Common-mode voltage reduction for matrix converters using all valid switch states

This paper presents a new space vector modulation(SVM) strategy for matrix converters to reduce the common-mode voltage (CMV). The reduction is achieved by using the switch states that connect each input phase to a different output phase, or the switch state that connects all the output phases to the input phase with minimum absolute voltage. These two types of states always produce lower peak CMV than the others, especially the former ones that result in zero CMV at the output side of matrix converters. In comparison with the existing SVM methods, this strategy has a very similar software overhead and calculation time. Simulation and experiment results are shown to validate the effectiveness of the proposed modulation method in reducing not only the peak value but also the root-mean-square value of the CMV

Geometrical Visualization of Indirect Space Vector Modulation for Matrix Converters Operating with Abnormal Supplies

Matrix Converters can be sensitive to abnormal supply conditions due to the absence of energy storage elements. This sensitivity can get worse when the Matrix Converters are modulated by a traditional Indirect Space Vector Modulation that assumes the input variables are sinusoidal and balanced. Therefore, this paper proposes a methodology for the modulation of Matrix Converters without requiring any assumption of the input voltages. The method uses a geometric representation based on the Singular Value Decomposition of the switch states to synthesize the rotating vectors of the target duty-cycle matrix. Furthermore, this paper mathematically highlights the factors that have regulate the amplitude of the output voltages and utilizes them to compensate the adverse effect of the abnormal input voltages. Experimental results presented in this paper validate that the proposed method can provide sinusoidal and balanced output currents in the presence of abnormal supply conditions

Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications

Sixth-generation wireless communication (6G) will be an integrated architecture of "space, air, ground and sea". One of the most difficult part of this architecture is the underwater information acquisition which need to transmitt information cross the interface between water and air.In this senario, ocean of things (OoT) will play an important role, because it can serve as a hub connecting Internet of things (IoT) and Internet of underwater things (IoUT). OoT device not only can collect data through underwater methods, but also can utilize radio frequence over the air. For underwater communications, underwater acoustic communications (UWA COMMs) is the most effective way for OoT devices to exchange information, but it is always tormented by doppler shift and synchronization errors. In this paper, in order to overcome UWA tough conditions, a deep neural networks based receiver for underwater acoustic chirp communication, called C-DNN, is proposed. Moreover, to improve the performance of DL-model and solve the problem of model generalization, we also proposed a novel federated meta learning (FML) enhanced acoustic radio cooperative (ARC) framework, dubbed ARC/FML, to do transfer. Particularly, tractable expressions are derived for the convergence rate of FML in a wireless setting, accounting for effects from both scheduling ratio, local epoch and the data amount on a single node.From our analysis and simulation results, it is shown that, the proposed C-DNN can provide a better BER performance and lower complexity than classical matched filter (MF) in underwater acoustic communications scenario. The ARC/FML framework has good convergence under a variety of channels than federated learning (FL). In summary, the proposed ARC/FML for OoT is a promising scheme for information exchange across water and air

Distributed Interference-Aware Cooperative MAC based on Stackelberg Pricing Game

International audienceCooperative communication can significantly enhance the efficient utilization of spectrum in wireless networks.The performance of wireless cooperative networks depends on careful medium access control and resource allocation such asrelay selection and cooperative link scheduling policy for interference management. Most of the previous centralized worksrely on precise instantaneous channel state information (CSI). In this paper, considering the impact of multi-relay linksinterference on relay selection and dynamic spectrum sharing, we propose a fully distributed relay medium access control (MAC)scheme for wireless cooperative networks based on Stackelberg game framework with only one-hop local CSI, where source nodesthat have their own information to send act as leader users and the others act as follower users. In this scenario, each relay nodeprices its spectrum resource to the corresponding source node to maximize its revenue. In addition, the proposed scheme not onlyhelps source nodes to find relay nodes by channel gains and obtain an optimal system performance, but also helps relay nodes tomaximize their revenue by asking suitable resource price. The interactions between the sources and relays leads iteratively to aStackelberg equilibrium, in which the cooperation system achieves high throughput and low transmission delay. Simulationresults are presented to show the effectiveness of the proposed approach

A Deadline-Aware and Distance-Aware Packet Scheduling Algorithm for Wireless Multimedia Sensor Networks

We propose a wireless differentiated queuing service (WDQS) algorithm to meet the diverse delay requirements in wireless multimedia sensor networks (WMSNs). WDQS adopts novel latest departure time (LDT) scheduling criteria to differentiate forwarding emergency by considering the packets’ lifetime, the known delay it has already experienced, and the remaining delay it will experience. We also propose an effective approach to estimate the unknown delay for the remaining journey without any message overhead by exploiting the query mechanism of the sink. We further discuss analytically the packet's lifetime setting to meet the end-to-end (e2e) delivery requirement. The simulation results verify our analytical discussion and show performance improvements in terms of e2e delay and packet drop rate

Landau Levels as a Probe for Band Topology in Graphene Moire Superlattices

We propose Landau levels as a probe for the topological character of electronic bands in two-dimensional moire superlattices. We consider two configurations of twisted double bilayer graphene (TDBG) that have very similar band structures, but show different valley Chern numbers of the flat bands. These differences between the AB-AB and AB-BA configurations of TDBG clearly manifest as different Landau level sequences in the Hofstadter butterfly spectra calculated using the tight-binding model. The Landau level sequences are explained from the point of view of the distribution of orbital magnetization in momentum space that is governed by the rotational C-2 and time-reversal T symmetries. Our results can be readily extended to other twisted graphene multilayers and h-BN/graphene heterostructures thus establishing the Hofstadter butterfly spectra as a powerful tool for detecting the nontrivial valley band topology