With the research of the upcoming sixth generation (6G) systems, new technologies will require wider bandwidth, larger scale antenna arrays and more diverse wireless communication scenarios on the future channel modeling. Considering channel model is prerequisite for system design and performance evaluation of 6G technologies, we face a challenging task: how to accurately and efficiently model 6G channel for various scenarios? This paper tries to answer it. Firstly, the features of cluster-nuclei (CN) and principle of cluster-nuclei based channel model (CNCM) are introduced. Then, a novel modeling framework is proposed to implement CNCM, which consists four steps: propagation environment reconstruction, cluster-nuclei identification, multipath parameters generation, and channel coefficients generation. Three-dimensional environment with material information is utilized to map CN with scatterers in the propagation pathway. CN are identified by geometrical and electric field calculation based on environmental mapping, and multipath components within CN are calculated by statistical characteristics of angle, power and delay domains. Finally, we present a three-level verification structure to investigate the accuracy and complexity of channel modeling comprehensively. Simulation results reveal that CNCM can perform higher accuracy than geometry-based stochastic model while lower complexity compared with ray-tracing model for practical propagation environment.

In the future smart transportation system, reliable vehicle-to-infrastructure (V2I) communication is very important to ensure vehicle driving safety and to improve vehicle driving efficiency. In this paper, V2I channel measurements at 5.92 GHz are conducted in typical urban and highway scenarios. The frequency and bandwidth of transmission, as well as the deployment of the RSU (roadside unit) and the OBU (on board unit), are selected by considering the recommendation proposed by 3GPP TR 36.885. Then, based on the measured data, the key channel characteristic parameters of the V2I channel are extracted, including path loss, root-mean-square delay spread, stationarity distance, and Doppler spread, etc. Also, the statistical characteristics of the parameters, including time-varying and Doppler characteristics, are investigated and characterized. The work in this paper helps researchers design technology and communication systems in similar scenarios.

The sub-6G band is too crowded to accommodate higher data rate, while the millimeter wave (mmWave) bands have abundant spectrum resources and massive MIMO can provide high spectral and energy efficiency. Therefore, the combination of the two, namely mmWave-MIMO system, has attracted intensive research interests. In this paper, we develop a high-speed mmWave-MIMO communication system and conduct exhaustive field tests. The detail of the system design is provided and the key modules of the testbed are analyzed. The testbed exploits high gain of mmWave RF and flexible configuration of embedded system. The validation and field tests show that the developed testbed can provide up to 2.3 Gbps network layer data rate in single channel with low latency and support point-to-multi-point (PtMP) transmission aided by relay. The testbed can be used in future B5G and 6G systems to provide high reliability and low latency wireless coverage.

Non-orthogonal multiple access (NOMA) has been integrated in millimeter-wave (mmWave) Massive MIMO systems to further enhance the spectrum efficiency, but NOMA has not been fully considered in lens mmWave systems. The fusion of these two technologies requires the joint design of beam selection and interference cancellation. In addition, when users follow the spatial cluster distribution, although the user clustering schemes based on K-means algorithm have been applied, the influence of the virtual and actual cluster center users on achievable sum rate has not been differentiated and analyzed in detail. To solve the above problems, a joint optimization scheme is proposed to maximize the achievable sum rate. The optimization problem is NP-hard, which is solved by using the divide-and-conquer approach. Concretely, based on the signal power loss analysis of directional deviation, a beam selection algorithm is designed for inter-cluster interference cancellation based on the K-means algorithm. Further for intra-cluster interference cancellation, a power allocation algorithm based on the bisection method is designed to guarantee the fairness of users in each cluster. The simulation results show that the proposed scheme offers a significant performance improvement in terms of both achievable sum rate and energy efficiency, and it is suitable for the large-scale user scenario due to its low complexity.

A joint Doppler shift and channel estimation method for the millimeter-wave communication system of an unmanned aerial vehicle (UAV) equipped with a large-scale uniform linear antenna (ULA) array has been proposed. Since Doppler shift induces intercarrier interference, the parameters of the channel paths have been decomposed into the Doppler shift and the channel information. In order to obtain the Doppler shift, a new estimation algorithm based on a combination of discrete Fourier transform and phase rotation has been proposed, which can determine the appropriate number of antennas. In addition to estimating the channel information, a low-complexity joint Doppler shift and channel estimation method has been designed that can quickly obtain accurate estimates. Furthermore, the achievable sum rate, the theoretical bounds of the mean squared errors, and the Cramér-Rao lower bounds of the estimation method have been derived. The analysis and simulation results prove that the performance of the proposed approach is close to the theoretical inference.

This paper proposes a novel joint channel estimation and beamforming scheme for the massive multiple-input-multiple-output (MIMO) frequency-division duplexing (FDD) wireless legitimate surveillance system. With the proposed scheme, the monitor with the full duplex capability realizes the proactive eavesdropping of the suspicious link by leveraging the pilot attack approach. Specifically, exploiting the effective eavesdropping rate and the mean square error as performance metrics and setting a total power budget at the training and transmission phases, while guaranteeing the information from suspicious source can be successfully decode, joint pilot design, power allocation and beamforming strategy are formulated as optimization problems for the two objective functions: MSE and effective eavesdropping rate. A closed-form expression of the optimal pilot with the limited length can be obtained via the channel correlation. The optimal power problem at the training phase can be solved by a simple bisection method. Then, based on the obtained imperfect estimated channel, the jamming beamforming at monitor optimization algorithm is proposed by utilizing the convex Semidefinite Programming approach to maximize the effective eavesdropping rate. Numerical results show that the proposed joint pilot design, power allocation and beamforming optimization scheme can improve the surveillance performance of the legitimate monitor as compared to the existing passive eavesdropping and jamming-assisted eavesdropping.

In this paper, a low-profile wideband dielectric resonator antenna (DRA) with a very compact planar size is investigated. The antenna consists of a high permittivity dielectric sheet on the top, a low permittivity substrate in the middle, and a probe feeding structure at the bottom. By digging an annular slot in the designated area of the square dielectric sheet, the resonant frequency of fundamental $TE_{111}$ mode can be effectively increased to be close to the high-order $TE_{131}$ mode. The two modes can be finally merged together, yielding a wide impedance bandwidth of 16.6%. Most importantly, the combination of the two modes is done on the premise of a fixed antenna planar size, which can be very compact and suitable for beam-scanning applications. A probe feeding structure is used to excite the DRA, making the antenna simple and practical to be integrated with other RF circuits. For verification, antenna prototypes with single-feed linear polarization and differential-feed dual polarization were fabricated and measured. Reasonable agreement between the measured and simulated results is observed.

Intelligent reflecting surface (IRS) is regarded as a promising technology because it can achieve higher passive beamforming gain. In particular, the IRS assisted simultaneous wireless information and power transfer (SWIPT) system can make the information decoding receivers (IDRs) have a higher signal-to-noise ratio (SNR), and the energy harvesting receivers (EHRs) have the guarantee of minimum harvested energy threshold. Motivated by the above, in this paper, we use the power splitting (PS) at the user and introduce artificial noise (AN) into the access point (AP), so that the user in system can harvest energy and decode information simultaneously, further improve the security of user. We jointly optimize the beamforming matrix at AP, the reflection phase shift at IRS and the PS ratio, in order to maximize the user's achievable secrecy rate, subject to the user's minimum harvested energy threshold and AP's transmission power. Due to the introduction of PS ratio, the coupling between variables is increased, and the complexity of the problem is significantly increased. Furthermore, the problem is non-convex, so we propose an efficient algorithm based on Taylor Formula, semi-definite relaxation (SDR) and alternating optimization (AO) to get the solution. Numerical results show that the proposed IRS-SWIPT system with PS and AN achieves significant performance improvement compared with other benchmark scheme.

With energy harvesting capability, the Internet of things~(IoT) devices transmit data depending on their available energy, which leads to a more complicated coupling and brings new technical challenges to delay optimization. In this paper, we study the delay-optimal random access~(RA) in large-scale energy harvesting IoT networks. We model a two-dimensional Markov decision process (MDP) to address the coupling between the data and energy queues, and adopt the mean field game (MFG) theory to reveal the coupling among the devices by utilizing the large-scale property. Specifically, to obtain the optimal access strategy for each device, we derive the Hamilton-Jacobi-Bellman (HJB) equation which requires the statistical information of other devices. Moreover, to model the evolution of the states distribution in the system, we derive the Fokker-Planck-Kolmogorov (FPK) equation based on the access strategy of devices. By solving the two coupled equations, we obtain the delay-optimal random access solution in an iterative manner with Lax-Friedrichs method. Finally, the simulation results show that the proposed scheme achieves significant performance gain compared with the conventional schemes.

With the rapid increasing of maritime activities, maritime wireless networks (MWNs) with high reliability, high energy efficiency, and low delay are required. However, the centralized networking with fixed resource scheduling is not suitable for MWNs due to the special environment. In this paper, we introduce the collaborative relay communication in distributed MWNs to improve the link reliability, and propose an orthogonal time-frequency resource block reservation based multiple access (RRMA) scheme for both one-hop direct link and two-hop collaborative relay link to reduce the interference. To further improve the network performance, we formulate an energy efficiency (EE) maximization resource allocation problem and solve it by an iterative algorithm based on the Dinkelbach method. Finally, numerical results are provided to investigate the proposed RRMA scheme and resource allocation algorithm, showing that the low outage probability and transmission delay can be attained by the proposed RRMA scheme. Moreover, the proposed resource allocation algorithm is capable of achieving high EE in distributed MWNs.

In space-based Automatic Identification Systems (AIS), due to high satellite orbits, several Ad Hoc cells within the observation range of the satellite are vulnerable to interference by an external signal. To increase efficiency in target detection and improve system security, a blind source separation method is adopted for processing the conflicting signals received by satellites. Compared to traditional methods, we formulate the separation problem as a clustering problem. Since our algorithm is affected by the sparseness of source signals, to get satisfactory results, our algorithm assumes that the distance between two arbitrary mixed-signal vectors is less than the doubled sum of variances of distribution of the corresponding mixtures. Signal sparsity is overcome by computing the Short-Time Fourier Transform, and the mixed source signals are separated using the improved PSO clustering. We evaluated the performance and the robustness of the proposed network architecture by several simulations. The experimental results demonstrate the effectiveness of the proposed method in not only improving satellite signal receiving ability but also in enhancing space-based AIS security.

Unmanned aerial vehicle (UAV)-assisted communications have been considered as a solution of aerial networking in future wireless networks due to its low-cost, high-mobility, and swift features. This paper considers a UAV-assisted downlink transmission, where UAVs are deployed as aerial base stations to serve ground users. To maximize the average transmission rate among the ground users, this paper formulates a joint optimization problem of UAV trajectory design and channel selection, which is NP-hard and non-convex. To solve the problem, we propose a multi-agent deep Q-network (MADQN) scheme. Specifically, the agents that the UAVs act as perform actions from their observations distributively and share the same reward. To tackle the tasks where the experience is insufficient, we propose a multi-agent meta reinforcement learning algorithm to fast adapt to the new tasks. By pretraining the tasks with similar distribution, the learning model can acquire general knowledge. Simulation results have indicated the MADQN scheme can achieve higher throughput than fixed allocation. Furthermore, our proposed multi-agent meta reinforcement learning algorithm learns the new tasks much faster compared with the MADQN scheme.

The emerging edge computing technology for the Internet of Things has been playing an important role in our daily life. It is promising to utilize a multi-receiver signcryption scheme to protect the transmission data when an edge device broadcasts its sensing data to many different end devices at a time. There are several things to consider when we design a signcryption scheme. First existing schemes need to maintain a secure channel to generate the user private key, which may increase economic costs. Second the system private key of those schemes is kept secret by a single key generation center (KGC), and the single point of failure of KGC may compromise the whole system. For this, we propose a multi-receiver multi-message signcryption scheme without the secure channel. Firstly the scheme allows KGC to send secrets through the public channel, which reduces maintenance costs. Secondly, to eliminate the single point of failure, the scheme utilizes multiple KGCs to manage the system private key, and updates the secret of each KGC periodically to resist advanced persistent threat attacks. We demonstrate that the proposed scheme can achieve expected security properties. Performance analysis shows that it is with shorter ciphertext length and higher efficiency.

Mobile edge computing (MEC) deployment in a multi-robot cooperation (MRC) system is an effective way to accomplish the tasks in terms of energy consumption and implementation latency. However, the computation and communication resources need to be considered jointly to fully exploit the advantages brought by the MEC technology. In this paper, the scenario where multi robots cooperate to accomplish the time-critical tasks is studied, where an intelligent master robot (MR) acts as an edge server to provide services to multiple slave robots (SRs) and the SRs are responsible for the environment sensing and data collection. To save energy and prolong the function time of the system, two schemes are proposed to optimize the computation and communication resources, respectively. In the first scheme, the energy consumption of SRs is minimized and balanced while guaranteeing that the tasks are accomplished under a time constraint. In the second scheme, not only the energy consumption, but also the remaining energies of the SRs are considered to enhance the robustness of the system. Through the analysis and numerical simulations, we demonstrate that even though the first policy may guarantee the minimization on the total SRs' energy consumption, the function time of MRC system by the second scheme is longer than that by the first one.

Federated learning (FL), which allows multiple mobile devices to cooperatively train a machine learning model without sharing their data with the central server, has received widespread attention. However, the process of FL involves frequent communications between the server and mobile devices, which incurs a long latency. Intelligent reflecting surface (IRS) provides a promising technology to address this issue, thanks to its capacity to reconfigure the wireless propagation environment. In this paper, we exploit the advantage of IRS to reduce the latency of FL. Specifically, we formulate a latency minimization problem for the IRS assisted FL system, by optimizing the communication resource allocations including the devices' transmit-powers, the uploading time, the downloading time, the multi-user decomposition matrix and the phase shift matrix of IRS. To solve this non-convex problem, we propose an efficient algorithm which is based on the Block Coordinate Descent (BCD) and the penalty difference of convex (DC) algorithm to compute the solution. Numerical results are provided to validate the efficiency of our proposed algorithm and demonstrate the benefit of deploying IRS for reducing the latency of FL. In particular, the results show that our algorithm can outperform the baseline of Majorization-Minimization (MM) algorithm with the fixed transmit-power by up to 30%.

Low earth orbit (LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the "ubiquitous connection" of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a three-tier computation architecture by leveraging the vertical cooperation among ground users, LEO satellites, and the cloud server, and the horizontal cooperation between LEO satellites. To improve the quality of service for ground users, we optimize the computation offloading decisions to minimize the total execution delay for ground users subject to the limited battery capacity of ground users and the computation capability of each LEO satellite. However, the formulated problem is a large-scale nonlinear integer programming problem as the number of ground users and LEO satellites increases, which is difficult to solve with general optimization algorithms. To address this challenging problem, we propose a distributed deep learning-based cooperative computation offloading (DDLCCO) algorithm, where multiple parallel deep neural networks (DNNs) are adopted to learn the computation offloading strategy dynamically. Simulation results show that the proposed algorithm can achieve near-optimal performance with low computational complexity compared with other computation offloading strategies.

This article establishes a three-tier mobile edge computing (MEC) network, which takes into account the cooperation between unmanned aerial vehicles (UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions, while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution (DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network (DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity.

The unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) architecture is expected to be a powerful technique to facilitate 5G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer (WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things (IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energy-efficiency (EE) minimization problem is formulated. Due to non-convexity and the time domain coupling of the variables in the formulated problem, a low-complexity online computation offloading and trajectory scheduling algorithm (OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene, which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing (MEC) into a constrained multiobjective optimization problem (CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

With the rapid growth of the demand for indoor location-based services (LBS), Wi-Fi received signal strength (RSS) fingerprints database has attracted significant attention because it is easy to obtain. The fingerprints algorithm based on convolution neural network (CNN) is often used to improve indoor localization accuracy. However, the number of reference points used for position estimation has significant effects on the positioning accuracy. Meanwhile, it is always selected arbitraily without any guiding standards. As a result, a novel location estimation method based on Jenks natural breaks algorithm (JNBA), which can adaptively choose more reasonable reference points, is proposed in this paper. The output of CNN is processed by JNBA, which can select the number of reference points according to different environments. Then, the location is estimated by weighted K-nearest neighbors (WKNN). Experimental results show that the proposed method has higher positioning accuracy without sacrificing more time cost than the existing indoor localization methods based on CNN.

Timely information updates are critical for real-time monitoring and control applications in the Internet of Things (IoT). In this paper, we consider a multi-antenna cellular IoT for state update where a base station (BS) collects information from randomly distributed IoT nodes through time-varying channel. Specifically, multiple IoT nodes are allowed to transmit their state update simultaneously in a spatial multiplex manner. Inspired by age of information (AoI), we introduce a novel concept of age of transmission (AoT) for the sceneries in which BS cannot obtain the generation time of the packets waiting to be transmitted. The deadline-constrained AoT-optimal scheduling problem is formulated as a restless multi-armed bandit (RMAB) problem. Firstly, we prove the indexability of the scheduling problem and derive the closed-form of the Whittle index. Then, the interference graph and complementary graph are constructed to illustrate the interference between two nodes. The complete subgraphs are detected in the complementary graph to avoid inter-node interference. Next, an AoT-optimal scheduling strategy based on the Whittle index and complete subgraph detection is proposed. Finally, numerous simulations are conducted to verify the performance of the proposed strategy.