EP) detector is conceived, where the codebooks are extended with zero vectors to reflect the silent indices. The simulation results demonstrate that the proposed IM-OTFS-SCMA system is capable of providing significant performance gain over the OTFS-SCMA counterpart.

The utilization of millimeter-wave frequencies and cognitive radio (CR) are promising ways to increase the spectral efficiency of wireless communication systems. However, conventional CR spectrum sensing techniques entail sampling the received signal at a Nyquist rate, and they are not viable for wideband signals due to their high cost. This paper expounds on how sub-Nyquist sampling in conjunction with deep learning can be leveraged to remove this limitation. To this end, we propose a multi-task learning (MTL) framework using convolutional neural networks for the joint inference of the underlying narrowband signal number, their modulation scheme, and their location in a wideband spectrum. We demonstrate the effectiveness of the proposed framework for real-world millimeter-wave wideband signals collected by physical devices, exhibiting a $91.7 \%$ accuracy in the joint inference task when considering up to two narrowband signals over a wideband spectrum. Ultimately, the proposed data-driven approach enables on-the-fly wideband spectrum sensing, combining accuracy, and computational efficiency, which are indispensable for CR and opportunistic networking.

The deployment of multiple intelligent reflecting surfaces (IRSs) in blockage-prone millimeter wave (mmWave) communication networks have garnered considerable attention lately. Despite the remarkably low circuit power consumption per IRS element, the aggregate energy consumption becomes substantial if all elements of an IRS are turned on given a considerable number of IRSs, resulting in lower overall energy efficiency (EE). To tackle this challenge, we propose a flexible and efficient approach that individually controls the status of each IRS element. Specifically, the network EE is maximized by jointly optimizing the associations of base stations (BSs) and user equipments (UEs), transmit beamforming, phase shifts of IRS elements, and the associations of individual IRS elements and UEs. The problem is efficiently addressed in two phases. First, the Gale-Shapley algorithm is applied for BS-UE association, followed by a block coordinate descent-based algorithm that iteratively solves the subproblems related to active beamforming, phase shifts, and element-UE associations. To reduce the tremendous dimensionality of optimization variables introduced by element-UE associations in large-scale IRS networks, we introduce an efficient algorithm to solve the associations between IRS elements and UEs. Numerical results show that the proposed elementwise control scheme improves EE by 34.24% compared to the network with IRS-all-on scheme.

The intelligent operation management of distribution services is crucial for the stability of power systems. Integrating the large language model (LLM) with 6G edge intelligence provides customized management solutions. However, the adverse effects of false data injection (FDI) attacks on the performance of LLMs cannot be overlooked. Therefore, we propose an FDI attack detection and LLM-assisted resource allocation algorithm for 6G edge intelligence-empowered distribution power grids. First, we formulate a resource allocation optimization problem. The objective is to minimize the weighted sum of the global loss function and total LLM fine-tuning delay under constraints of long-term privacy entropy and energy consumption. Then, we decouple it based on virtual queues. We utilize an LLM-assisted deep Q network (DQN) to learn the resource allocation strategy and design an FDI attack detection mechanism to ensure that fine-tuning remains on the correct path. Simulations demonstrate that the proposed algorithm has excellent performance in convergence, delay, and security.

High-performance positioning, navigation and timing (PNT) service is critical to the safe flight of low-altitude aircraft and the effective management of low altitude traffic. In low-altitude economic scenarios, the specificity of massive unmanned aerial vehicle (UAV) flights and the complexity of low-altitude airspace traffic management impose stringent demand on the high-continuity, high-accuracy, real-time, and high-security PNT service. However, the current PNT service, which primarily relies on Global Navigation Satellite System (GNSS), Micro-Electro-Mechanical System Inertial Navigation System (MEMS INS), etc., is completely inadequate to support the future needs of low-altitude economic development. In order to bridge the huge gap between existing capability and future demand, a three-layer PNT architecture based on the collaboration of space-based, air-based and ground-based PNT systems is proposed for low-altitude economy. The space-based layer consists of high, medium even possible low orbit GNSS constellations, such as BeiDou Navigation Satellite System (BDS), for high-precision, high-security absolute positioning and timing. The air-based layer leverages inter-aircraft links for high-reliability dynamic relative positioning. The ground-based layer includes pseudolite network, as well as 5G-advanced (5G-A)/6G network, for more comprehensive coverage and real-time positioning. To this end, it is imperative to make breakthroughs in key technologies, from systems to airborne terminal, including but not limited to high-precision anti-jamming GNSS signal processing, high-reliability relative positioning, real-time pseudolite positioning, and high-efficient multi-source information fusion at airborne terminal, etc. Due to the moderate redundancy, heterogeneous mechanism, and multiple coverage from multiple PNT systems, the proposed layered PNT architecture possesses high robustness and resilient. Additionally, the integration of INS, LiDAR and vision etc. perception technologies can significantly enhance the PNT capability. As a result, the proposed three-layer PNT architecture enable greater autonomy for low-altitude aircraft and intelligent traffic management for massive UAV operations, and promoting the safe and efficient development of the low-altitude economy.

Applying non-orthogonal multiple access (NOMA) to the mobile edge computing (MEC) network supported by unmanned aerial vehicles (UAVs) can improve spectral efficiency and achieve massive user access on the basis of solving computing resource constraints and coverage problems. However, the UAV-enabled network has a serious risk of information leakage on account of the openness of wireless channel. This paper considers a UAV-MEC secure network based on NOMA technology, which aims to minimize the UAV energy consumption. To achieve the purpose while meeting the security and users' latency requirements, we formulate an optimization problem that jointly optimizes the UAV trajectory and the allocation of network resources. Given that the original problem is non-convex and multivariate coupled, we proposed an effective algorithm to decouple the non-convex problem into independent user relation coefficients and subproblems based on successive convex approximation (SCA) and block coordinate descent (BCD). The simulation results showcase the performance of our optimization scheme across various parameter settings and confirm its superiority over other benchmarks with respect to energy consumption.

The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment. The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment. Moreover, the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service (QoS) without impacting the Service Level Agreements (SLAs). However, the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements. In this paper, Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme (CBBM-WARMS) is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment. This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud. Then, it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources. It further used CBBM for potential Virtual Machine (VM) deployment that attributes towards the provision of optimal resources. It is proposed with the capability of achieving optimal QoS with minimized time, energy consumption, SLA cost and SLA violation. The experimental validation of the proposed CBBM-WARMS confirms minimized SLA cost of 19.21\% and reduced SLA violation rate of 18.74\%, better than the compared autonomic cloud resource managing frameworks.

Multilevel coding (MLC) is a commonly used polar coded modulation scheme, but challenging to implement in engineering due to its high complexity and long decoding delay for high-order modulations. To address these limitations, a novel two-level serially concatenated MLC scheme, in which the bit-levels with similar reliability are bundled and transmitted together, is proposed. The proposed scheme hierarchically protects the two bit-level sets: the bit-level sets at the higher level are sufficiently reliable and do not require excessive resources for protection, whereas only the bit-level sets at the lower level are encoded by polar codes. The proposed scheme has the advantages of low power consumption, low delay and high reliability. Moreover, an optimized constellation signal labeling rule that can enhance the performance is proposed. Finally, the superiority of the proposed scheme is validated through the theoretical analysis and simulation results. Compared with the bit interleaving coding modulation (BICM) scheme, under 256-quadrature amplitude modulation (QAM), the proposed scheme attains a performance gain of 1.0 dB while reducing the decoding complexity by 54.55%.

Constituted by BCH component codes and its ordered statistics decoding (OSD), the successive cancellation list (SCL) decoding of U-UV structural codes can provide competent error-correction performance in the short-to-medium length regime. However, this list decoding complexity becomes formidable as the decoding output list size increases. This is primarily incurred by the OSD. Addressing this challenge, this paper proposes the low complexity SCL decoding through reducing the complexity of component code decoding, and pruning the redundant SCL decoding paths. For the former, an efficient skipping rule is introduced for the OSD so that the higher order decoding can be skipped when they are not possible to provide a more likely codeword candidate. It is further extended to the OSD variant, the box-and-match algorithm (BMA), in facilitating the component code decoding. Moreover, through estimating the correlation distance lower bounds (CDLBs) of the component code decoding outputs, a path pruning (PP)-SCL decoding is proposed to further facilitate the decoding of U-UV codes. In particular, its integration with the improved OSD and BMA is discussed. Simulation results show that significant complexity reduction can be achieved. Consequently, the U-UV codes can outperform the cyclic redundancy check (CRC)-polar codes with a similar decoding complexity.

Low Earth orbit (LEO) satellite networks have the advantages of low transmission delay and low deployment cost, playing an important role in providing reliable services to ground users. This paper studies an efficient inter-satellite cooperative computation offloading (ICCO) algorithm for LEO satellite networks. Specifically, an ICCO system model is constructed, which considers using neighboring satellites in the LEO satellite networks to collaboratively process tasks generated by ground user terminals, effectively improving resource utilization efficiency. Additionally, the optimization objective of minimizing the system task computation offloading delay and energy consumption is established, which is decoupled into two sub-problems. In terms of computational resource allocation, the convexity of the problem is proved through theoretical derivation, and the Lagrange multiplier method is used to obtain the optimal solution of computational resources. To deal with the task offloading decision, a dynamic sticky binary particle swarm optimization algorithm is designed to obtain the offloading decision by iteration. Simulation results show that the ICCO algorithm can effectively reduce the delay and energy consumption.

Frequent extreme disasters have led to frequent large-scale power outages in recent years. To quickly restore power, it is necessary to understand the damage information of the distribution network accurately. However, the public network communication system is easily damaged after disasters, causing the operation center to lose control of the distribution network. In this paper, we considered using satellites to transmit the distribution network data and focus on the resource scheduling problem of the satellite emergency communication system for the distribution network. Specifically, this paper first formulates the satellite beam-pointing problem and the access-channel joint resource allocation problem. Then, this paper proposes the Priority-based Beam-pointing and Access-Channel joint optimization algorithm (PBAC), which uses convex optimization theory to solve the satellite beam pointing problem, and adopts the block coordinate descent method, Lagrangian dual method, and a greedy algorithm to solve the access-channel joint resource allocation problem, thereby obtaining the optimal resource scheduling scheme for the satellite network. Finally, this paper conducts comparative experiments with existing methods to verify the effectiveness of the proposed methods. The results show that the total weighted transmitted data of the proposed algorithm is increased by about 19.29$\sim$26.29% compared with other algorithms.