Communicating on millimeter wave (mmWave) bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission. However, mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles. To overcome these challenges, highly directional beams are exploited to achieve robust links by hybrid beamforming. Accurately aligning the transmitter and receiver beams, i.e. beam training, is vitally important to high data rate transmission. However, it may cause huge overhead which has negative effects on initial access, handover, and tracking. Besides, the mobility patterns of users are complicated and dynamic, which may cause tracking error and large tracking latency. An efficient beam tracking method has a positive effect on sustaining robust links. This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation (6G) mobile network. Especially, some open research problems are proposed to realize fast, accurate, and robust beam training and tracking. We hope that this survey provides guidelines for the researchers in the area of mmWave communications.

In this paper, we propose an active reconfigurable intelligent surface (RIS) enabled hybrid relaying scheme for a multi-antenna wireless powered communication network (WPCN), where the active RIS is employed to assist both wireless energy transfer (WET) from the power station (PS) to energy-constrained users and wireless information transmission (WIT) from users to the receiving station (RS). For further performance enhancement, we propose to employ both transmit beamforming at the PS and receive beamforming at the RS. We formulate a sum-rate maximization problem by jointly optimizing the RIS phase shifts and amplitude reflection coefficients for both the WET and the WIT, transmit and receive beamforming vectors, and network resource allocation. To solve this non-convex problem, we propose an efficient alternating optimization algorithm with the linear minimum mean squared error criterion, semi-definite relaxation (SDR) and successive convex approximation techniques. Specifically, the tightness of applying the SDR is proved. Simulation results demonstrate that our proposed scheme with 10 reflecting elements (REs) and 4 antennas can achieve 17.78% and 415.48% performance gains compared to the single-antenna scheme with 10 REs and passive RIS scheme with 100 REs, respectively.

With the rapid development of smart phone, the location-based services (LBS) have received great attention in the past decades. Owing to the widespread use of WiFi and Bluetooth devices, Received Signal Strength Indication (RSSI) fingerprint-based localization method has obtained much development in both academia and industries. In this work, we introduce an efficient way to reduce the labor-intensive site survey process, which uses an UWB/IMU-assisted fingerprint construction (UAFC) and localization framework based on the principle of Automatic radio map generation scheme (ARMGS) is proposed to replace the traditional manual measurement. To be specific, UWB devices are employed to estimate the coordinates when the collector is moved in a reference point (RP). An anchor self-localization method is investigated to further reduce manual measurement work in a wide and complex environment, which is also a grueling, time-consuming process that is lead to artificial errors. Moreover, the measurements of IMU are incorporated into the UWB localization algorithm and improve the label accuracy in fingerprint. In addition, the weighted k-nearest neighbor (WKNN) algorithm is applied to online localization phase. Finally, filed experiments are carried out and the results confirm the effectiveness of the proposed approach.

In this paper, we propose the Two-way Deep Reinforcement Learning (DRL)-Based resource allocation algorithm, which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode. Secondary users (SUs) in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access (PD-SCMA) scheme, and the physical resources of the cognitive base station are virtualized into two types of slices: enhanced mobile broadband (eMBB) slice and ultrareliable low latency communication (URLLC) slice. We design the Double Deep Q Network (DDQN) network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient (DDPG) network output the optimal power allocation scheme. The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service (QoS) of SUs. Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction. Additionally, compared with the Power Domain Non-orthogonal Multiple Access (PD-NOMA) slices and the Sparse Code Multiple Access (SCMA) slices, the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.

The emergence of various new services has posed a huge challenge to the existing network architecture. To improve the network delay and backhaul pressure, caching popular contents at the edge of network has been considered as a feasible scheme. However, how to efficiently utilize the limited caching resources to cache diverse contents has been confirmed as a tough problem in the past decade. In this paper, considering the time-varying user requests and the heterogeneous content sizes, a user preference aware hierarchical cooperative caching strategy in edge-user caching architecture is proposed. We divide the caching strategy into three phases, that is, the content placement, the content delivery and the content update. In the content placement phase, a cooperative content placement algorithm for local content popularity is designed to cache contents proactively. In the content delivery phase, a cooperative delivery algorithm is proposed to deliver the cached contents. In the content update phase, a content update algorithm is proposed according to the popularity of the contents. Finally, the proposed caching strategy is validated using the MovieLens dataset, and the results reveal that the proposed strategy improves the delay performance by at least 35.3% compared with the other three benchmark strategies.

Due to the interdependency of frame synchronization (FS) and channel estimation (CE), joint FS and CE (JFSCE) schemes are proposed to enhance their functionalities and therefore boost the overall performance of wireless communication systems. Although traditional JFSCE schemes alleviate the influence between FS and CE, they show deficiencies in dealing with hardware imperfection (HI) and deterministic line-of-sight (LOS) path. To tackle this challenge, we proposed a cascaded ELM-based JFSCE to alleviate the influence of HI in the scenario of the Rician fading channel. Specifically, the conventional JFSCE method is first employed to extract the initial features, and thus forms the non-Neural Network (NN) solutions for FS and CE, respectively. Then, the ELM-based networks, named FS-NET and CE-NET, are cascaded to capture the NN solutions of FS and CE. Simulation and analysis results show that, compared with the conventional JFSCE methods, the proposed cascaded ELM-based JFSCE significantly reduces the error probability of FS and the normalized mean square error (NMSE) of CE, even against the impacts of parameter variations.

Code converters are essential in digital nano communication; therefore, a low-complexity optimal QCA layout for a BCD to Excess-3 code converter has been proposed in this paper. A QCA clock-phase-based design technique was adopted to investigate integration with other complicated circuits. Using a unique XOR gate, the recommended circuit’s cell complexity has been decreased. The findings produced using the QCADesigner-2.0.3, a reliable simulation tool, prove the effectiveness of the current structure over earlier designs by considering the number of cells deployed, the area occupied, and the latency as design metrics. In addition, the popular tool QCAPro was used to estimate the energy dissipation of the proposed design. The proposed technique reduces the occupied space by $\sim40\%$, improves cell complexity by $\sim20\%$, and reduces energy dissipation by $\sim1.8$ times (at $\gamma = 1.5E_K$) compared to the current scalable designs. This paper also studied the suggested structure’s energy dissipation and compared it to existing works for a better performance evaluation.

More and more accounts or devices are shared by multiple users in video applications, which makes it difficult to provide recommendation service. Existing recommendation schemes overlook multi-user sharing scenarios, and they cannot make effective use of the mixed information generated by multi-user when exploring users’ potential interests. To solve these problems, this paper proposes an adaptive program recommendation system for multi-user sharing environment. Specifically, we first design an offline periodic identification module by building multi-user features and periodically predicting target user in future sessions, which can separate the profile of target user from mixed log records. Subsequently, an online recommendation module with adaptive time-varying exploration strategy is constructed by jointly using personal information and multi-user social information provided by identification module. On one hand, to learn the dynamic changes in user-interest, a time-varying linear upper confidence bound (LinUCB) based on personal information is designed. On the other hand, to reduce the risk of exploration, a time-invariant LinUCB based on separated multi-user social information from one account/device is proposed to compute the quality scores of programs for each user, which is integrated into the time-varying LinUCB by cross-weighting strategy. Finally, experimental results validate the efficiency of the proposed scheme.

Intelligent reflecting surface (IRS) is a newly emerged and promising paradigm to substantially improve the performance of wireless communications by constructing favorable communication channels via properly tuning massive reflecting elements. This paper considers a distributed IRS aided decode-and-forward (DF) relaying system over Nakagami-m fading channels. Based on a tight approximation for the distribution of the received signal-to-noise ratio (SNR), we first derive exact closed-form expressions of the outage probability, ergodic capacity, and energy efficiency for the considered system. Moreover, we propose the optimal IRS configuration considering the energy efficiency and pilot overhead. Finally, we compare the performance between the distributed IRS-aided DF relaying and multi-IRS-only systems, and verify the analytical results by using monte carlo simulations.

UAV-aided cellular networks, millimeter wave (mm-wave) communications and multi-antenna techniques are viewed as promising components of the solution for beyond-5G (B5G) and even 6G communications. By leveraging the power of stochastic geometry, this paper aims at providing an effective framework for modeling and analyzing a UAV-aided heterogeneous cellular network, where the terrestrial base stations (TBSs) and the UAV base stations (UBSs) co-exist, and the UBSs are provided with mm-wave and multi-antenna techniques. By modeling the TBSs as a PPP and the UBSs as a Matern hard-core point process of type II (MPH-II), approximated but accurate analytical results for the average rate of the typical user of both tiers are derived through an approximation method based on the mean interference-to-signal ratio (MISR) gain. The influence of some relevant parameters is discussed in detail, and some insights into the network deployment and optimization are revealed. Numerical results show that some trade-offs are worthy of being considered, such as the antenna array size, the altitude of the UAVs and the power control factor of the UBSs.

Secret key generation (SKG) is a promising solution to the problem of wireless communications security. As the first step of SKG, channel probing affects it significantly. Although there have been some probing schemes, there is a lack of research on the optimization of the probing process. This study investigates how to optimize correlated parameters to maximize the SKG rate (SKGR) in the time-division duplex (TDD) mode. First, we build a probing model which includes the effects of transmitting power, the probing period, and the dimension of sample vectors. Based on the model, the analytical expression of the SKGR is given. Next, we formulate an optimization problem for maximizing the SKGR and give an algorithm to solve it. We conclude the SKGR monotonically increases as the transmitting power increases. Relevant mathematical proofs are given in this study. From the simulation results, increasing appropriately the probing period and the dimension of the sample vector could increase the SKGR dramatically compared to a yardstick, which indicates the importance of optimizing the parameters related to the channel probing phase.

In recent years, blockchain technology integration and application has gradually become an important driving force for new technological innovation and industrial transformation. While blockchain technology and applications are developing rapidly, the emerging security risks and obstacles have gradually become prominent. Attackers can still find security issues in blockchain systems and conduct attacks, causing increasing losses from network attacks every year.

Serverless computing is a promising paradigm in cloud computing that greatly simplifies cloud programming. With serverless computing, developers only provide function code to serverless platform, and these functions are invoked by its driven events. Nonetheless, security threats in serverless computing such as vulnerability-based security threats have become the pain point hindering its wide adoption. The ideas in proactive defense such as redundancy, diversity and dynamic provide promising approaches to protect against cyberattacks. However, these security technologies are mostly applied to serverless platform based on ``stacked" mode, as they are designed independent with serverless computing. The lack of security consideration in the initial design makes it especially challenging to achieve the all life cycle protection for serverless application with limited cost. In this paper, we present ATSSC, a proactive defense enabled attack tolerant serverless platform. ATSSC integrates the characteristic of redundancy, diversity and dynamic into serverless seamless to achieve high-level security and efficiency. Specifically, ATSSC constructs multiple diverse function replicas to process the driven events and performs cross-validation to verify the results. In order to create diverse function replicas, both software diversity and environment diversity are adopted. Furthermore, a dynamic function refresh strategy is proposed to keep the clean state of serverless functions. We implement ATSSC based on Kubernetes and Knative. Analysis and experimental results demonstrate that ATSSC can effectively protect serverless computing against cyberattacks with acceptable costs.

Due to the lack of authentication mechanism in BeiDou navigation satellite system (BDS), BD-II civil navigation message (BDII-CNAV) are vulnerable to spoofing attack and replay attack. To solve this problem, we present a security authentication protocol, called as BDSec, which is designed by using China's cryptography Shangyong Mima (SM) series algorithms, such as SM2/4/9 and Zu Chongzhi (ZUC) algorithm. In BDSec protocol, both of BDII-CNAV and signature information are encrypted using the SM4 algorithm (Symmetric encryption mechanism). The encrypted result is used as the subject authentication information. BDSec protocol applies SM9 algorithm (Identity-based cryptography mechanism) to protect the integrity of the BDII-CNAV, adopts the SM2 algorithm (Public key cryptosystem) to guarantee the confidentiality of the important session information, and uses the ZUC algorithm (Encryption and integrity algorithm) to verify the integrity of the message authentication serial number and initial information and the information in authentication initialization sub-protocol respectively. The results of the SVO logic reasoning and performance analysis show that BDSec protocol meets security requirements for the dual user identity authentication in BDS and can realize the security authentication of BDII-CNAV.

With the promotion of digital currency, how to effectively solve the authenticity, privacy and usability of digital currency issuance has been a key problem. Redactable signature scheme (RSS) can provide the verification of the integrity and source of the generated sub-documents and solve the privacy problem in digital currency by removing blocks from the signed documents. Unfortunately, it has not realized the consolidation of signed documents, which can not solve the problem of merging two digital currencies. Now, we introduce the concept of weight based on the threshold secret sharing scheme (TSSS) and present a redactable signature scheme with merge algorithm (RSS-MA) using the quasi-commutative accumulator. Our scheme can reduce the communication overhead by utilizing the merge algorithm when transmitting multiple digital currency signatures. Furthermore, this can effectively hide the scale of users' private monetary assets and the number of transactions between users. While meeting the three properties of digital currency issuance, in order to ensure the availability of digital currency after redacting, editors shall not remove the relevant identification information block form digital currency. Finally, our security proof and the analysis of efficiency show that RSS-MA greatly improves the communication and computation efficiency when transmitting multiple signatures.

The compatibility of different quantum algorithms should be considered when these algorithms are combined. In this paper, the method of combining Grover and Simon is studied for the first time, under some preconditions or assumptions. First, we give two preconditions of applying Grover’s algorithm, which ensure that the success probability of finding the marked element is close to 1. Then, based on these two preconditions, it is found out that the success probability of the quantum algorithm for FX-construction is far less than 1. Furthermore, we give the design method of the Oracle function, and then present the general method of combining Grover and Simon algorithm for attacking block ciphers, with success probability close to 1.

With the gradual popularization of 5G communications, the application of multi-antenna broadcasting technology has become widespread. Therefore, this study aims to investigate the wireless covert communication in the two-user cooperative multi-antenna broadcast channel. We focus on the issue that the deteriorated reliability and undetectability are mainly affected by the transmission power. To tackle this issue, we design a scheme based on beamforming to increase the reliability and undetectability of wireless covert communication in the multi-antenna broadcast channel. We first modeled and analyzed the cooperative multi-antenna broadcasting system, and put forward the target question. Then we use the SCA (successive convex approximation) algorithm to transform the target problem into a series of convex sub-problems. Then the convex problems are solved and the covert channel capacity is calculated. In order to verify the effectiveness of the scheme, we conducted simulation verification. The simulation results show that the proposed beamforming scheme can effectively improve the reliability and undetectability of covert communication in multi-antenna broadcast channels.

In this paper, we concentrate on a reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system to improve the offload efficiency with moving user equipments (UEs). We aim to minimize the energy consumption of all UEs by jointly optimizing the discrete phase shift of RIS, UEs' transmitting power, computing resources allocation, and the UEs’ task offloading strategies for local computing and offloading. The formulated problem is a sequential decision making across multiple coherent time slots. Furthermore, the mobility of UEs brings uncertainties into the decision-making process. To cope with this challenging problem, the deep reinforcement learning-based Soft Actor-Critic (SAC) algorithm is first proposed to effectively optimize the discrete phase of RIS and the UEs’ task offloading strategies. Then, the transmitting power and computing resource allocation can be determined based on the action. Numerical results demonstrate that the proposed algorithm can be trained more stably and perform approximately $14\%$ lower than the deep deterministic policy gradient benchmark in terms of energy consumption.

Beam management, including initial access (IA) and beam tracking, is essential to the millimeter-wave Unmanned Aerial Vehicle (UAV) network. However, the conventional communication-only and feedback-based schemes suffer a high delay and low accuracy of beam alignment, since they only enable the receiver to passively ``hear'' the information of the transmitter from the radio domain. This paper presents a novel sensing-assisted beam management approach, the first solution that fully utilizes the information from the visual domain to improve communication performance. We employ both integrated sensing and communication and computer vision techniques and design an extended Kalman filtering method for beam tracking and prediction. Besides, we also propose a novel dual identity association solution to distinguish multiple UAVs in dynamic environments. Real-world experiments and numerical results show that the proposed solution outperforms the conventional methods in IA delay, association accuracy, tracking error, and communication performance.

Dielectric resonator magnetoelectric dipole (DRMED) arrays with enhanced isolation, reduced cross-polarization, and backward radiation are proposed for base station (BS) applications. The proposed antenna comprises an elevated dielectric resonator antenna (DRA) on a small metal plate above a sizeable common ground plane. The DRA is designed in its $TE_{\delta11}$ mode, acting like a magnetic dipole. The surface current excited by the differential probes flowing on the small ground plane is equivalent to an electric dipole. Since these two equivalent dipoles are orthogonal, they have the magnetoelectric dipole characteristics with reduced backward radiation. Meanwhile, the small ground planes can be treated as decoupling structures to provide a neutralization path to cancel the original coupling path. A linearly-polarized 4-element prototype array was verified experimentally in previous work. Here, a dual-polarized DRMED antenna is presented to construct a 2-element and 4$\times$4 array for BS applications. To investigate its MIMO performance, sophisticated multi-cell scenario simulations are carried out. By using the proposed dual-polarized DRMED array, the cellular system capacity is improved by 118.6% compared to a conventional DRA array. This significant MIMO system improvement is mainly due to the reduced backward radiation and, therefore, reduced inter-cell interferences. Measurements align well with the simulations.

Millimeter-wave (mmWave) radar communication has emerged as an important technique for future wireless systems. However, the interference between the radar signal and communication data is the main issue that should be considered for the joint radar communication system. In this paper, a co-sharing waveform (CSW) is proposed to achieve communication and radar sensing simultaneously. To eliminate the co-interference between the communication and sensing signal, signal splitting and processing methods for communication data demodulation and radar signal processing are given respectively. Simulation results show that the bit error rate (BER) of CSW is close to that of the pure communication waveform. Moreover, the proposed CSW can achieve better performance than the existing waveforms in terms of range and velocity estimation.