In order to improve the anomaly detection efficiency of network traffic, firstly, the model is established for network flows based on complex networks. Aiming at the uncertainty and fuzziness between network traffic characteristics and network states, the deviation extent is measured from the normal network state using deviation interval uniformly, and the intuitionistic fuzzy sets (IFSs) are established for the various characteristics on the network model that the membership degree, non-membership degree and hesitation margin of the IFSs are used to quantify the ownership of values to be tested and the corresponding network state. Then, the knowledge measure (KM) is introduced into the intuitionistic fuzzy weighted geometry (IFWG_ω) to weight the results of IFSs corresponding to the same network state with different characteristics together to detect network anomaly comprehensively. Finally, experiments are carried out on different network traffic datasets to analyze the evaluation indicators of network characteristics by our method, and compare with other existing anomaly detection methods. The experimental results demonstrate that the changes of various network characteristics are inconsistent under abnormal attack, and the accuracy of anomaly detection results obtained by our method is higher, verifying our method has a better detection performance.

Solar power is the most widely used renewable energy source, which reduces pollution consequences from using conventional fossil fuels. However, supplying stable power from solar power generation remains challenging because it is difficult to forecast power generation. Accurate prediction of solar power generation would allow effective control of the amount of electricity stored in batteries, leading in a stable supply of electricity. Although the number of power plants is increasing, building a solar power prediction model for a newly constructed power plant usually requires collecting a new training dataset for the new power plant, which takes time to collect a sufficient amount of data. This paper aims to develop a highly accurate solar power prediction model for multiple power plants available for both new and existing power plants. The proposed method trains the model on existing multiple power plants to generate a general prediction model, and then uses it for a new power plant while waiting for the data to be collected. In addition, the proposed method tunes the general prediction model on the newly collected dataset and improves the accuracy for the new power plant. We evaluated the proposed method on 55 power plants in Japan with the dataset collected for two and a half years. As a result, the pre-trained models of our proposed method significantly reduces the average RMSE of the baseline method by 73.19%. This indicates that the model can generalize over multiple power plants, and training using datasets from other power plants is effective in reducing the RMSE. Fine-tuning the pre-trained model further reduces the RMSE by 8.12%.

This paper introduces a deep reinforcement learning approach to solve the virtual network function scheduling problem in dynamic scenarios. We formulate an integer linear programming model for the problem in static scenarios. In dynamic scenarios, we define the state, action, and reward to form the learning approach. The learning agents are applied with the asynchronous advantage actor-critic algorithm. We assign a master agent and several worker agents to each network function virtualization node in the problem. The worker agents work in parallel to help the master agent make decision. We compare the introduced approach with existing approaches by applying them in simulated environments. The existing approaches include three greedy approaches, a simulated annealing approach, and an integer linear programming approach. The numerical results show that the introduced deep reinforcement learning approach improves the performance by 6-27% in our examined cases.

In this paper, we investigate the sum rate (SR) maximization problem for downlink cooperative non-orthogonal multiple access (C-NOMA) system under in-phase and quadrature-phase (IQ) imbalance at the base station (BS) and destination. The BS communicates with users by a half-duplex amplified-and-forward (HD-AF) relay under imperfect IQ imbalance. The sum rate maximization problem is formulated as a non-convex optimization with the quality of service (QoS) constraint for each user. We first use the variable substitution method to transform the non-convex SR maximization problem into an equivalent problem. Then, a joint power and rate allocation algorithm is proposed based on successive convex approximation (SCA) to maximize the SR of the systems. Simulation results verify that the algorithm can improve the SR of the C-NOMA compared with the cooperative orthogonal multiple access (C-OMA) scheme.

In this paper, we investigate the Access Point (AP) selection problem in Cell-Free Massive multiple-input multiple-output (MIMO) system. Firstly, we add a connecting coefficient to the uplink data transmission model. Then, the problem of AP selection is formulated as a discrete combinatorial optimization problem which can be dealt with by the particle swarm algorithm. However, when the number of optimization variables is large, the search efficiency of the traditional particle swarm algorithm will be significantly reduced. Then, we propose an ‘user-centric’ cooperative coevolution scheme which includes the proposed probability-based particle evolution strategy and random-sampling-based particle evaluation mechanism to deal with the search efficiency problem. Simulation results show that proposed algorithm has better performance than other existing algorithms.

In this paper, user set selection in the allocation sequences of round-robin (RR) scheduling for distributed antenna transmission with block diagonalization (BD) pre-coding is proposed. In prior research, the initial phase selection of user equipment allocation sequences in RR scheduling has been investigated. The performance of the proposed RR scheduling is inferior to that of proportional fair (PF) scheduling under severe intra-cell interference. In this paper, the multi-input multi-output technology with BD pre-coding is applied. Furthermore, the user equipment (UE) sets in the allocation sequences are eliminated with reinforcement learning. After the modification of a RR allocation sequence, no estimated throughput calculation for UE set selection is required. Numerical results obtained through computer simulation show that the maximum selection, one of the criteria for initial phase selection, outperforms the weighted PF scheduling in a restricted realm in terms of the computational complexity, fairness, and throughput.

High-accuracy wideband signal transmission is essential for 5G and Beyond wireless communication systems. Memory nonlinearity in transmitters is a serious issue for the goal, because it deteriorates the quality of signal and lowers the system performance. This paper studies a post-reception nonlinear compensation (PRC) schemes consisting of frequency domain equalizers (FDEs) and a blind nonlinear compensator (BNLC). A frequency-domain memory nonlinearity modeling approach is employed, and several PRC configurations with FDEs and BNLC are evaluated through computer simulations. It is concluded that the proposed PRC schemes can effectively compensate memory nonlinearity in wideband transmitters via frequency-selective propagation channel. By implementing the PRC in a base station, uplink performance will be enhanced without any additional cost and power consumption in user terminals.

Autonomous driving technology is expected to be applied to various applications with unmanned vehicles (UVs), such as small delivery vehicles for office supplies and smart wheelchairs. UV remote control by a cloud server (CS) would achieve cost-effective applications with a large number of UVs. In general, dead time in real-time feedback control reduces the control accuracy. On remote path tracking control by the CS, UV control accuracy deteriorates due to transmission delay and jitter through the Internet. Digital twin computing (DTC) and jitter buffer are effective to solve this problem. In our previous study, we clarified effectiveness of them in UV remote control by CS. The jitter buffer absorbs the transmission delay jitter of control signals. This is effective to achieve accurate UV remote control. Adaptive buffering time optimization according to real-time transmission characteristics is necessary to achieve more accurate UV control in CS-based remote control system with DTC and jitter buffer. In this study, we proposed a method for the adaptive optimization according to real-time transmission delay characteristics. To quantitatively evaluate the effectiveness of the method, we created a UV remote control simulator of the control system. The results of simulations quantitatively clarify that the adaptive optimization by the proposed method improves the UV control accuracy.