The interoperability of satellite networks is crucial for achieving global low latency connections in B5G/6G communication. However, the use of different crypto-systems by multiple operators poses security threats such as cross domain identity spoofing and man-in-the-middle attacks. This article proposes a two-way anonymous and cross PKI-IBC authentication mechanism for the inter-satellite-networks interoperability scenarios. The proposed mechanism pre-establishes trust between authority centers of diverse operators. When authenticating, satellites online request the public key of the opponent’s operator’s authority center. Thus, satellites can guarantee the authenticity of each other’s public keys and then verify their identities mutually. We also generate temporary symmetric keys to conceal the public key and identity of the satellites, preserving identity privacy. Finally, the session key is derived through symmetric encryption to ensure the confidentiality of subsequent messages. ProVerif and BAN logic have formally verified that the proposed mechanism can achieve mutual authentication and key negotiation, while ensuring that private keys are not leaked. The numerical results indicate that our mechanism has lower authentication latency and communication overhead compared to existing schemes.

Quantum computing revolutionizes the way of solving complex problems and handling vast datasets, which shows great potential to accelerate the machine learning process. However, data leakage in quantum machine learning (QML) may present privacy risks. Although differential privacy (DP), which protects privacy through the injection of artificial noise, is a well-established approach, its application in the QML domain remains under-explored. In this paper, we propose to harness inherent quantum noises to protect data privacy in QML. Especially, considering the Noisy Intermediate-Scale Quantum (NISQ) devices, we leverage the unavoidable shot noise and incoherent noise in quantum computing to preserve the privacy of QML models for binary classification. We mathematically analyze that the gradient of quantum circuit parameters in QML satisfies a Gaussian distribution, and derive the upper and lower bounds on its variance, which can potentially provide the DP guarantee. Through simulations, we show that a target privacy protection level can be achieved by running the quantum circuit a different number of times.

In this paper, we investigate the joint communication-motion planning problem for unmanned aerial vehicle (UAV) swarm in the presence of jammers. Specifically, we consider a cluster-based UAV swarm architecture, where multiple cluster member (CM) UAVs transmit messages to a cluster head (CH) UAV through air-to-air links affected by malicious jammers. Our objective is to maximize the sum uplink rate of the UAV swarm by optimizing the trajectories and the transmit power of all UAVs. To achieve this goal, we formulate a joint multi-UAV trajectories and transmit power optimization problem under speed, transmit power, trajectories and received signal-to-interference-plus-noise ratio (SINR) constraints. In order to solve the problem, we establish a Markov decision process (MDP). For the multi-agent environment and the high-dimensional continuous action space, we adopt a multi-agent twin delayed deep deterministic (MATD3) policy gradient-based algorithm. Simulation results show that the proposed scheme can effectively improve the sum uplink rate of the UAV swarm compared to the baseline schemes.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has been touted as a promising solution for providing computing services in disaster relief and other settings due to its flexibility and ease of deployment. Nevertheless, providing computing services for a large number of mobile devices is challenged by UAVs’ limited computation and energy resources. To this end, we propose a scheme for joint trajectory optimization and task offloading that aims to minimize the total delay of all computing tasks. Our proposed scheme involves formulating the scheduling of mobile devices and computing tasks, adjustment of UAV flight angle and speed, and transmission power control as a non-convex mixed integer programming problem. In order to address the issue, we establish a Markov decision process (MDP) for UAV-assisted MEC systems. Given the high-dimensional continuous action space, we adopt a reinforcement learning algorithm based on Deep Deterministic Policy Gradient (DDPG). The results of the simulation indicate that our suggested scheme outperforms the baseline schemes in processing delay, and the DDPG-based algorithm exhibits rapid convergence.

With the continuous development of artificial intelligence (AI) technology, AI applications will play an increasingly important role in the sixth generation (6G) networks. At the same time, the emergence of technologies such as cloud computing has led to a growing number of AI models being applied in the Internet-of-Things (IoT). However, increasing sizes of AI models cause heavy burden on networks. In this paper, a multipath transmission scheme for the model slices based on the network function virtualization (NFV) is proposed. First, an optimization problem is formulated to decide the storage nodes for the model slices and the routing. With the physical network resource constraints, the problem is formulated as a mixed integer linear programming (MILP) to minimize the transmission cost. Second, a heuristic algorithm based on the steiner tree problem is designed to solve the optimization problem. Finally, based on the transfer learning method we get one generic slice and two specific slices from VGG16 for simulation. The results show when the destination nodes number and the network size are large, the transmission scheme for model slices has better performance in bandwidth utilization.

This paper investigates the uplink secure transmission in an intelligent reflecting surface (IRS) aided Cell-Free Multiple Input Multiple Output network. To maximize the minimum secrecy rate (SR) among legitimate users, we jointly optimize the uplink power control vector and the passive beamforming vector at IRS with consideration of resource allocation fairness. We propose an alternating optimization based SR max-min fairness algorithm to solve the non-convex problem. Based on semidefinite relaxation, the sub-problem of phase optimization at IRS is solved. Geometric programming is utilized to handle the optimization of power control with the assist of condensation method. Simulation results verify that the proposed algorithm can converge to obtain the solution. The minimum SR of the proposed scheme is increased by 14% compared with random phase scheme and the max-min fairness among users is realized.

The sensitivity of millimeter-wave (mmWave) to blockage and the requirement for the communication system to support mobility scenarios makes mmWave blockage and beam prediction necessary. In this paper, the effect of diffraction characteristics on improving blockage and beam prediction is investigated. A dataset with diffraction is created, and a recurrent neural network (RNN) is designed to capture the diffraction characteristics in the dataset. Further, the generalization ability of the RNN for different scenarios is researched. The results show that the accuracy of both blockage and beam prediction is further improved by utilizing diffraction characteristics. In addition, the blockage and beam prediction accuracy of the designed RNN is increased by 1.5% and 9.7% compared with a reference deep neural networks (DNN). Finally, applying the RNN model trained in the outdoor scenario to the prediction in the indoor scenario, the indoor blockage and beam prediction accuracy respectively reach up to 99.8% and 98.3% of the outdoor’s.

The multi-probe anechoic chamber (MPAC) method is seen as the most promising method in over-the-air (OTA) radiated test for wireless devices, and its cost mainly comes from the ports of the channel emulator (CE) which are connected with probes placed in the chamber. In this paper, a new probe selection method based on differential evolution (DE) algorithm is proposed, which can effectively reduce the number of probes and accurately simulate the spatial characteristics of the target channel. Simulation results show that the proposed algorithm has great expansibility and can achieve high accuracy of channel emulation with low cost in various MPAC setups.

Background matting is a recently developed image matting approach, with applications to image and video editing. It refers to estimating both the alpha matte and foreground from a pair of images with and without foreground objects. Recent work has applied deep learning to background matting, with very promising performance achieved. However, existing deep models are supervised which require a large dataset with ground truth alpha mattes for training. To avoid the cost of data collection and possible bias in training data, this paper proposes a dataset-free unsupervised deep learning-based approach for background matting. Observing that the local smoothness of alpha matte can be well characterized by the untrained network prior called deep matte prior, we model the foreground and alpha matte using the priors encoded by two generative convolutional neural networks. To avoid possible overfitting during unsupervised learning, a two-stage learning scheme is developed which contains projection-based training and Bayesian post refinement. An alpha-matte-driven initialization scheme is also developed for performance boost. Even without calling external training data, the proposed approach provides competitive performance to recent supervised learning-based methods in the experiments.

Millimeter-wave technology is the key technology to support the connection and throughput of the Industrial Internet of Things (IIoT). And the channel model is the basis for the evaluation, optimization, and deployment of the wireless communication system. To model the millimeter-wave channel characteristics in the IIoT scenario, we conduct measurements in a factory-like scenario at 28 GHz. In measurements, antenna heights are set to be higher or lower than most of the metal machines, separately. Based on the collected data, we analyze the large-scale fading and small-scale fading characteristics, including path loss, delay spread (DS), and angular spread (AS). These channel characteristics are statistically modeled. The effects of antenna heights on these channel characteristics are also investigated. Furthermore, we study the distance dependence of the DS and AS.

This paper contributes improvements on both the defect of dot product and the imbalance of the datasets in matrix factorization. Above all, matrix factorization is still the most widely used technology in the recommendation system. However, its dot product does not follow triangle inequality, which restricts the improvement of its effect. We take inspiration from the distance factor of metric learning, and convert the determinants of user-item relevance from the size of the dot product to the distance of the metric factorization. Furthermore, the number of positive examples is much smaller than the negatives in most datasets. Such an unbalanced scenario will affect the accuracy of recommendations. Inspired by the positive semidefinite matrix of the popular Mahalanobis distance in the field of metric learning, we have fully considered the interaction information between users and items and propose the concept of all-weighted matrix. Finally, the combination of the two improved techniques proposed the All-Weighted Metric Factorization (AWMF) method, which is applied to the personalized ranking task. We have done scientific and adequate experiments on three common datasets, and the results outperform several baselines on different evaluation indicators.

To improve the revenue of dairy farms, cow estrus must be accurately monitored to track mating time. Narrow Band Internet of Things (NB-IoT) is considered as a promising technology to realize cost-effective detection system attributing to its wide coverage and low power consumption. To increase the success rate of real-time detection, machine learning based algorithms have been applied to extract patterns from estrus data. However, due to the lack of multivariate time series data, most previous studies do not consider using the time correlation to guide estrus detection. In this paper, we present a NB-IoT based solution framework where multivariate behavioral time series data collected by neck-mounted sensors and then uploaded to a cloud data center for further analysis through NB-IoT network. Based on the collected data, we propose an estrus prediction algorithm which gives estrus alert by exploiting Long-Short Term Memory (LSTM) and Convolution Neural Network (CNN). Through numerical studies conducted using real data set from the pasture, we show that our proposed solution outperforms exiting detection algorithms in terms of accuracy and efficiency.

For providing highly demanding services with powerful computational ability and ultra low-latency communication, mobile edge computing (MEC) has been recognized as a bright rising star among key technologies for the next-generation networking. Generally, jointly optimizing offloading decision and resource allocation in one multi-variable problem is complicated. To decrease computational scale and develop practicable strategy by splitting problems, we divide the workflow of MEC-enabled base station into two stages. First, through formulating a task offloading problem, we propose a low-complexity improved simulated annealing-based heuristic offloading decision (SAHOD) algorithm to maximize network revenue from the perspective of mobile network operator. Then, the optimal fine-grained resource allocation solution is obtained in closed forms via Lagrange duality decomposition method. Furthermore, an effective realtime sub-gradient-based resource allocation (SGRA) algorithm is presented to converge to a specific optimal allocation strategy within the adjustable accuracy. For given users, simulation results show that our SAHOD algorithm can earn about 20.5% more revenue than value-based greedy algorithm. Besides, our SGRA algorithm can converge within 4 iterations and obtain approximately 19.3% more sum rates than static scheduling method.

In this paper, we propose an indoor positioning model using the principle of Bayesian estimation. By analyzing the mobile features of the target and combining these features with state transition probability, we narrow down the belief region of the location distribution. To make our system model more intuitive, some basic concepts about the factor graph and the corresponding sum-product algorithm are introduced. Then, we map our system model onto the factor graph and make basic rules for message propagation. Based on the probability graphic model, mobile features enhanced sum-product algorithm is implemented to calculate the posterior probability of the target given range measurement observations. Simulation results show that the positioning precision of the proposed algorithm is higher than that of the existing algorithms at different speed levels, and the proposed algorithm also performs more robust in noisy indoor environment.

As location information becomes vitally important, positioning has been a highly desirable feature of 5G system which enables a huge amount of location-based applications and services. Millimeter wave (mmWave) is the promising technology for both offering better spectrum resource and positioning performance. A virtualized indoor office scenario with only one mmWave base station (BS) is considered in this paper. User equipment (UE) motion feature, mmWave line-of-sight (LoS) and first order reflection paths' AoA-ToA are fused for indoor positioning. Firstly, an improved least mean square (LMS) algorithm that combines motion message is proposed to refine the multi-path AoA estimation. Furthermore, a modified multi-path unscented Kalman filter (UKF) is proposed to track UE's position in the scenario. The information exchanges of the two stages not only consist of estimates(position, AoA) but also variance of position. Based on the simulation results, the proposed methods provide 2 times LoS-AoA estimation gains and centimeter 3D positioning accuracy respectively. Besides, this strategy is capable of positioning task with insufficient anchor nodes (AN).

Over-the-air (OTA) radiated testing for multi-input multi-output (MIMO) capable mobile terminals has been actively discussed in the standardization in recent years, where multiprobe anechoic chamber (MPAC) method has been selected. Setting up a multiprobe configuration is costly, so finding ways to limit the number of probes will make the implementation of the test system simpler and cheaper. In this paper, two probe subset selection algorithms for three dimensional (3D) MPAC and fading emulator are proposed, namely, decremental selection algorithm (DSA) and error threshold selection algorithm based on alternating search (SAAS), where the goal is to minimize the number of probe antennas while ensuring the accuracy of the target channel emulation. Simulation results show that a small number of probe sets are selected under the given error threshold by the two algorithms, which greatly saves the cost of setup configuration. The performance of SAAS generally outperform that of DSA, especially when there are fewer probes selected.

In this paper, considering non-ideal power amplifier (PA) and circuit power, we optimize the energy efficiency (EE) of two-way full-duplex relaying (FDR) systems with decode-and-forward (DF) protocol. With higher cooperative gain, the direct link (DL) of the regenerative FDR is decoded at the receiver and can contribute to the useful signal rather than the interference, which can further improve the system performance significantly. With this mechanism, the joint transmit power and duration optimization problem is formulated to maximize the EE of the FDR system, which is proved to be non-convex and cannot be resolved by the standard convex methods. Therefore, a two step optimization algorithm (TSOA) is proposed to solve the original non-convex problem, where the optimality of the solution is ensured due to the rigorously proved convexity. Simulations are carried out to verify the EE performance of our proposed scheme, compared with the existing schemes. It is also revealed that the FDR systems are basically comparable to the half-duplex relaying (HDR) counterparts in terms of EE if the residual self-interference (RSI) is below a certain level, and have the ability to achieve higher data rate demand.

Dynamic time division duplex (TDD) downlink/uplink (DL/UL) reconfiguration has been regarded as a promising technique to enhance the radio resource utilization. However, serious co-channel co-subframe DL-UL interference caused by the opposite transmission direction may weaken the system performance. In this paper, we propose a novel cluster-based enhanced interference mitigation and traffic adaption (eIMTA) scheme, which considers the number of times that the cluster configuration does not meet the transmission demand of the cell. In order to maximize the packet throughput and resource utilization, cells with severe DL-UL interferences and similar transmission demands will be clustered together in our proposed scheme. The balance between traffic adaption flexibility and interference mitigation is obtained. Furthermore, an optimized reconfiguration algorithm is developed to select the configuration that matches the transmission requirements of the cells in cluster best based on the buffered DL/UL traffic ratio. System level simulation results indicate that our cluster-based eIMTA scheme can achieve substantial gains in the cell average packet throughput and cell-edge UE packet throughput.

Machine-type communication (MTC) is endorsed in the fifth-generation (5G) networks to realize innovative IoT based applications, such as smart city and intelligent manufacturing. MTC devices with sensing and communication capabilities can monitor the surrounding environment and transmit the collected information back to Base Station (BS) for further data analysis. The dense deployment of sensing devices calls for a clustering structure to preprocess the redundant data to avoid traffic overload. Moreover, due to limited battery capacity, the energy cost remains a critical concern in such IoT systems. In this paper, we propose an energy-efficient clustering routing algorithm. Considering the non-uniform traffic distribution, we propose an uneven cluster formation scheme for load balancing and energy efficiency. Moreover, we propose a distributed cluster head (CH) rotation mechanism to balance energy consumption within each cluster. As for long distance transmission to BS, we design a dynamic multi-hop routing algorithm among CH nodes based on a proposed distance-and-energy-aware cost function to avoid the energy hole problem. Simulation results show that the performance of our proposed algorithm is competitive in terms of network lifetime, throughput and energy efficiency.

The millimeter-wave band will be one of the most key components in the next generation wireless communication system. In this paper, a radio channel measurement was conducted in an indoor office environment at 28 GHz with 500 MHz bandwidth. The channel sounder with the clock synchronization was used to measure in both line-of-sight (LoS) and none- line-of-sight (NLoS) scenarios. The channel impulse responses (CIRs) are recorded with an omnidirectional antenna at TX and a horizontal-rotating horn antenna fixed at the same height at RX as references. The space-alternating generalized expectation-maximization (SAGE) algorithm was applied to extract the channel characteristics of multipath components (MPCs) from the synthesized CIRs, and then the direct synthesized CIRs and the CIRs constructed from SAGE results were compared in terms of power delay profiles (PDPs) and power angular spread (PAS). It is found that the reconstructed results closely approximate real results. In addition, the cluster numbers and the inner-cluster root mean square (RMS) angle spreads are drawn after the clustering analysis and they cohere with the changes of the corresponding surrounding environment of the point, regardless of LoS or NLoS. The wireless channel propagation at 28 GHz is heavily dependent on the environment because the linear and reflective propagation are the main mode of transmission.

Device-to-device (D2D) communication is a promising method to reduce power consumption and improve the throughput of cellular networks. However, densely deployed D2D pairs could result in severe interference to cellular users without proper power control. Therefore, the discrete location-aware power control (DLPC) scheme is proposed in uplink D2D underlaid cellular networks. The entire cell area is divided into several regions, and a total power budget for each region is conducted with weighted allocation, to meet the constraint on the outage probability of cellular user. Then DLPC scheme requires only the locations of active D2D pairs rather than channel information or massive calculations, which is of low complexity in implementation. Simulation indicates that DLPC can improve the outage probability of D2D pairs and the network throughput compared with the traditional greedy power control scheme.

To dynamically and efficiently utilize the vacant spectrum resources, cognitive radio is proposed as a potential solution, where spectrum sensing is one of the indispensable techniques. As one of the remaining issues in spectrum sensing, how to achieve the fast and energy efficient spectrum sensing in face of a wide band spectrum with an acceptable sensing accuracy is still a big challenge. Therefore, to reduce the spectrum sensing cost, a fast spectrum sensing scheme is proposed in this paper. Firstly, the channels of the same service are classified into highly correlated groups by a modified version of the greedy algorithm for set covering problem. In each group, only one representative channel (RC) is detected and the current busy-idle states of other channels, namely, estimated channels (EC), can be inferred according to their historical states and the current state of RC by the proposed joint Markov and channel correlation algorithm. Based on the real-time measurement results on GSM service and TV service in Beijing, the proposed scheme is proved and verified to be efficient on the premise of low estimated error.

We consider the problem of channel allocation in cognitive radio network (CRN) with the restraints for adjacent-channel interference (ACI) caused by CRN, which is significant in the existing communication system and is easily ignored by researchers. In order to mitigate the effects of the adjacent-channel interference, it is a convenient way to use guard bands in CRN, when cognitive radio (CR) users dynamically exploit the idle spectrum owned by the primary user (PU). This paper focuses on a joint power control and channel assignment in a multi-channel CRN which realizes the use of spectrum effectively under the interference restrictions. Moreover, there are two important aspects of this guard bands method, which are the difference of primary networks and the operational mechanism of CRN, that we take into account. Since the optimization problem is, in general, NP-hard, we apply the coloring theory to reduce the complexity while providing the near-optimal performance. Finally, simulation results are provided, and the detailed numerical results are analyzed.

This paper investigates the cluster dynamic behaviors in an outdoor propagation environment to facilitate the outdoor channel modeling and simulation. To identify the relevant clusters from measured channel data in joint spatial-temporal domain, an automatic cluster identification algorithm has been employed. By introducing a closed loop scheme and a multi-dimension filter, significant improvements on the algorithm performance can be obtained. Furthermore, based on analysis on the cluster identification results, this paper proposes two stochastic models for cluster time-variant characteristics including cluster lifespan and cluster spatial-temporal evolution. Empirical examples reveal the applicability and accuracy of the models of which can be made use in related simulations to imitate the continuous evolution of realistic channels.

Coordinated multi-point (CoMP) has been adopted in 3GPP LTE-Advanced to improve the coverage of high data rates. Downlink CoMP is mainly categorized into joint transmission (JT) and coordinated beamforming (CB). Previous research shows that JT does not benefit cell average throughput. Moreover, traditional CB algorithms are based on iterative evaluation, resulting in very high computational complexity. To address these issues, in this paper, we propose an effective inter-cell interference coordination scheme for downlink CoMP, which includes two phases. Firstly, in order to improve the performance of all user equipments (UEs), cell edge UEs employ JT, while CB is adopted by both cell edge and cell center UEs. Secondly, we design linear CB precoders by maximizing a signal to leakage and noise ratio (SLNR) metric, so as to reduce processing complexity. System-level simulations validate that, in contrast to non-CoMP, the proposed scheme brings a cell average spectral efficiency (SE) gain up to 21.50% and a cell edge SE gain up to 119.2%.

Coordinated Multi-Point (CoMP) transmission is considered in 3GPP LTE-Advanced as a key technique to improve the cell-edge performance. In order to support joint resource allocation among coordinate cells in CoMP systems, efficient frequency reuse schemes need to be designed. However, most of the existing frequency reuse schemes are not suitable for CoMP transmission due to not considering multi-cell joint transmission scenario in their frequency reuse rule. To solve this problem, a cooperative frequency reuse (CFR) scheme is proposed in this paper, which divides the cell-edge area of each cell into two types of zones, and defines a frequency reuse rule to support CoMP transmission for users in these zones. Compared with the conventional soft frequency reuse (SFR) scheme, simulation results demonstrate that the CFR scheme reduces the blocking probability by more than 50%, and improves the cell-edge throughput by 30~40%, with 5~9% additional cell-average throughput.

For the third generation systems' low frequency efficiency and small system capacity, there should be greatly changed in physical techniques in fourth generation mobile communication systems while many advanced techniques suited for high bite rate, high frequency efficiency and large dynamic range, such as JT, STC, MIMO, OFDMA, distributed antenna, are taken into considerations. But besides physical techniques, basic network layer techniques, e.g. the constructions of cellular and the modes of handover, must be broken through in the fourth generation mobile communication systems. The group cell structure proposed in WTI is a novel cellular construction method, which is fit well for these new advanced physical techniques. And slide handover (intelligent group handover) in multicell infrastructure is also novel handover mode emphasized in this paper. According to the current research, the slide handover in multicell infrastructure could improve the system capacity dramatically.

A novel joint-optimization idea for uplink multicarrier CDMA (MC-CDMA) system is presented in this paper. This idea can greatly mitigate the multiple-access interference (MAI) and improve the system performance. Compared to traditional MC-CDMA systems the optimum system has much better bit error rate performance but also large feedback cost. Then some suboptimum systems are introduced which still have good performance similar to the optimum system and less feedback amount.

The joint transmission (JT) technique was mainly presented by Baier et al. (2000). JT used in the downlink channel does not require a training sequence in the downlink in theory. Moreover, the mobile station (MS) does not demand channel estimation. However, JT requires a huge dynamic range and good linearity of the power amplifier of the transmitter because of the high peak to average ratio of transmitted signals of JT. In this paper, a new scheme called pre-distortion based joint transmission is proposed. Two pre-distortion approaches are also presented. One is based on the pre-clip technique, the other on a pre-nonlinear power amplifier. Finally, simulation results show that the pre-distortion based JT scheme has better BER performance and the BER performance of pre-distortion based JT under nonideal power amplifier is better than that of JT under ideal power amplifier. This is to say, maybe it is not worth compensating the deep channel shading.