In order to meet the emerging requirements for high computational complexity, low delay and energy consumption of the 5th generation wireless systems (5G) network, ultra-dense networks (UDNs) combined with multi-access edge computing ( MEC) can further improve network capacity and computing capability. In addition, the integration of green energy can effectively reduce the on-grid energy consumption of system and realize green computation. This paper studies the joint optimization of user association (UA) and resource allocation (RA) in MEC enabled UDNs under the green energy supply pattern, users need to perceive the green energy status of base stations (BSs) and choose the one with abundant resources to associate. To minimize the computation cost for all users, the optimization problem is formulated as a mixed integer nonlinear programming (MINLP) which is NP-hard. In order to solve the problem, a deep reinforcement learning ( DRL)-based association and optimized allocation (DAOA) scheme is designed to solve it in two stages. The simulation results show that the proposed scheme has good performance in terms of  computationcost and time out ratio, as well achieve load balancing potentially.

Unmanned aerial vehicle base stations ( UAV-BSs) can provide a fast network deployment scheme for heterogeneous networks. However, unmanned aerial vehicle (UAV) has limited capability and cannot assist the base station (BS) well. The ability of a UAV to assist the BSs is limited, and the cluster deployment relies on the leading UAV. The dispersive deployment of multiple UAVs (multi-UAVs) need a macro base station (MBS) to determine their positions to prevent collisions or interference. Therefore, a distributed cooperative deployment scheme is proposed for UAVs to solve this problem. The scheme can increase the ability of UAVs to assist users and reduce the pressure on BSs to deploy UAVs. Firstly, the randomly distributed users are pre-clustered. Then the placement problem was modeled as a circle expansion problem and a pre-clustering radius expansion algorithm was proposed. Under the constraint of users-data rates, it provides services for more users. Finally, the proposed algorithm was compared with the density-aware placement algorithm. The simulation results show that the proposed algorithm can provide services for more users and improve the coverage rate of users while ensuring the data rates.

A downlink covert communication model that consists of a base station and two legitimate users was considered. In addition to the general signals shared by the two users, the base station will send the covert signals only to one user in a certain time without wanting the other to detect this covert communication behavior. In order to achieve covert communication, two information transmission schemes are designed based on transmission antenna selection (TAS) with the help of artificial noise (AN) transmitted by the user receiving the covert signals, denoted as TAS-Ι and TAS-Πrespectively. Considering the best detection performance of the user only receiving the general signals, under the two schemes, the detection error probabilities and their average values, the connection probabilities, the system covert throughputs are separately calculated. In addition, on the premise of meeting the system’s covert conditions, an optimization scheme is proposed to maximize the covert system throughput. Finally, the simulation

results show that the proposed system can realize covert communication successfully, and the system covert performance under TAS-Ι is better than that under TAS-Π.

Joint user pairing and power allocation approach is investigated to meet the rate requirement of enhanced mobile broadband (eMBB) slicing and delay constraint of ultra-reliable low-latency communication (URLLC) slicing simultaneously in downlink non-orthogonal multiple access ( NOMA) system. For maximizing the proportional fairness among mobile terminals, a two-step algorithm is proposed. For a given user sets, the optimal user pairing sets and the factor of the power allocation in a group were obtained to ensure the quality of service (QoS) and the isolation between different types of slicings. Simulation results show that the proposed joint algorithm can provide better throughput than orthogonal multiple access (OMA).

In heterogeneous wireless networks with time-varying channels, the video rate is usually adjusted based on the network bandwidth to guarantee ultra-low latency video transmission under an end-to-end target delay constraint. However, the target delay with a fixed value according to historical experience cannot guarantee the quality of video continuously since wireless network bandwidth changes rapidly, especially when the network deteriorates. An alternative scheme is to dynamically set the target delay according to the network status within an acceptable delay range. However, this scheme cannot be ensured in heterogeneous wireless networks with time-varying channels. Thus, to address this issue, a multi-objective optimization algorithm for joint optimization of rate control and target delay is proposed, where the target delay and video rate are jointly adjusted dynamically. Second, to reduce the optimization complexity due to the multi-objective and multi-parameter characteristics, multi-objective optimization algorithm be decomposed and solved by optimizing each independent sub-problem. Finally, the proposed algorithm is verified on a semi-physical simulation platform. Experiments show that the frame loss rate is reduced from 6.65% to 2.06%, and a peak signal-to-noise ratio (PSNR) gain of 18.32% is obtained when the network performance is low.

In order to solve the sensing and motion uncertainty problem of motion planning in narrow passage environment, a partition sampling strategy based on partially observable Markov decision process (POMDP) was proposed. The method combines partition sampling strategy and can improve the success rate of the robot motion planning in the narrow passage. Firstly, the environment is divided into open area and narrow area by using a partition sampling strategy, and generates the initial trajectory of the robot with fewer sampling points. Secondly, the method can calculate a local optimal solution of the initial nominal trajectory by solving POMDP problem, and iterates an overall optimal trajectory of robot motion. The proposed method follows the general POMDP solution framework, in which the belief dynamics is approximated by an extended Kalman filter (EKF), and the value function is represented by an effective quadratic function in the belief space near the nominal trajectory. Using a belief space variant of iterative linear quadratic Gaussian (iLQG) to perform the value iteration, which results in a linear control policy over the belief space that is locally optimal around the nominal trajectory. A new nominal trajectory is generated by executing the control strategy iteration, and the process is repeated until it converges to a locally optimal solution. Finally, the robot gets the optimal trajectory to safely pass through a narrow passage. The experimental results show that the proposed method can efficiently improves the performance of motion planning under uncertainty.

Vehicle trajectory modeling is an important foundation for urban intelligent services. Trajectory prediction of cars is a hot topic. A model including convolutional neural network (CNN) and long short-term memory (LSTM) was proposed, which is named trajectory-CNN-LSTM (TCL). CNN can extract the spatial features of the trajectory in the input image. Besides, LSTM can extract the time-series features of the input trajectory. After that, the model uses fully connected layers to merge the two features for the final predicting. The experiments on the Porto dataset of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) show that the average prediction error of TCL is reduced by 0.15 km, 0.42 km, and 0.39 km compared to the trajectory-convolution (T-CONV), multi-layer perceptron (MLP), and recurrent neural network (RNN) model, respectively.

Human motion prediction is a critical issue in human-robot collaboration (HRC) tasks. In order to reduce thelocal error caused by the limitation of the capture range and sampling frequency of the depth sensor, a hybrid human motion prediction algorithm, optimized sliding window polynomial fitting and recursive least squares (OSWPF-RLS) was proposed. The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input, and uses recursive least squares (RLS) to predict the human movement trajectories within the time window. Then, the optimized sliding window polynomial fitting (OSWPF) is used to calculate the multi-step prediction value, and the increment of multi-step prediction value was appropriately constrained. Experimental results show that compared with the existing benchmark algorithms, the OSWPF-RLS algorithm improved the multi-

step prediction accuracy of human motion and enhanced the ability to respond to different human movements.  

Aiming at the shortcomings of current gesture tracking methods in accuracy and speed, based on deep learning You Only Look Once version 4 (YOLOv4) model, a new YOLOv4 model combined with Kalman filter rea-time hand tracking method was proposed. The new algorithm can address some problems existing in hand tracking technology such as detection speed, accuracy and stability. The convolutional neural network (CNN) model YOLOv4 is used to detect the target of current frame tracking and Kalman filter is applied to predict the next position and bounding box size of the target according to its current position. The detected target is tracked by comparing the estimated result with the detected target in the next frame and, finally, the real-time hand movement track is displayed. The experimental results validate the proposed algorithm with the overall success rate of 99.43%

at speed of 41.822 frame/ s, achieving superior results than other algorithms. 

Aiming at the shortcomings of current gesture tracking methods in accuracy and speed, based on deep learning You Only Look Once version 4 (YOLOv4) model, a new YOLOv4 model combined with Kalman filter rea-time hand tracking method was proposed. The new algorithm can address some problems existing in hand tracking technology such as detection speed, accuracy and stability. The convolutional neural network (CNN) model YOLOv4 is used to detect the target of current frame tracking and Kalman filter is applied to predict the next position and bounding box size of the target according to its current position. The detected target is tracked by comparing the estimated result with the detected target in the next frame and, finally, the real-time hand movement track is displayed. The experimental results validate the proposed algorithm with the overall success rate of 99.43%

at speed of 41.822 frame/ s, achieving superior results than other algorithms. 

In order to specify brain temporal dynamics difference between two representative puns, homonymic and semantic puns, alternate presentation of words and phrase ( APWP) paradigm was proposed. The highlight of APWP paradigm is to make sentences strictly presented in word-phrase-word-phrase-word forms, which helps relieve visual  fatigue of the monotonous presentation form and prevent disturbance by the settled position of the ending word. Following the APWP paradigm, participants are invited to read puns presenting in word-phrase-word-phrase-word forms. Meanwhile, event-related potential (ERP) was adopted to record their electroencephalogram (EEG) data. By observing two linguistic cognitive indexes of EEG data, N400 and P600 caused by puns, it was found that there were significant difference of logical mechanisms between homonymic and semantic puns. For homonymic puns, a significant P600 effect without any obvious N400 amplitude was elicited for the pronunciation of heterograph. For semantic puns, an apparent N400 amplitude might reflect ambiguities and comprehensive difficulty of a homonym into its discourse context. This study also conveyed that the APWP paradigm proved to be a good model for sentences research, which can be applied to other linguistic phenomena of complete context, such as metaphor, irony and jokes, sentence pattern and syntactic research.