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.

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.

A thin compact broadband coplanar-fed rectangular-ring monopole antenna parasitically-loaded by three nested concentric rectangle rings and a $\pi$-shaped stub is proposed suitable for modern communication needs. It has an overall area of only 25 mm$\times$6 mm (0.29$\lambda_{0}$$\times$0.07$\lambda_0$ at 3.5 GHz), which can be the base radiating element of the MIMO array, being easily integrated into any wireless device. Its measured (simulated) fractional bandwidth is 24.6% (31.6%) ranging from 3.25 (3.09) to 4.16 (4.25) GHz, being applicable to the 5G N48, N77, and N78 bands. Practical guidelines are also provided to make the proposed design operate on some other additional 5G bands (e.g., N41 or N46) without compromising its overall size. As far as the radiation properties are concerned, the antenna with such small dimensions radiates nearly bidirectionally and omnidirectionally in the $E$- and $H$-plane, respectively, and has an average measured (simulated) peak realized gain of -0.1 (1.8) dBi over the band of interest. The proposed antenna is wideband, physically small and relatively easy to manufacture, making it straightforward to integrate with the RF electronics in Io T sensors.

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.

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.

Blockchain-based spectrum sharing with consensus is the key technology for sixth-generation mobile communication to realize dynamic spectrum management. In order to avoid the waste of computing and communication resources, a spectrum sharing policy-based consensus mechanism is proposed in this paper. Firstly, a spectrum sharing algorithm based on graph neural network is designed in the satellite-terrestrial spectrum sharing networks under the underlay model. It avoids high computational overhead of the traditional iterative optimization algorithm when the wireless channel condition and network topology are highly dynamic. Secondly, a consensus mechanism based on spectrum sharing strategy is designed, which converts the traditional meaningless hash problem into the graph neural network training. Miners compete for accounting rights by training a graph neutral network model that meets the spectrum sharing requirement. Finally, the consensus delay, communication and storage overhead of the proposed consensus mechanism are analyzed theoretically. The simulation results show that the proposed consensus mechanism can effectively improve spectrum efficiency with excellent scalability and generalization performance.

This paper proposes a class of novel progressive edge growth-based codebooks for downlink sparse code multiple access (SCMA) systems. In the first scheme, we propose to progressively design the codebooks of each resource node (RN) instead of rotating a mother constellation (MC) as in the conventional SCMA works. In the other one, based on the MC, a multi-resources rotated codebooks are proposed to improve the performance of the superimposed constellations. The resultant codebooks are respectively referred to as the resource edge multi-dimensional codebooks (REMC) and the user edge multi-dimensional codebooks (UEMC). Additionally, we delve into the detailed design of the MC and the superimposed constellation. Then, we pay special attention to the application of the proposed schemes to challenging design cases, particularly for the high dimensional, high rate, and irregular codebooks, where the corresponding simplified schemes are proposed to reduce the complexity of codebook design. Finally, simulation results are presented to demonstrate the superiority of our progressive edge growth-based schemes. The numerical results indicate that the proposed codebooks significantly outperform the state-of-the-art codebooks. In addition, we also show that the proposed REMC codebooks outperform in the lower signal-to-noise ratio (SNR) regime, whereas the UEMC codebooks exhibit better performance at higher SNRs.

This paper investigates the performance of a simultaneous wireless information and power transfer (SWIPT) enabled two-way one-relay (TWOR) system based on generalized carrier index differential chaos shift keying (GCI-DCSK). We derive the expressions for the proposed scheme's bit error rate (BER), considering both additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels. The simulation results demonstrate that the proposed SWIPT-enabled GCI-DCSK TWOR system performs slightly worse than the DCSK TWOR system without SWIPT at the same distance. However, a notable advantage of the proposed system is that the relay node is self-sustainable, which is particularly significant considering the challenges associated with battery replacement in relay nodes. Furthermore, we derive the outage probability and validate the accuracy of the derived formulas using simulation results.

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

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

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

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.

Heavy routing overhead in Mobile Ad hoc Network (MANET) is a main bottleneck limiting the network performance. In this paper, we propose a novel Clustering OLSR (C-OLSR) approach, which utilizes two schemes, i.e., clustering and optimized Topology Control (TC) message transfer to reduce the control overhead of OLSR while guaranteeing its real-time requirement. To reduce the control overhead, in C-LOSR, flooding of TC messages is only limited in the cluster. All TC messages are integrated into a Cluster Topology Control (CTC) message by the cluster header and broadcast over the network. To satisfy the real-time requirement, any topology change will trigger CTC messages over the network. Extensive simulations have been done to evaluate the performance of the proposed C-OLSR. Results show that C-OLSR can achieve lower control overhead than OLSR by 44.32\% in static networks and by 23.21\% in dynamic networks.

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.

In the Internet of Things (IoT) based scenarios, the network may encounter significant issues related to energy and communication as a result of a progressively growing number of terminals. We introduce simultaneous wireless power transfer (SWIPT) technology assisted by a reconfigurable intelligent surface (RIS) to counteract this challenge. Thus, the network's flexibility and reliability will be further enhanced. According to this system architecture, the scattering-parameter-based communication model is introduced to disclose hardware features for an energy efficiency (EE) maximization problem. Specifically, the potential unauthorized demodulation is also considered in the problem formulation. To resolve the issue, an alternative strategy is utilized to optimize iteratively the coupled variables. In particular, the block coordinate descent (BCD) approach based on the Sherman-Morrison formula is proposed to solve the RIS subproblem. The numerical results prove the hardware effects cannot be dismissed lightly. Besides, the configuration of the RIS may impact the network performance directly.

Most blind image quality assessment (BIQA) methods require a large amount of time to collect human opinion scores as training labels, which limits their usability in practice. Thus, we present an opinion-unaware BIQA method based on deep reinforcement learning which is trained without subjective scores, named DRL-IQA. Inspired by the human visual perception process, our model is formulated as a quality reinforced agent, which consists of the dynamic distortion generation part and the quality perception part. By considering the image distortion degradation process as a sequential decision-making process, the dynamic distortion generation part can develop a strategy to add as many different distortions as possible to an image, which enriches the distortion space to alleviate overfitting. A reward function calculated from quality degradation after adding distortion is utilized to continuously optimize the strategy. Furthermore, the quality perception part can extract rich quality features from the quality degradation process without using subjective scores, and accurately predict the state values that represent the image quality. Experimental results reveal that our method achieves competitive quality prediction performance compared to other state-of-the-art BIQA methods.

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

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

Based on the objective reality of channel estimation error, this paper introduces a novel artificial noise (AN) aided spatial modulation (SM) secrecy-enhancing scheme under imperfect channel state information (CSI). In the proposed scheme, SM is used to activate one antenna from the transmit antennas, and the information symbols will be transmitted with the designed AN at each timeslot. By utilizing the legitimate channel's imperfect CSI, AN is generated across two adjacent timeslots. Because the CSI is known at the legitimate receiver, then it can perfectly cancel the AN. However, the eavesdropper knows nothing of the legitimate channel's CSI, so it can not recover any useful information from the AN. At the receiver, a new detection scheme that detects across two adjacent timeslots is also proposed. With imperfect CSI, the secrecy rate of the proposed scheme is derived over Rayleigh fading channels in order to investigate the performance. Moreover, based on the secrecy performance analysis, the lower bound of the ergodic secrecy rate (ESR), the corresponding closed form of the lower bound, and the approximated expression are also derived. The simulation results verified in this paper prove that the proposed scheme with imperfect CSI can achieve satisfactory performance.

In the Satellite-integrated Internet of Things (S-IoT), data freshness in the time-sensitive scenarios could not be guaranteed over the time-varying topology with current distribution strategies aiming to reduce the transmission delay. To address this problem, in this paper, we propose an age-optimal caching distribution mechanism for the high-timeliness data collection in S-IoT by adopting a freshness metric, as called age of information (AoI) through the caching-based single-source multi-destinations (SSMDs) transmission, namely Multi-AoI, with a well-designed cross-slot directed graph (CSG). With the proposed CSG, we make optimizations on the locations of cache nodes by solving a nonlinear integer programming problem on minimizing Multi-AoI. In particular, we put up forward three specific algorithms respectively for improving the Multi-AoI, i.e., the minimum queuing delay algorithm (MQDA) based on node deviation from average level, the minimum propagation delay algorithm (MPDA) based on the node propagation delay reduction, and a delay balanced algorithm (DBA) based on node deviation from average level and propagation delay reduction. The simulation results show that the proposed mechanism can effectively improve the freshness of information compared with the random selection algorithm.

Blockchain, as a distributed ledger, inherently possesses tamper-resistant capabilities, creating a natural channel for covert communication. However, the immutable nature of data storage might introduce challenges to communication security. This study introduces a blockchain-based covert communication model utilizing dynamic Base-K encoding. The proposed encoding scheme utilizes the input address sequence to determine K to encode the secret message and determines the order of transactions based on K, thus ensuring effective concealment of the message. The dynamic encoding parameters enhance flexibility and address issues related to identical transaction amounts for the same secret message. Experimental results demonstrate that the proposed method maintains smooth communication and low susceptibility to tampering, achieving commendable concealment and embedding rates.

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