磁耦合谐振式超导无线电能传输线圈错动对传输效率的影响仿真

超导以其低阻抗的特性,受到了广泛关注和应用.将超导线圈引入无线电能传输系统中,能够提升其传输性能.本文通过有限元仿真软件COMSOL Multiphysics对超导线圈错动对无线电能传输系统传输性能的影响进行了仿真,得出超导无线电能传输性能较之传统的在线圈错动时性能更好,在大功率的使用场合中能更好的适应实际情况,减少损耗,提升传输质量,为超导无线电能传输技术的应用提供了一定的参考依据.     

Multichannel Power Constraints-Based SNR Estimation for the non-cooperative and cooperative scenarios of multi-user Wireless Network

Cooperative communication holds a significant task in Wireless Networks (WNs) by enhancing communication reliability, power, efficiency, and network connectivity. In addition, cooperative communication also enables the progression of a well-organized method for improving the WN quality. Moreover, it facilitates the exploitation of communicating resources by permitting the pathways and nodes in a network to assist in the transmission of data. In this research, a Multi-Channel Power Constraints-Based SNR Estimation (MCPC-SNR Estimation) is considered with source node to destination node and relay node to destination node. Also, three probabilistic models like joint entropy, mutual information, and correlation are considered for SNR parameters. Rather than considering the signals with the current information of signal and channel, we consider signal-to-noise ratio combining the method with static and dynamic Signal to Noise Ratio (SNR) estimation for the non-cooperative and cooperative scenarios. Finally, an assessment of the Multi-Channel Power Constraints-Based SNR Estimation (MCPC-SNR) model shows that the power allocations at the source, as well as relay nodes for transmissions, and the destination and relay nodes, have a notable effect on the Bit Error Rate (BER) performance for power-constrained cooperative communications. The analysis proves that the proposed work ensures a much lower Bit Error Rate (BER) for almost all timing error variations, which probably reaches 0.005.     

Balancing module in evolutionary optimization and Deep Reinforcement Learning for multi-path selection in Software Defined Networks

Software Defined Network (SDN) has been used in many organizations due to its efficiency in transmission. Machine learning techniques have been applied in SDN to improve its efficiency in resource scheduling. The existing models in SDN have limitations of overfitting, local optima trap and lower efficiency in path selection. This study applied Balancing Module (BM)-Spider Monkey Optimization (SMO)-Crow Search Algorithm (CSA) for multi path selection in SDN to improve its efficiency. The balancing module applies Gaussian distribution to balance between exploration and exploitation in the multi-path selection process. The Balancing module helps to escape local optima trap and increases the convergence rate. Deep Reinforcement learning is applied for resource scheduling in SDN. The Deep reinforcement learning technique uses the reward function to improve the learning performance, and the BM-SMO-CSA technique has 30 J energy consumption, where the existing models: DRL has 40 J energy consumption, and Graph-ACO has 62 J energy consumption.     

Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems

In this paper, the optimization of network performance to support the deployment of federated learning (FL) is investigated. In particular, in the considered model, each user owns a machine learning (ML) model by training through its own dataset, and then transmits its ML parameters to a base station (BS) which aggregates the ML parameters to obtain a global ML model and transmits it to each user. Due to limited radio frequency (RF) resources, the number of users that participate in FL is restricted. Meanwhile, each user uploading and downloading the FL parameters may increase communication costs thus reducing the number of participating users. To this end, we propose to introduce visible light communication (VLC) as a supplement to RF and use compression methods to reduce the resources needed to transmit FL parameters over wireless links so as to further improve the communication efficiency and simultaneously optimize wireless network through user selection and resource allocation. This user selection and bandwidth allocation problem is formulated as an optimization problem whose goal is to minimize the training loss of FL. We first use a model compression method to reduce the size of FL model parameters that are transmitted over wireless links. Then, the optimization problem is separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The ultimate user selection and bandwidth allocation are obtained by iteratively compressing the model and solving these two subproblems. Simulation results show that the proposed FL algorithm can improve the accuracy of object recognition by up to 16.7% and improve the number of selected users by up to 68.7%, compared to a conventional FL algorithm using only RF.     

ITER HCSB TBMһά����ѧ�Ż����

ITER实验包层模块(TBM)的中子学问题与系统的氚增殖、热工水力、安全等问题密切相关,因此TBM的中子学优化设计极为重要。在CHHCSBTBM设计描述报告的一维中子学计算模型的基础上,利用一维中子输运计算程序ONEDANT和配套的数据库,以功率密度和氚增殖比等参数为优化目标进行中子学优化设计,得到了更为合理的中子学设计方案。     

AMI通信网络中微功率无线通信的性能测试技术及系统研究

微功率无线通信是高级量测体系(AMI)的主要通信方式之一。各厂家的通信模块性能不一,测试技术尚未形成统一的规范。设计了微功率无线通信的测试系统,由射频性能测试系统和协议一致性分析系统组成。系统采用屏蔽测试箱、多功能电磁波小室、综合测试分析仪、误码分析仪、标准协议信号源等模块组成,屏蔽箱对800MHz频率以下的无线电信号有超过70dB 的抑制,可以提供相对纯净的无线电暗室环境,减少外界无线电波的干扰。测试频率范围为30MHz~1GHz,提供66个测试频点,测试频率误差小于2ppm,误码测试精度小于0.001%,功率测试精度（闭环）为5%（0.2dB）。实现了在实验室情况下对微功率无线网络性能的全面测试和评估,具备很好可操作性、便利性和可复现性。     

基于功率转移函数的全光整形器性能评估

基于输入/输出功率转移函数的线性化三段模型,考察了全光整形器性能的理论评价方法,通过构建一个光纤四波混频整形仿真系统,验证了这种方法的合理性.分析了高斯分布的输入信号对全光整形器输出性能的影响.根据输入信号特性,将转移函数曲线由低到高分为五个区域,当信号"1"的均值落在第五区域时可获得较高的品质因数值和误码率性能的提升.最佳工作状态下,随着转移函数中间段斜率的增加,全光整形器输出信号品质因数值和误码率性能的提升会趋于饱和,但消光比有一个线性的增加.     

面向近红外光谱定量分析的深度学习建模与模型迁移

近红外光谱分析技术依赖于表征光谱向量和预测目标之间关系的化学计量学方法。然而,样品的光谱由信号和各种噪声组成,传统化学计量学方法较难直接提取光谱的有效特征,并为复杂的预测任务建立具有较强泛用性的校正模型。进一步地,受限于仪器间的差异,在一台仪器上建立的模型应用于另一台仪器时,难以取得相同的定量分析结果。为此,提出了一种基于卷积神经网络和迁移学习的定量分析建模及模型传递方案,以提高模型在单仪器和跨仪器上的预测性能。在卷积神经网络的基础上,一种结合多尺度特征融合和残差结构,名为MSRCNN的先进模型被设计,并在主仪器上展现了卓越的预测能力。然后,设计了四种的基于fine-tune模型迁移策略,将在主仪器上建立的MSRCNN模型迁移到从仪器。在药品和小麦的公开数据集上的实验结果表明,MSRCNN在主仪器上的RMSE和R2分别为2.587,0.981和0.309,0.977,优于PLS,SVM和CNN。在利用30个从仪器的样本微调主仪器建立的模型后,迁移MSRCNN中的卷积层和全连接层的方案取得了最好效果,其RMSE和R2可分别达到2.289,0.982和0.379,0.965。增加参与模型微调的从仪器样本,可进一步提高性能。     

Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning

Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a content centric network. Power control and optimal scheduling can significantly improve the wireless multicast network’s performance under fading. However, the model-based approaches for power control and scheduling studied earlier are not scalable to large state spaces or changing system dynamics. In this paper, we use deep reinforcement learning, where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learned for reasonably large systems via this approach. Further, we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-time scale approach to simultaneously learn the optimal queuing strategy along with power control. We demonstrate the scalability, tracking and cross-layer optimization capabilities of our algorithms via simulations. The proposed multi-time scale approach can be used in general large state-space dynamical systems with multiple objectives and constraints, and may be of independent interest.     

Subchannel and power optimization for sum rate maximization in downlink multicarrier NOMA networks

In a multicarrier NOMA system, the subchannel allocation (SA) and power allocation (PA) are intricately linked and essential for improving system throughput. Also, for the successful execution of successive interference cancellations (SIC) at the receiver, a minimum power gap is required among users. As a result, this research comes up with optimization of the SA and PA to maximize the sum rate of the NOMA system while sticking to the minimum power gap constraint in addition to minimum user rate, maximum number of users in a subchannel and power budget constraints for downlink transmission in multicarrier NOMA networks. To ensure that the formulated problem can be solved in polynomial time, we propose solving it in two stages; SA followed by PA. To obtain SA, we investigate four algorithms: Greedy, WSA, WCA, and WCF. For PA, we propose a low-complexity algorithm. We compare the performance of the proposed method with benchmark method that does not consider the minimum power gap constraint. We conclude that employing WCF algorithm with the PA algorithm gives the best sum rate performance.