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.

Spectrum prediction is considered as a key technology to assist spectrum decision. Despite the great efforts that have been put on the construction of spectrum prediction, achieving accurate spectrum prediction emphasizes the need for more advanced solutions. In this paper, we propose a new multi-channel multi-step spectrum prediction method using Transformer and stacked bidirectional LSTM (Bi-LSTM), named TSB. Specifically, we use multi-head attention and stacked Bi-LSTM to build a new Transformer based on encoder-decoder architecture. The self-attention mechanism composed of multiple layers of multi-head attention can continuously attend to all positions of the multichannel spectrum sequences. The stacked Bi-LSTM can learn these focused coding features by multi-head attention layer by layer. The advantage of this fusion mode is that it can deeply capture the long-term dependence of multichannel spectrum data. We have conducted extensive experiments on a dataset generated by a real simulation platform. The results show that the proposed algorithm performs better than the baselines.

In this paper, we investigate covert communications under multi-antenna detection, and explore the impacts of the warden's channel state information (CSI) availability and the noise uncertainty on system covert capability. The detection performance at warden is analyzed in two cases under the perfect and statistical CSI at warden, respectively. In particular, for the former one, the warden utilizes the likelihood ratio (LR) detector, while for the latter one, the generalized likelihood ratio (GLR) detector is adopted. We first consider the scenario where the blocklength is finite, and demonstrate that the covert rate under both cases asymptotically goes to zero as the blocklength goes to infinity. Subsequently, we take the noise uncertainty at the warden into account which leads to positive covert rate, and characterize the covert rate for infinite blocklength. Specially, we derive the optimal transmit power for the legitimate transmitter that maximizes the covert rate. Besides, the rate gap under two cases, with different CSI availability at the warden, can be presented in closed form. Finally, numerical results validate the effectiveness of our theoretical analysis and also demonstrate the impacts of the factors studied on the system covertness.

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.

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.

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.

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.

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.

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.

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.

Large number of antennas and higher bandwidth usage in massive multiple-input-multiple-output (MIMO) systems create immense burden on receiver in terms of higher power consumption. The power consumption at the receiver radio frequency (RF) circuits can be significantly reduced by the application of analog-to-digital converter (ADC) of low resolution. In this paper we investigate bandwidth efficiency (BE) of massive MIMO with perfect channel state information (CSI) by applying low resolution ADCs with Rician fadings. We start our analysis by deriving the additive quantization noise model, which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in radar. We also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency (BE) of the system. We emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing (RZF) combining algorithm. We also provide a generic analysis of energy efficiency (EE) with different options of bits by calculating the energy efficiencies (EE) using the achievable rates. We emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.

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.

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.

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.

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.

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.

Cognitive radar is a concept proposed by Simon Haykin in 2006 as a new generation of radar system that imitates human cognitive features. Different from the adaptive signal processing at the receiver in adaptive radar, the cognitive radar realizes closed-loop adaptive policy adjustment of both transmitter and receiver in the continuous interaction with the environment. As a networked radar may significantly enhance the flexibility and robustness than its monostatic counterpart, the wireless networked cognitive radar (WNCR) attracts increasing research. This article firstly reviews the concept and development of cognitive radar, especially the related researches of networked cognitive radar. Then, the co-design of cognitive radar and communication is investigated. Although the communication quality between radar sensing nodes is the premise of detection, tracking, imaging and anti-jamming performance of the WNCR, the latest researches seldom consider the communication architecture design for WNCR. Therefore, this article mainly focuses on the proposal of WNCR concept based on the researches of cognitive radar and analyzes research challenges of WNCR system in practical application, and the corresponding guidelines are proposed to inspire future research.

The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is regarded as a promising paradigm for enhancing the connectivity and reliability of non-orthogonal multiple access (NOMA) networks. However, the transmission of STAR-RIS enhanced NOMA networks performance is severely limited due to the inter-user interference (IUI) on multi-user detections. To mitigate this drawback, we propose a generalized quadrature spatial modulation (GQSM) aided STAR-RIS in conjunction with the NOMA scheme, termed STAR-RIS-NOMA-GQSM, to improve the performance of the corresponding NGMA network. By STAR-RIS-NOMA-GQSM, the information bits for all users in transmission and reflection zones are transmitted via orthogonal signal domains to eliminate the IUI so as to greatly improve the system performance. The low-complexity detection and upper-bounded bit error rate (BER) of STAR-RIS-NOMA-GQSM are both studied to evaluate its feasibility and performance. Moreover, by further utilizing index modulation (IM), we propose an enhanced STAR-RIS-NOMA-GQSM scheme, termed E-STAR-RIS-NOMA-GQSM, to enhance the transmission rate by dynamically adjusting reflection patterns in both transmission and reflection zones. Simulation results show that the proposed original and enhanced scheme significantly outperform the conventional STAR-RIS-NOMA and also confirm the precision of the theoretical analysis of the upper-bounded BER.

Recent deep neural network (DNN) based blind image quality assessment (BIQA) approaches take mean opinion score (MOS) as ground-truth labels, which would lead to cross-datasets biases and limited generalization ability of the DNN-based BIQA model. This work validates the natural instability of MOS through investigating the neuropsychological characteristics inside the human visual system during quality perception. By combining persistent homology analysis with electroencephalogram (EEG), the physiologically meaningful features of the brain responses to different distortion levels are extracted. The physiological features indicate that although volunteers view exactly the same image content, their EEG features are quite varied. Based on the physiological results, we advocate treating MOS as noisy labels and optimizing the DNN based BIQA model with early-stop strategies. Experimental results on both inner-dataset and cross-dataset demonstrate the superiority of our optimization approach in terms of generalization ability.

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.

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.

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.

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.

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.

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 mmWave massive multiple-input multiple-output (MIMO) communication systems, the extension of low-complexity narrowband precoding schemes to be operated on wideband systems under frequency-selective channels remains an important challenge at the current time. This paper investigates a low complexity wideband hybrid precoding scheme for mmWave massive MIMO multicarrier systems under a single-user, fully-connected hybrid architecture. We show that the radio frequency (RF) precoding/combining vectors can be directly derived from the eigenvectors of the optimal fully-digital covariance matrix over all subcarriers in order to maximize the sum rate of spectral efficiency. We also suggest a new method that iteratively reduces the residual error between the covariance matrix and the sum of products of precoding matrices over all the subcarriers to improve the performance in the case where the number of RF chains is higher than the number of streams. The results of the simulation show that the proposed schemes' complexity is low compared to the present methods, and their performance can almost reach the upper bound achieved by the optimal full-baseband design.

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 high-speed railway (HSR) wireless communication, the rapid channel changes and limited high-capacity access cause significant impact on the link performance. Meanwhile, the Doppler shift caused by high mobility leads to the inter-carrier interference. In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted receive spatial modulation (SM) scheme based on the spatial-temporal correlated HSR Rician channel. The characteristics of SM and the phase shift adjustment of RIS are used to mitigate the performance degradation in high mobility scenarios. Considering the influence of channel spatial-temporal correlation and Doppler shift, the effects of different parameters on average bit error rate (BER) performance and upper bound of ergodic capacity are analyzed. Therefore, a joint antenna and RIS-unit selection algorithm based on the antenna removal method is proposed to increase the capacity performance of communication links. Numerical results show that the proposed RIS-assisted receive SM scheme can maintain high transmission capacity compared to the conventional HSR-SM scheme, whereas the degradation of BER performance can be compensated by arranging a large number of RIS-units. In addition, selecting more RIS-units has better capacity performance than activating more antennas in the low signal-to-noise ratio regions.

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%.

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 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 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.

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).

Orthogonal time frequency space (OTFS) technique, which modulates data symbols in the delay-Doppler (DD) domain, presents a potential solution for supporting reliable information transmission in high-mobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing (UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model (HMM). The empirical state evolution (SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm, we derive the update criterion for the hyperparameters through the expectation-maximization (EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

Hybrid precoder design is a key technique providing better antenna gain and reduced hardware complexity in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. In this paper, Gaussian Mixture learned approximate message passing (GM-LAMP) network is presented for the design of optimal hybrid precoders suitable for mmWave Massive MIMO systems. Optimal hybrid precoder designs using a compressive sensing scheme such as orthogonal matching pursuit (OMP) and its derivatives results in high computational complexity when the dimensionality of the sparse signal is high. This drawback can be addressed using classical iterative algorithms such as approximate message passing (AMP), which has comparatively low computational complexity. The drawbacks of AMP algorithm are fixed shrinkage parameter and non-consideration of prior distribution of the hybrid precoders. In this paper, the fixed shrinkage parameter problem of the AMP algorithm is addressed using learned AMP (LAMP) network, and is further enhanced as GM-LAMP network using the concept of Gaussian Mixture distribution of the hybrid precoders. The simulation results show that the proposed GM-LAMP network achieves optimal hybrid precoder design with enhanced achievable rates, better accuracy and low computational complexity compared to the existing algorithms.

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.

In indoor environments, various battery-powered Internet of Things (IoT) devices, such as remote controllers and electronic tags on high-level shelves, require efficient energy management. However, manually monitoring remaining energy levels and battery replacement is both inadequate and costly. This paper introduces an energy management system for indoor IoT, which includes a mobile energy station (ES) for enabling on-demand wireless energy transfer (WET) in radio frequency (RF), some energy receivers (ERs), and a cloud server. By implementing a two-stage positioning system and embedding energy receivers into traditional IoT devices, we robustly manage their energy storage. The experimental results demonstrate that the energy receiver can harvest a minimum power of $58$ mW.

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.

The selection and coordinated application of government innovation policies are crucial for guiding the direction of enterprise innovation and unleashing their innovation potential. However, due to the lengthy, voluminous, complex, and unstructured nature of regional innovation policy texts, traditional policy classification methods often overlook the reality that these texts cover multiple policy topics, leading to lack of objectivity. In contrast, topic mining technology can handle large-scale textual data, overcoming challenges such as the abundance of policy content and difficulty in classification. Although topic models can partition numerous policy texts into topics, they cannot analyze the interplay among policy topics and the impact of policy topic coordination on enterprise innovation in detail. Therefore, we propose a big data analysis scheme for policy coordination paths based on the latent Dirichlet allocation (LDA) model and the fuzzy-set qualitative comparative analysis (fsQCA) method by combining topic models with qualitative comparative analysis. The LDA model was employed to derive the topic distribution of each document and the word distribution of each topic and enable automatic classification through algorithms, providing reliable and objective textual classification results. Subsequently, the fsQCA method was used to analyze the coordination paths and dynamic characteristics. Finally, experimental analysis was conducted using innovation policy text data from 31 provincial-level administrative regions in China from 2012 to 2021 as research samples. The results suggest that the proposed method effectively partitions innovation policy topics and analyzes the policy configuration, driving enterprise innovation in different regions.