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.

In vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the edge receives a local model and updates the global model, effectively reducing the global aggregation latency. Due to different amounts of local data, computing capabilities and locations of the vehicles, renewing the global model with same weight is inappropriate. The above factors will affect the local calculation time and upload time of the local model, and the vehicle may also be affected by Byzantine attacks, leading to the deterioration of the vehicle data. However, based on deep reinforcement learning (DRL), we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL. At the same time, when aggregating AFL, we can focus on those vehicles with better performance to improve the accuracy and safety of the system. In this paper, we proposed a vehicle selection scheme based on DRL in VEC. In this scheme, vehicle's mobility, channel conditions with temporal variations, computational resources with temporal variations, different data amount, transmission channel status of vehicles as well as Byzantine attacks were taken into account. Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

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.

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.

With the rapid development of cloud computing, edge computing, and smart devices, computing power resources indicate a trend of ubiquitous deployment. The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect. To overcome these problems and improve network efficiency, a new network computing paradigm is proposed, i.e., Computing Power Network (CPN). Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly. In this survey, we make an exhaustive review on the state-of-the-art research efforts on computing power network. We first give an overview of computing power network, including definition, architecture, and advantages. Next, a comprehensive elaboration of issues on computing power modeling, information awareness and announcement, resource allocation, network forwarding, computing power transaction platform and resource orchestration platform is presented. The computing power network testbed is built and evaluated. The applications and use cases in computing power network are discussed. Then, the key enabling technologies for computing power network are introduced. Finally, open challenges and future research directions are presented as well.

In recent times, various power control and clustering approaches have been proposed to enhance overall performance for cell-free massive multiple-input multiple-output (CF-mMIMO) networks. With the emergence of deep reinforcement learning (DRL), significant progress has been made in the field of network optimization as DRL holds great promise for improving network performance and efficiency. In this work, our focus delves into the intricate challenge of joint cooperation clustering and downlink power control within CF-mMIMO networks. Leveraging the potent deep deterministic policy gradient (DDPG) algorithm, our objective is to maximize the proportional fairness (PF) for user rates, thereby aiming to achieve optimal network performance and resource utilization. Moreover, we harness the concept of “divide and conquer” strategy, introducing two innovative methods termed alternating DDPG (A-DDPG) and hierarchical DDPG (H-DDPG). These approaches aim to decompose the intricate joint optimization problem into more manageable sub-problems, thereby facilitating a more efficient resolution process. Our findings unequivocally showcase the superior efficacy of our proposed DDPG approach over the baseline schemes in both clustering and downlink power control. Furthermore, the A-DDPG and H-DDPG obtain higher performance gain than DDPG with lower computational complexity.

With the development of wireless communication technology, an urgent problem to be solved is co-site broadband interference on independent communication platforms such as satellites, space stations, aircrafts and ships. Also, the problem of strong self-interference rejection should be solved in the co-time co-frequency full duplex mode which realizes spectrum multiplication in 5G communication technology. In the research of such interference rejection, interference cancellation technology has been applied. In order to reject multipath interference, multitap double LMS (Least Mean Square) loop interference cancellation system is often used for cancelling RF (Radio Frequency) domain interference cancelling. However, more taps will lead to a more complex structure of the cancellation system. A novel tap single LMS loop adaptive interference cancellation system was proposed to improve the system compactness and reduce the cost. In addition, a mathematical model was built for the proposed cancellation system, the correlation function of CP2FSK (Continuous Phase Binary Frequency Shift Keying) signal was derived, and the quantitative relationship was established between the correlation function and the interference signal bandwidth and tap delay differential. The steady-state weights and the expression of the average interference cancellation ratio (ICR) were deduced in the scenes of LOS (Line of Sight) interference with antenna swaying on an independent communication platform and indoor multipath interference. The quantitative relationship was deeply analyzed between the interference cancellation performance and the parameters such as antenna swing, LMS loop gain, and interference signal bandwidth, which was verified by simulation experiment. And the performance of the proposed interference cancellation system was compared with that of the traditional double LMS loop cancellation system. The results showed that the compact single LMS loop cancellation system can achieve an average interference rejection capability comparable to the double LMS loop cancellation system.

Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment, we consider few-shot learning-based automatic modulation classification (AMC) to improve its reliability. A data enhancement module (DEM) is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC. Multimodal network is designed to have multiple residual blocks, where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction. Moreover, a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM. Since different model may output different results, cooperative classifier is designed to avoid the randomness of single model and improve the reliability. Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.

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.

This work employs intelligent reflecting surface (IRS) to enhance secure and covert communication performance. We formulate an optimization problem to jointly design both the reflection beamformer at IRS and transmit power at transmitter Alice in order to optimize the achievable secrecy rate at Bob subject to a covertness constraint. We first develop a Dinkelbach-based algorithm to achieve an upper bound performance and a high-quality solution. For reducing the overhead and computational complexity of the Dinkelbach-based scheme, we further conceive a low-complexity algorithm in which analytical expression for the IRS reflection beamforming is derived at each iteration. Examination result shows that the devised low-complexity algorithm is able to achieve similar secrecy rate performance as the Dinkelbach-based algorithm. Our examination also shows that introducing an IRS into the considered system can significantly improve the secure and covert communication performance relative to the scheme without IRS.

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 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.

The proportionate recursive least squares (PRLS) algorithm has shown faster convergence and better performance than both proportionate updating (PU) mechanism based least mean squares (LMS) algorithms and RLS algorithms with a sparse regularization term. In this paper, we propose a variable forgetting factor (VFF) PRLS algorithm with a sparse penalty, e.g., $l_1$-norm, for sparse identification. To reduce the computation complexity of the proposed algorithm, a fast implementation method based on dichotomous coordinate descent (DCD) algorithm is also derived. Simulation results indicate superior performance of the proposed algorithm.

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.

Time-Sensitive Network (TSN) with deterministic transmission capability is increasingly used in many emerging fields. It mainly guarantees the Quality of Service (QoS) of applications with strict requirements on time and security. One of the core features of TSN is traffic scheduling with bounded low delay in the network. However, traffic scheduling schemes in TSN are usually synthesized offline and lack dynamism. To implement incremental scheduling of newly arrived traffic in TSN, we propose a Dynamic Response Incremental Scheduling (DR-IS) method for time-sensitive traffic and deploy it on a software-defined time-sensitive network architecture. Under the premise of meeting the traffic scheduling requirements, we adopt two modes, traffic shift and traffic exchange, to dynamically adjust the time slot injection position of the traffic in the original scheme, and determine the sending offset time of the new time-sensitive traffic to minimize the global traffic transmission jitter. The evaluation results show that DR-IS method can effectively control the large increase of traffic transmission jitter in incremental scheduling without affecting the transmission delay, thus realizing the dynamic incremental scheduling of time-sensitive traffic in TSN.

Automatic modulation classification (AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet, to classify different kinds of modulation signals. The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by down-sampling convolution. Moreover, through dense skip-connecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multi-level features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model. The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset "Over the Air" in signal-to-noise (SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and 97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet. Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.

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.

Intelligent Reflecting Surface (IRS), with the potential capability to reconstruct the electromagnetic propagation environment, evolves a new IRS-assisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals leakage. However, when multiple IRSs are involved, accurate channel estimation is still a challenge due to the extra hardware complexity and communication overhead. Besides the cross-interference caused by massive reflecting paths, it is hard to obtain the close-formed solution for the optimization of covert communications. On this basis, the paper improves a heterogeneous multi-agent deep deterministic policy gradient (MADDPG) approach for the joint active and passive beamforming (Joint A&P BF) optimization without the channel estimation, where the base station (BS) and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency (CSE) cooperatively. Thanks to the 'centralized training and distributed execution' feature of MADDPG, each agent can execute the active or passive beamforming independently based on its partial observation without referring to others. Numeral results demonstrate that the proposed deep reinforcement learning (DRL) approach could not only obtain a preferable CSE of legitimate users and a low detection of probability (LPD) of warden, but also alleviate the communication overhead and simplify the IRSs deployment.

In this paper, an intelligent reflecting surface (IRS)-and-unmanned aerial vehicle (UAV)-assisted two-way amplify-and-forward (AF) relay network in maritime Internet of Things (IoT) is proposed, where ship1 ($\text{S}_1$) and ship2 ($\text{S}_2$) can be viewed as data collecting centers. To enhance the message exchange rate between $\text{S}_1$ and $\text{S}_2$, a problem of maximizing minimum rate is cast, where the variables, namely AF relay beamforming matrix and IRS phase shifts of two time slots, need to be optimized. To achieve a maximum rate, a low-complexity alternately iterative (AI) scheme based on zero forcing and successive convex approximation (LC-ZF-SCA) algorithm is presented. To obtain a significant rate enhancement, a high-performance AI method based on one step, semidefinite programming and penalty SCA (ONS-SDP-PSCA) is proposed. Simulation results show that by the proposed LC-ZF-SCA and ONS-SDP-PSCA methods, the rate of the IRS-and-UAV-assisted AF relay network surpass those of with random phase and only AF relay networks. Moreover, ONS-SDP-PSCA perform better than LC-ZF-SCA in aspect of rate.

Integrated sensing and communication (ISAC) is considered an effective technique to solve spectrum congestion in the future. In this paper, we consider a hybrid reconfigurable intelligent surface (RIS)-assisted downlink ISAC system that simultaneously serves multiple single-antenna communication users and senses multiple targets. Hybrid RIS differs from fully passive RIS in that it is composed of both active and passive elements, with the active elements having the effect of amplifying the signal in addition to phase-shifting. We maximize the achievable sum rate of communication users by collaboratively improving the beamforming matrix at the dual function base station (DFBS) and the phase-shifting matrix of the hybrid RIS, subject to the transmit power constraint at the DFBS, the signal-to-interference-plus-noise-ratio (SINR) constraint of the radar echo signal and the RIS constraint are satisfied at the same time. The built-in RIS-assisted ISAC design problem model is significantly non-convex due to the fractional objective function of this optimization problem and the coupling of the optimization variables in the objective function and constraints. As a result, we provide an effective alternating optimization approach based on fractional programming (FP) with block coordinate descent (BCD) to solve the optimization variables. Results from simulations show that the hybrid RIS-assisted ISAC system outperforms the other benchmark solutions.

Puncturing has been recognized as a promising technology to cope with the coexistence problem of enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC) traffic. However, the steady performance of eMBB traffic while meeting the requirements of URLLC traffic with puncturing is a major challenge in some realistic scenarios. In this paper, we pay attention to the timely and energy-efficient processing for eMBB traffic in the industrial Internet of Things (IIoT), where mobile edge computing (MEC) is employed for data processing. Specifically, the performance of eMBB traffic and URLLC traffic in a MEC-based IIoT system is ensured by setting the threshold of tolerable delay and outage probability, respectively. Furthermore, considering the limited energy supply, an energy minimization problem of eMBB device is formulated under the above constraints, by jointly optimizing the resource blocks (RBs) punctured by URLLC traffic, data offloading and transmit power of eMBB device. With Markov's inequality, the problem is reformulated by transforming the probabilistic outage constraint into a deterministic constraint. Meanwhile, an iterative energy minimization algorithm (IEMA) is proposed. Simulation results demonstrate that our algorithm has a significant reduction in the energy consumption for eMBB device and achieves a better overall effect compared to several benchmarks.

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.

With the rapid development and application of energy harvesting technology, it has become a prominent research area due to its significant benefits in terms of green environmental protection, convenience, and high safety and efficiency. However, the uneven energy collection and consumption among IoT devices at varying distances may lead to resource imbalance within energy harvesting networks, thereby resulting in low energy transmission efficiency. To enhance the energy transmission efficiency of IoT devices in energy harvesting, this paper focuses on the utilization of collaborative communication, along with pricing-based incentive mechanisms and auction strategies. We propose a dynamic relay selection scheme, including a ladder pricing mechanism based on energy level and a Kuhn-Munkre Algorithm based on an auction theory employing a negotiation mechanism, to encourage more IoT devices to participate in the collaboration process. Simulation results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of improving the energy efficiency of the system.

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.

The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day. Therefore, it is essential to make medical services connected to internet, available in every remote location during these situations. Also, the security issues in the Internet of Medical Things (IoMT) used in these service, make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures. Hence, services in the healthcare ecosystem need rapid, uninterrupted, and secure facilities. The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas. This research aims to develop an intelligent Software Defined Networks (SDNs) enabled secure framework for IoT healthcare ecosystem. We propose a hybrid of machine learning and deep learning techniques (DNN + SVM) to identify network intrusions in the sensor-based healthcare data. In addition, this system can efficiently monitor connected devices and suspicious behaviours. Finally, we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios. the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.

Wireless Sensor Network (WSN) comprises a set of interconnected, compact, autonomous, and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment. WSNs are commonly used in various applications such as environmental monitoring, surveillance, healthcare, agriculture, and industrial automation. Despite the benefits of WSN, energy efficiency remains a challenging problem that needs to be addressed. Clustering and routing can be considered effective solutions to accomplish energy efficiency in WSNs. Recent studies have reported that metaheuristic algorithms can be applied to optimize cluster formation and routing decisions. This study introduces a new Northern Goshawk Optimization with boosted coati optimization algorithm for cluster-based routing (NGOBCO-CBR) method for WSN. The proposed NGOBCO-CBR method resolves the hot spot problem, uneven load balancing, and energy consumption in WSN. The NGOBCO-CBR technique comprises two major processes such as NGO based clustering and BCO-based routing. In the initial phase, the NGO-based clustering method is designed for cluster head (CH) selection and cluster construction using five input variables such as residual energy (RE), node proximity, load balancing, network average energy, and distance to BS (DBS).
Besides, the NGOBCO-CBR technique applies the BCO algorithm for the optimum selection of routes to BS. The experimental results of the NGOBCO-CBR technique are studied under different scenarios, and the obtained results showcased the improved efficiency of the NGOBCO-CBR technique over recent approaches in terms of different measures.

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.

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.

Named data networking (NDNs) is an idealized deployment of information-centric networking (ICN) that has attracted attention from scientists and scholars worldwide. A distributed in-network caching scheme can efficiently realize load balancing. However, such a ubiquitous caching approach may cause problems including duplicate caching and low data diversity, thus reducing the caching efficiency of NDN routers. To mitigate these caching problems and improve the NDN caching efficiency, in this paper, a hierarchical-based sequential caching (HSC) scheme is proposed. In this scheme, the NDN routers in the data transmission path are divided into various levels and data with different request frequencies are cached in distinct router levels. The aim is to cache data with high request frequencies in the router that is closest to the content requester to increase the response probability of the nearby data, improve the data caching efficiency of named data networks, shorten the response time, and reduce cache redundancy. Simulation results show that this scheme can effectively improve the cache hit rate (CHR) and reduce the average request delay (ARD) and average route hop (ARH).

The global Internet is composed of more than 70,000 autonomous domain networks interconnected through the Border Gateway Protocol (BGP). Studying the ecological evolution of BGP network is of great significance for analyzing the evolution trend of the global Internet. This paper focuses on the evolution of Country-Level BGP network ecosystems in 24 years, and innovatively studies the relationship between Country-Level BGP network and economy, breaking through the limitations of traditional research that only focuses on BGP network. The results revealed that the number of global BGP networks has increased by nearly 23 times and that network interconnection has increased nearly 80 times over in 24 years. It was found that the growth of the global BGP network ecosystem has slowed overall due to major global security events, although the BGP network ecosystem in some Southeast Asian countries is developing against the trend. At the same time, there is a significant positive correlation between the BGP network ecology and the national economy in the time dimension; there is a strong positive correlation in the spatial dimension, but the trend is weakening year by year.

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.

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.

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.

In a post-disaster environment characterized by frequent interruptions in communication links, traditional wireless communication networks are ineffective. Although the "store-carry-forward" mechanism characteristic of Delay Tolerant Networks (DTNs) can transmit data from Internet of things devices to more reliable base stations or data centres, it also suffers from inefficient data transmission and excessive transmission delays. To address these challenges, we propose an intelligent routing strategy based on node sociability for post-disaster emergency network scenarios. First, we introduce an intelligent routing strategy based on node intimacy, which selects more suitable relay nodes and assigns the corresponding number of message copies based on comprehensive utility values. Second, we present an intelligent routing strategy based on geographical location of nodes to forward message replicas secondarily based on transmission utility values. Finally, experiments demonstrate the effectiveness of our proposed algorithm in terms of message delivery rate, network cost ratio and average transmission delay.

In the time-difference-of-arrival (TDOA) localization, robust least squares (LS) problems solved by mathematical programming were proven to be superior in mitigating the effects of non-line-of-sight (NLOS) propagation. However, the existing algorithms still suffer from two disadvantages: 1) The algorithms strongly depend on prior information; 2) The approaches do not satisfy the mean square error (MSE) optimal criterion of the measurement noise. To tackle the troubles, we first formulate an MSE minimization model for measurement noise by taking the source and the NLOS biases as variables. To obtain stable solutions, we introduce a penalty function to avoid abnormal estimates. We further tackle the nonconvex locating problem with semidefinite relaxation techniques. Finally, we incorporate mixed constraints and variable information to improve the estimation accuracy. Simulations and experiments show that the proposed method achieves consistent performance and good accuracy in dynamic NLOS environments.

The received signals used for sparse code multiple access (SCMA) detection are usually contaminated with noise during transmission, which exposes an issue of low decoding efficiency. To address this issue, a novel detector based on a residual network (ResNet) perception fusion framework (RSMPA) is proposed for uplink SCMA system in this paper. Specifically, we first formulate a joint design of perception system and traditional communication module. A perception framework based on ResNet is applied to cancel the noise component and enhance the communication system performance. The ResNet model is designed and trained using the clean and noisy SCMA signal, respectively. Based on the denoised output, information iteration process is executed for multi-user detection. Simulation results indicate that the perception model achieves an excellent denoising performance for SCMA system and the proposed scheme outperforms the conventional detection algorithms in terms of SER performance.

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.

Reconfigurable intelligent surface (RIS) has proven to be promising for future wireless communication. Due to its ability to manipulate electromagnetic (EM) waves, RIS provides a flexible and programmable way to implement intelligent wireless environments. While path loss modeling has been conducted in some prior research, an issue remaining unknown is the characteristics of multi-beam path loss for RIS. In this paper, we model, simulate and measure the multi-beam path loss in RIS-assisted broadcast communication scenarios. We propose two specific configurations of RIS and derive the path loss models, which reveal that the incident beam can be equally divided into multiple beams without power loss through rational design of the phase coding. The proposed path loss model is validated through simulation subsequently. To further verify our conclusions, we build a millimeter wave (mmWave) measurement system with a 35 GHz fabricated RIS. The measurement result corresponds well with the simulation, which shows a difference of about 3 dB in the received signal power of quad-beam compared with dual-beam, as well as dual-beam compared with single-beam, except for the impact of radiation patterns of the antennas and RIS elements.

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.