An Efficient and flexible implementation of block ciphers is critical to achieve information security processing. Existing implementation methods such as GPP, FPGA and cryptographic application-specific ASIC provide the broad range of support. However, these methods could not achieve a good tradeoff between high-speed processing and flexibility. In this paper, we present a reconfigurable VLIW processor architecture targeted at block cipher processing, analyze basic operations and storage characteristics, and propose the multi-cluster register-file structure for block ciphers. As for the same operation element of block ciphers, we adopt reconfigurable technology for multiple cryptographic processing units and interconnection scheme. The proposed processor not only flexibly accomplishes the combination of multiple basic cryptographic operations, but also realizes dynamic configuration for cryptographic processing units. It has been implemented with 0.18µmCMOS technology, the test results show that the frequency can reach 350MHz, and power consumption is 420mw. Ten kinds of block and hash ciphers were realized in the processor. The encryption throughput of AES, DES, IDEA, and SHA-1 algorithm is 1554Mbps, 448Mbps, 785Mbps, and 424Mbps respectively, the test result shows that our processor’s encryption performance is significantly higher than other designs.

This paper analyzes the threat of TCG Software Stack (TSS)/TCM Service Module (TSM) deadlock in multi-user environment such as cloud and discusses its causes and mechanism. In addition, this paper puts forward a dynamic priority task scheduling strategy based on value evaluation to handle this threat. The strategy is based on the implementation features of trusted hardware and establishes a multi-level ready queue. In this strategy, an algorithm for real-time value computing is also designed, and it can adjust the production curves of the real time value by setting parameters in different environment, thus enhancing its adaptability, which is followed by scheduling and algorithm description. This paper also implements the algorithm and carries out its performance optimization. Due to the experiment result from Intel NUC, it is shown that TSS based on advanced DPTSV is able to solve the problem of deadlock with no negative influence on performance and security in multi-user environment.

With the exponential growth of the data traffic in wireless communication systems, terahertz (THz) frequency band is envisioned as a promising candidate to support ultra- broadband for future beyond fifth generation (5G), bridging the gap between millimeter wave (mmWave) and optical frequency ranges. The purpose of this paper is to provide a comprehensive literature review on the development towards THz communications and presents some key technologies faced in THz wireless communication systems. Firstly, despite the substantial hardware problems that have to be developed in terms of the THz solid state superheterodyne receiver, high speed THz modulators and THz antennas, the practical THz channel model and the efficient THz beamforming are also described to compensate for the severe path attenuation. Moreover, two different kinds of lab-level THz communication systems are introduced minutely, named a solid state THz communication system and a spatial direct modulation THz communication system, respectively. The solid state THz system converts intermediate frequency (IF) modulated signal to THz frequency while the direct modulation THz system allows the high power THz sources to input for approving the relatively long distance communications. Finally, we discuss several potential application scenarios as well as some vital technical challenges that will be encountered in the future THz communications.

Massive MIMO is one of the key technologies in future 5G communications which can satisfy the requirement of high speed and large capacity. This paper considers antenna selection and power allocation design to promote energy conservation then provide good quality of service (QoS) for the whole massive MIMO uplink network. Unlike previous related works, hardware impairment, transmission efficiency, and energy consumption at the circuit and antennas are involved in massive MIMO networks. In order to ensure the QoS, we consider the minimum rate constraint for each user and the system, which increases the complexity of power allocation problem for maximizing energy and spectral efficiency in massive MIMO system. To this end, a quantum-inspired social emotional optimization (QSEO) algorithm is proposed to obtain the optimal power control strategy in massive MIMO uplink networks. Simulation results assess the great advantages of QSEO which previous strategies do not have.

with the development of 5G, the future wireless communication network tends to be more and more intelligent. In the face of new service demands of communication in the future such as super-heterogeneous network, multiple communication scenarios, large number of antenna elements and large bandwidth, new theories and technologies of intelligent communication have been widely studied, among which Deep Learning (DL) is a powerful technology in artificial intelligence(AI). It can be trained to continuously learn to update the optimal parameters. This paper reviews the latest research progress of DL in intelligent communication, and emphatically introduces five scenarios including Cognitive Radio (CR), Edge Computing (EC), Channel Measurement (CM), End to end Encoder/Decoder (EED) and Visible Light Communication (VLC). The prospect and challenges of further research and development in the future are also discussed.

Terahertz (THz) communications are envisioned as a key technology for the sixth-generation wireless communication system (6G). However, it is not practical to perform large-scale channel measurements with high degrees of freedom at THz frequency band. This makes empirical or stochastic modeling approaches relying on measurements no longer stand. In order to break through the bottleneck of scarce full-dimensional channel sounding measurements, this paper presents a novel paradigm for THz channel modeling towards 6G. With the core of high-performance ray tracing (RT), the presented paradigm requires merely quite limited channel sounding to calibrate the geometry and material electromagnetic (EM) properties of the three-dimensional (3D) environment model in the target scenarios. Then, through extensive RT simulations, the parameters extracted from RT simulations can be fed into either ray-based novel stochastic channel models or cluster-based standard channel model families. Verified by RT simulations, these models can generate realistic channels that are valuable for the design and evaluation of THz systems. Representing two ends of 6G THz use cases from microscopy to macroscopy, case studies are made for close-proximity communications, wireless connections on a desktop, and smart rail mobility, respectively. Last but not least, new concerns on channel modeling resulting from distinguishing features of THz wave are discussed regarding propagation, antenna array, and device aspects, respectively.

The emergence of backdoor attacks poses a serious security threat for radio signal modulation recognition. In the training phase, the backdoor can be implanted into Deep Neural Network (DNN) through adding malicious poisoned samples, and the poisoned model will produce false predictions with specified samples. In this paper, for backdoor attacks in radio signals field, we propose a novel defense method, which can remove the poisoned samples and repair the model based on feature fusion and clustering analysis. First, the Signal Classification (SC) features and the Instantaneous Frequency (IF) features of samples are obtained and fused, the fused features are performed clustering analysis to determine the poisoned category using clustering metrics and Median Absolute Deviation (MAD). Then the poisoned samples in the poisoned category are identified with the key feature values calculated based on the dimensionality reduction results of SC features. Finally, the poisoned samples are removed and a clean model is retrained using the remaining clean data. The experimental results against two backdoor attacks on two benchmark datasets demonstrate that our proposed method greatly reduces the attack success rate of backdoor attacks while ensuring no loss of the model accuracy.

Copy-Move Forgery (CMF) is one of the simple and effective operations to create forged digital images. Recently, techniques based on Scale Invariant Features Transform (SIFT) are widely used to detect CMF. Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance. However, for some CMF images, these approaches cannot produce satisfactory detection results. For instance, the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result. Sometimes these approaches may even produce error results. According to our observations, one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences. These values are only applicable to a few images, which limits their application. To solve the problem, a novel approach named as CMF Detection with Particle Swarm Optimization (CMFD-PSO) is proposed in this paper. CMFD-PSO integrates the Particle Swarm Optimization (PSO) algorithm into the SIFT-based framework. It utilizes the PSO algorithm to generate customized parameter values for images, which are used for CMF detection under the SIFT-based framework. Experimental results show that CMFD-PSO has good performance.

The interplay between artificial intelligence (AI) and fog radio access networks (F-RANs) is investigated in this work from two perspectives: how F-RANs enable hierarchical AI to be deployed in wireless networks and how AI makes F-RANs smarter to better serve mobile devices. Due to the heterogeneity of processing capability, the cloud, fog, and device layers in F-RANs provide hierarchical intelligence via centralized, distributed, and federated learning. In addition, cross-layer learning is also introduced to further reduce the demand for the memory size of the mobile devices. On the other hand, AI provides F-RANs with technologies and methods to deal with massive data and make smarter decisions. Specifically, machine learning tools such as deep neural networks are introduced for data processing, while reinforcement learning (RL) algorithms are adopted for network optimization and decisions. Then, two examples of AI-based applications in F-RANs, i.e., health monitoring and intelligent transportation systems, are presented, followed by a case study of an RL-based caching application in the presence of spatio-temporal unknown content popularity to showcase the potential of applying AI to F-RANs.

With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation (6G) mobile communications be on the eve of the fifth-generation (5G) deployment. To pave the way for the development of 6G and beyond, we provide 6G visions in this paper. We first introduce the state-of-the-art technologies in 5G and indicate the necessity to study 6G. By taking the current and emerging development of wireless communications into consideration, we envision 6G to include three major aspects, namely, mobile ultra-broadband, super Internet-of-Things (IoT), and artificial intelligence (AI). Then, we review key technologies to realize each aspect. In particular, teraherz (THz) communications can be used to support mobile ultra-broadband, symbiotic radio and satellite-assisted communications can be used to achieve super IoT, and machine learning techniques are promising candidates for AI. For each technology, we provide the basic principle, key challenges, and state-of-the-art approaches and solutions.

High frequency (HF) communication, commonly covering frequency range between 3 and 30 MHz, is an important wireless communication paradigm to offer over-the-horizon or even global communications with ranges up to thousands of kilometers via skywave propagation with ionospheric refraction. It has widespread applications in fields such as emergency communications in disaster areas, remote communications with aircrafts or ships and non-light-of-the-sight military operations. This tutorial article overviews the history of HF communication, demystifies the recent advances, and provides a preview of the next few years, which the authors believe will see fruitful outputs towards wideband, intelligent and integrated HF communications. Specifically, we first present brief preliminaries on the unique features of HF communications to facilitate general readers in the communication community. Then, we provide a historical review to show the technical evolution on the three generations of HF communication systems. Further, we highlight the key challenges and research directions. We hope that this article will stimulate more interests in addressing the technical challenges on the research and development of future HF radio communication systems.

One key advantage of 4G OFDM system is the relatively simple receiver implementation due to the orthogonal resource allocation. However, from sum-capacity and spectral efficiency points of view, orthogonal systems are never the achieving schemes. With the rapid development of mobile communication systems, a novel concept of non-orthogonal transmission for 5G mobile communications has attracted researches all around the world. In this trend, many new multiple access schemes and waveform modulation technologies were proposed. In this paper, some promising ones of them were discussed which include Non-orthogonal Multiple Access (NOMA), Sparse Code Multiple Access (SCMA), Multi-user Shared Access (MUSA), Pattern Division Multiple Access (PDMA) and some main new waveforms including Filter-bank based Multicarrier (FBMC), Universal Filtered Multi-Carrier (UFMC), Generalized Frequency Division Multiplexing (GFDM). By analyzing and comparing features of these technologies, a research direction of guiding on future 5G multiple access and waveform are given.

In 6G era, service forms in which computing power acts as the core will be ubiquitous in the network. At the same time, the collaboration among edge computing, cloud computing and network is needed to support edge computing service with strong demand for computing power, so as to realize the optimization of resource utilization. Based on this, the article discusses the research background, key techniques and main application scenarios of computing power network. Through the demonstration, it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing, storage and network, and adapt to the integration needs of computing power and network in various scenarios, such as user oriented, government enterprise oriented, computing power open and so on.

Joint radar and communication (JRC) technology has become important for civil and military applications for decades. This paper introduces the concepts, characteristics and advantages of JRC technology, presenting the typical applications that have benefited from JRC technology currently and in the future. This paper explores the state-of-the-art of JRC in the levels of coexistence, cooperation, co-design and collaboration. Compared to previous surveys, this paper reviews the entire trends that drive the development of radar sensing and wireless communication using JRC. Specifically, we explore an open research issue on radar and communication operating with mutual benefits based on collaboration, which represents the fourth stage of JRC evolution. This paper provides useful perspectives for future researches of JRC technology.

Network virtualization (NV) is pushed forward by its proponents as a crucial attribute of next generation network, aiming at overcoming the gradual ossification of current networks, particularly to the worldwide Internet. Through virtualization, multiple customized virtual networks (VNs), requested by users, are allowed to coexist on the underlying substrate networks (SNs). In addition, the virtualization scheme contributes to sharing underlying physical resources simultaneously and seamlessly. However, multiple technical issues still stand in the way of NV successful implementation. One key technical issue is virtual network embedding (VNE), known as the resource allocation problem for NV. This paper conducts a survey of embedding algorithms for VNE problem. At first, the NV business model for VNE problem is presented. Then, the latest VNE problem description is presented. Main performance metrics for evaluating embedding algorithms are also involved. Afterwards, existing VNE algorithms are detailed, according to the novel proposed category approach. Next, key future research aspects of embedding algorithms are listed out. Finally, the paper is briefly concluded.

In recent years, autonomous driving technology has made good progress, but the non-cooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges. V2I (Vehicle-to-Infrastructure) communication is a potential solution to enable cooperative intelligence of vehicles and roads. In this paper, the RGB-PVRCNN, an environment perception framework, is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication technology. This framework integrates vision feature based on PVRCNN. The normal distributions transform(NDT) point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the intersection. The field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy.

Fuzzing is an effective technique to find security bugs in programs by quickly exploring the input space of programs. To further discover vulnerabilities hidden in deep execution paths, the hybrid fuzzing combines fuzzing and concolic execution for going through complex branch conditions. In general, we observe that the execution path which comes across more and complex basic blocks may have a higher chance of containing a security bug. Based on this observation, we propose a hybrid fuzzing method assisted by static analysis for binary programs. The basic idea of our method is to prioritize seed inputs according to the complexity of their associated execution paths. For this purpose, we utilize static analysis to evaluate the complexity of each basic block and employ the hardware trace mechanism to dynamically extract the execution path for calculating the seed inputs' weights. The key advantage of our method is that our system can test binary programs efficiently by using the hardware trace and hybrid fuzzing. To evaluate the effectiveness of our method, we design and implement a prototype system, namely SHFuzz. The evaluation results show SHFuzz discovers more unique crashes on several real-world applications and the LAVA-M dataset when compared to the previous solutions.

With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issue. In this paper, we present an access control system with privilege separation based on privacy protection (PS-ACS). In the PS-ACS scheme, we divide users into private domain (PRD) and public domain (PUD) logically. In PRD, to achieve read access permission and write access permission, we adopt the Key-Aggregate Encryption (KAE) and the Improved Attribute-based Signature (IABS) respectively. In PUD, we construct a new multi-authority ciphertext policy attribute-based encryption (CP-ABE) scheme with efficient decryption to avoid the issues of single point of failure and complicated key distribution, and design an efficient attribute revocation method for it. The analysis and simulation result show that our scheme is feasible and superior to protect users’ privacy in cloud-based services.

Ultra-Wide Bandwidth (UWB) localization based on time of arrival (TOA) and angle of arrival (AOA) has attracted increasing interest owing to its high accuracy and low cost. However, existing localization methods often fail to achieve satisfactory accuracy in realistic environments due to multipath effects and non-line-of-sight (NLOS) propagation. In this paper, we propose a passive anchor assisted localization (PAAL) scheme, where the active anchor obtains TOA/AOA measurements to the agent while the passive anchors capture the signals from the active anchor and agent. The proposed method fully exploits the time-difference-of-arrival (TDOA) information from the measurements at the passive anchors to complement single-anchor joint TOA/AOA localization. The performance limits of the PAAL system are derived as a benchmark via the information inequality. Moreover, we implement the PAAL system on a low-cost UWB platform, which can achieve 20 cm localization accuracy in NLOS environments.

The terahertz (THz) antennas, which have features of small size, wide frequency bandwidth and high data rate, are important devices for transmitting and receiving THz electromagnetic waves in the emerging THz systems. However, most of THz antennas suffer from relatively high loss and low fabrication precision due to their small sizes in high frequency bands of THz waves. Therefore, this paper presents a detailed overview of the most recent research on the performance improvement of THz antennas. Firstly, the development of THz antennas is briefly reviewed and the basic design ideas of THz antennas are introduced. Then, THz antennas are categorized as metallic antennas, dielectric antennas and new material antennas. After that, the latest research progress in THz photoconductive antennas, THz horn antennas, THz lens antennas, THz microstrip antennas and THz on-chip antennas are discussed. In particular, the practical difficulties for the development of THz antennas are discussed with promising approaches. In addition, this paper also presents a short review of the process technology of THz antennas. Finally, the vital challenges and the future research directions for THz antennas are presented.

Large intelligent surface (LIS) is considered as a new solution to enhance the performance of wireless networks[1]. LIS comprises low-cost passive elements which can be well controlled. In this paper, a LIS is invoked in the vehicular networks. We analyze the system performance under Weibull fading. We derive a novel exact analytical expression for outage probability in closed form. Based on the analytical result, we discuss three special scenarios including high SNR case, low SNR case, as well as weak interference case. The corresponding approximations for three cases are provided, respectively. In order to gain more insights, we obtain the diversity order of outage probability and it is proved that the outage probability at high SNR depends on the interference, threshold and fading parameters which leads to 0 diversity order. Furthermore, we investigate the ergodic achievable rate of LIS-assisted vehicular networks and present the closed-form tight bounds. Similar to the outage performance, three special cases are studied and the asymptotic expressions are provided in simple forms. A rate ceiling is shown for high SNRs due to the existence of interference which results 0 high SNR slope. Finally, we give the energy efficiency of LIS-assisted vehicular network. Numerical results are presented to verify the accuracy of our analysis. It is evident that the performance of LIS-assisted vehicular networks with optimal phase shift scheme exceeds that of traditional vehicular networks and random phase shift scheme significantly.

Mobile communication standards have been developed into a new era of B5G and 6G. In recent years, low earth orbit (LEO) satellites and space Internet have become hot topics. The integrated satellite and terrestrial systems have been widely discussed by industries and academics, and even are expected to be applied in those huge constellations in construction. This paper points out the trends of two stages towards system integration of the terrestrial mobile communication and the satellite communications: to be compatible with 5G, and to be integrated within 6G. Based on analysis of the challenges of both stages, key technologies are thereafter analyzed in detail, covering both air interface currently discussed in 3GPP for B5G and also novel network architecture and related transmission technologies toward future 6G.

Sentiment Analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get information about the behavioral state of people (opinion) through reviews and comments. Numerous techniques have been aimed to analyze the sentiment of the text, however, they were unable to come up to the complexity of the sentiments. The complexity requires novel approach for deep analysis of sentiments for more accurate prediction. This research presents a three-step Sentiment Analysis and Prediction (SAP) solution of Text Trend through K-Nearest Neighbor (KNN). At first, sentences are transformed into tokens and stop words are removed. Secondly, polarity of the sentence, paragraph and text is calculated through contributing weighted words, intensity clauses and sentiment shifters. The resulting features extracted in this step played significant role to improve the results. Finally, the trend of the input text has been predicted using KNN classifier based on extracted features. The training and testing of the model has been performed on publically available datasets of twitter and movie reviews. Experiments results illustrated the satisfactory improvement as compared to existing solutions. In addition, GUI (Hello World) based text analysis framework has been designed to perform the text analytics.

In this paper, we present an approach to improve the accuracy of environmental sound event detection in a wireless acoustic sensor network for home monitoring. Wireless acoustic sensor nodes can capture sounds in the home and simultaneously deliver them to a sink node for sound event detection. The proposed approach is mainly composed of three modules, including signal estimation, reliable sensor channel selection, and sound event detection. During signal estimation, lost packets are recovered to improve the signal quality. Next, reliable channels are selected using a multi-channel cross-correlation coefficient to improve the computational efficiency for distant sound event detection without sacrificing performance. Finally, the signals of the selected two channels are used for environmental sound event detection based on bidirectional gated recurrent neural networks using two-channel audio features. Experiments show that the proposed approach achieves superior performances compared to the baseline.

In this paper, we propose a Packet Cache-Forward (PCF) method based on improved Bayesian outlier detection to eliminate out-of-order packets caused by transmission path drastically degradation during handover events in the moving satellite networks, for improving the performance of TCP. The proposed method uses an access node satellite to cache all received packets in a short time when handover occurs and forward them out in order. To calculate the cache time accurately, this paper establishes the Bayesian based mixture model for detecting delay outliers of the entire handover scheme. In view of the outliers’ misjudgment, an updated classification threshold and the sliding window has been suggested to correct category collections and model parameters for the purpose of quickly identifying exact compensation delay in the varied network load statuses. Simulation shows that, comparing to average processing delay detection method, the average accuracy rate was scaled up by about 4.0%, and there is about 5.5% cut in error rate in the meantime. It also behaves well even though testing with big dataset. Benefiting from the advantage of the proposed scheme in terms of performance, comparing to conventional independent handover and network controlled synchronized handover in simulated LEO satellite networks, the proposed independent handover with PCF eliminates packet out-of-order issue to get better improvement on congestion window. Eventually the average delay decreases more than 70% and TCP performance has improved more than 300%.

By leveraging the 5G enabled vehicular ad hoc network (5G-VANET), it is widely recognized that connected vehicles have the potentials to improve road safety, transportation intelligence and provide in-vehicle entertainment experience. However, many enabling applications in 5G-VANET rely on the efficient content sharing among mobile vehicles, which is a very challenging issue due to the extremely large data volume, rapid topology change, and unbalanced traffic. In this paper, we investigate content prefetching and distribution in 5G-VANET. We first introduce an edge computing based hierarchical architecture for efficient distribution of large-volume vehicular data. We then propose a multi-place multi-factor prefetching scheme to meet the rapid topology change and unbalanced traffic. The content requests of vehicles can be served by neighbors, which can improve the sharing efficiency and alleviate the burden of networks. Furthermore, we use a graph theory based approach to solve the content distribution by transforming it into a maximum weighted independent set problem. Finally, the proposed scheme is evaluated with a greedy transmission strategy to demonstrate its efficiency.

As a development direction of urban rail transit system, the train autonomous circumambulate system (TACS) can operate in a safer, more efficient, and more economical mode. However, most urban rail transit systems transmit signals through industrial, scientific, and medical (ISM) frequency bands or narrow frequency bands, which cannot meet the requirements of TACS. As a promising solution, the 5th generation (5G) mobile communication provides more services for the future urban rail transit systems, and covers the shortages of exiting communication technologies in terms of capacity and reliability. In this paper, we first briefly review the research status of current train control system and introduce its limitations. Next, we propose a novel network architecture, and present new technologies and requirements of the proposed architecture for TACS. Some potential challenges are then discussed to give insights for further research of TACS.

The theory and experiment of quantum information have been studied extensively in recent years, and the feasibility of quantum communication has been proved. Although the fundamental technology is not yet mature, research on quantum internet should be conducted. To implement quantum internet, an architecture that describes how quantum nodes are linked to form networks and how protocol functions are vertically composed need to be developed urgently. In this paper, we present a novel design of a cluster-based structure to describe how quantum nodes are interconnected, and how the structure can improve the performance of qubit transmission and reduce the network complexity. The idea of the quantum local area network (QLAN) is proposed as an essential component of the quantum internet. Besides, each quantum repeater links to neighboring repeaters to form a core network, and multiple QLANs are connected through the core network. The core network can be grouped into different hierarchical quantum repeater networks according to needed service requirements. For the sake of interoperability and fast prototyping, we adopt the idea of OSI layering model of the current Internet in the design of quantum internet. Finally, we elaborate on the composition of quantum nodes and the realization of end-to-end communication.

With the popularity of the internet, users hope to better protect their privacy while obtaining network services. However, in the traditional centralized authentication scheme, identity information such as the user's private key is generated, stored, and managed by the network operator. Users can’t control their identity information, which will lead to a great threat to the privacy of users. Based on redactable blockchain, we propose a fine-grained and fair identity authentication scheme for mobile networks. In our proposed scheme, the user’s identity information is generated and controlled by the users. We first propose a notion of score chameleon hash (SCH), which can delete or update the information of illegal users so as to dynamically update the status of users and provide users with more fine-grained and fair services. We propose another notion of self-updating secret sharing (SUSS), which allows users to update the trapdoor and the corresponding hash key after redacting the blockchain without requiring trusted authority to redistribute the trapdoor. Experimental results show that, compared with the immutable blockchain Bitcoin, the redactable blockchain in our identity authentication scheme provides users with fine-grained and fair redacting functions, and can be adopted with a small additional overhead.

Due to its high mobility and flexible deployment, unmanned aerial vehicle (UAV) is drawing unprecedented interest in both military and civil applications to enable agile and ubiquitous connectivity. Mainly operating in an open environment, UAV communications benefit from dominant line-of-sight links; however, this on the other hand renders the communications more vulnerable to malicious attacks. Recently, physical layer security (PLS) has been introduced to UAV systems as an important complement to the conventional cryptography-based approaches. In this paper, a comprehensive survey on the current achievements of UAV-PLS is conducted. We first introduce the basic concepts including typical static/mobile UAV deployment scenarios, the unique air-to-ground channel and aerial nodes distribution models, as well as various roles that a UAV may act when PLS is concerned. Then, we start by reviewing the secrecy performance analysis and enhancing techniques for statically deployed UAV systems, and extend the discussion to the more general scenario where the UAVs' mobility is further exploited. For both cases, respectively, we summarize the commonly adopted methodologies, then describe important works in the literature in detail. Finally, potential research directions and challenges are discussed to provide an outlook for future works in the area of UAV-PLS.

In this paper, we exploit clustered interference alignment (IA) for efficient subchannel allocation in ultra-dense orthogonal frequency division multiplexing access (OFDMA) based femtocell networks, which notably improves the spectral efficiency as well as addresses the feasibility issue of IA. Our problem is formulated as a combinatorial optimization problem which is NP-hard. To avoid obtaining its optimal solution by exhaustive search, we propose a two-phases efficient solution with low-complexity. The first phase groups all the femtocell user equipments (FUEs) into disjoint clusters, and the second phase allocates subchannels to the formed clusters where IA is performed. By doing this, the intra-cluster and inter-cluster interferences are mitigated by clustered IA and subchannel allocation in ultra-dense femtocell networks, respectively. Also, low-complexity algorithm is proposed to solve the corresponding sub-problem in each phase. Simulation results demonstrate that the proposed scheme not only outperforms other related schemes, but also provides a close performance to the optimal solution.

Edge intelligence is anticipated to underlay the pathway to connected intelligence for 6G networks, but the organic confluence of edge computing and artificial intelligence still needs to be carefully treated. To this end, this article discusses the concepts of edge intelligence from the semantic cognitive perspective. Two instructive theoretical models for edge semantic cognitive intelligence (ESCI) are first established. Afterwards, the ESCI framework orchestrating deep learning with semantic communication is discussed. Two representative applications are present to shed light on the prospect of ESCI in 6G networks. Some open problems are finally listed to elicit the future research directions of ESCI.

Processors have been playing important roles in both communication infrastructure systems and terminals. In this paper, both application specific and general purpose processors for communications are discussed including the roles, the history, the current situations, and the trends. One trend is that ASIPs (Application Specific Instruction-set Processors) are taking over ASICs (Application Specific Integrated Circuits) because of the increasing needs both on performance and compatibility of multi-modes. The trend opened opportunities for researchers crossing the boundary between communications and computer architecture. Another trend is the serverlization, i.e., more infrastructure equipments are replaced by servers. The trend opened opportunities for researchers working towards high performance computing for communication, such as research on communication algorithm kernels and real time programming methods on servers.

Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks. The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries. In addition, Orthogonal Multiple Access(OMA)technique cannot utilize limited spectrum resources fully and efficiently. Therefore, Non-Orthogonal Multiple Access(NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important, especially in highly-dynamic vehicular edge computing networks. The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers. Self-Imitation Learning(SIL)-based Deep Reinforcement Learning(DRL)has emerged as a promising machine learning technique to break through obstacles in various research fields, especially in time-varying networks. In this paper, we first introduce related MEC technologies in vehicular networks. Then, we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL, with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers. Numerical results demonstrate that the proposed algorithm outperforms other methods.

In the 6G era, Space-Air-Ground Integrated Network (SAGIN) are anticipated to deliver global coverage, necessitating support for a diverse array of emerging applications in high-mobility, hostile environments. Under such conditions, conventional orthogonal frequency division multiplexing (OFDM) modulation, widely employed in cellular and Wi-Fi communication systems, experiences performance degradation due to significant Doppler shifts. To overcome this obstacle, a novel two-dimensional (2D) modulation approach, namely orthogonal time frequency space (OTFS), has emerged as a key enabler for future high-mobility use cases. Distinctively, OTFS modulates information within the delay-Doppler (DD) domain, as opposed to the time-frequency (TF) domain utilized by OFDM. This offers advantages such as Doppler and delay resilience, reduced signaling latency, a lower peak-to-average ratio (PAPR), and a reduced-complexity implementation. Recent studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications (ISAC). In this article, we present an in-depth review of OTFS technology in the context of the 6G era, encompassing fundamentals, recent advancements, and future directions. Our objective is to provide a helpful resource for researchers engaged in the field of OTFS.

Since orthogonal time-frequency space (OTFS) can effectively handle the problems caused by Doppler effect in high-mobility environment, it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication. However, the inter-Doppler interference (IDI) problem caused by fractional Doppler poses great challenges to channel estimation. To avoid this problem, this paper proposes a joint time and delay-Doppler (DD) domain based on sparse Bayesian learning (SBL) channel estimation algorithm. Firstly, we derive the original channel response (OCR) from the time domain channel impulse response (CIR), which can reflect the channel variation during one OTFS symbol. Compare with the traditional channel model, the OCR can avoid the IDI problem. After that, the dimension of OCR is reduced by using the basis expansion model (BEM) and the relationship between the time and DD domain channel model, so that we have turned the underdetermined problem into an overdetermined problem. Finally, in terms of sparsity of channel in delay domain, SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel. The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.

Coordinated signal processing can obtain a huge transmission gain for Fog Radio Access Networks (F-RANs). However, integrating into large scale, it will lead to high computation complexity in channel estimation and spectral efficiency loss in transmission performance. Thus, a joint cluster formation and channel estimation scheme is proposed in this paper. Considering research remote radio heads (RRHs) centred serving scheme, a coalition game is formulated in order to maximize the spectral efficiency of cooperative RRHs under the conditions of balancing the data rate and the cost of channel estimation. As the cost influences to the necessary consumption of training length and estimation error. Particularly, an iterative semi-blind channel estimation and symbol detection approach is designed by expectation maximization algorithm, where the channel estimation process is initialized by subspace method with lower pilot length. Finally, the simulation results show that a stable cluster formation is established by our proposed coalition game method and it outperforms compared with full coordinated schemes.

This paper presents a ZUC-256 stream cipher algorithm hardware system in order to prevent the advanced security threats for 5G wireless network. The main innovation of the hardware system is that a six-stage pipeline scheme comprised of initialization and work stage is employed to enhance the solving speed of the critical logical paths. Moreover, the pipeline scheme adopts a novel optimized hardware structure to fast complete the Mod($2^{31}$-1) calculation. The function of the hardware system has been validated experimentally in detail. The hardware system shows great superiorities. Compared with the same type system in recent literatures, the logic delay reduces by 47% with an additional hardware resources of only 4 multiplexers, the throughput rate reaches 5.26 Gbps and yields at least 45% better performance, the throughput rate per unit area increases 14.8%. The hardware system provides a faster and safer encryption module for the 5G wireless network.

With the increasing maritime activities, a great demand of wide-area maritime digital data services is needed. Therefore, Narrowband Internet of Things (NB-IoT) that can provide wide coverage has been expected as an application for maritime communication networks (MCNS). In this paper, we aim to enhance the spectral efficiency in NB-IoT by reducing the cyclic prefix (CP) overhead in random access signal without causing interference. The key point of the proposed scheme is the symbols transmitted for multiple times repeatedly in NB-IoT. Specifically, all CP are removed and multi-path fading effect is eliminated by using a repeated symbol to cover the disturbed symbol to construct a circular convolution structure of the channel with the same effect as adding CP. In addition, a single-tap equalization is still appropriate. To validate the effectiveness of the proposed scheme, simulation results are carried out with respect to the bit error ratio (BER).

This paper investigates the problem of data scarcity in spectrum prediction. A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes. The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency. Moreover, the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very limited. To address the above issues, we develop a cross-band data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network (GAN) and deep transfer learning. Firstly, through the similarity measurement, we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency band. Then, through the data augmentation by feeding the small amount of the target data into the pre-trained GAN, temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from GAN. Finally, experiment results demonstrate the effectiveness of the proposed framework.