WiFi6 Dynamic Channel Optimization Method for Fault Tolerance in Power Communication Network

As the scale of power networks has expanded,the demand for multi-service transmission has gradually increased.The emergence of WiFi6 has improved the transmission efficiency and resource utilization of wireless networks.However,it still cannot cope with situations such as wireless access point(AP)failure.To solve this problem,this paper combines orthogonal fre-quency division multiple access(OFDMA)technology and dynamic channel optimization technology to design a fault-tolerant WiFi6 dynamic resource optimization method for achieving high quality wireless services in a wirelessly covered network even when an AP fails.First,under the premise of AP layout with strong coverage over the whole area,a faulty AP determination method based on beacon frames(BF)is designed.Then,the maximum signal-to-interference ratio(SINR)is used as the principle to select AP reconnection for the affected users.Finally,this paper designs a dynamic access selection model(DASM)for service frames of power Internet of Things(IoTs)and a schedul-ing access optimization model(SAO-MF)based on multi-frame transmission,which enables access optimization for differentiated services.For the above mechanisms,a heuristic resource allocation algorithm is proposed in SAO-MF.Simulation results show that the method can reduce the delay by 15%and improve the throughput by 55%,ensuring high-quality communication in power wireless networks.

Decentralized Heterogeneous Federal Distillation Learning Based on Blockchain

Load forecasting is a crucial aspect of intelligent Virtual Power Plant(VPP)management and ameans of balancing the relationship between distributed power grids and traditional power grids.However,due to the continuous emergence of power consumption peaks,the power supply quality of the power grid cannot be guaranteed.Therefore,an intelligent calculation method is required to effectively predict the load,enabling better power grid dispatching and ensuring the stable operation of the power grid.This paper proposes a decentralized heterogeneous federated distillation learning algorithm(DHFDL)to promote trusted federated learning(FL)between different federates in the blockchain.The algorithm comprises two stages:common knowledge accumulation and personalized training.In the first stage,each federate on the blockchain is treated as ameta-distribution.After aggregating the knowledge of each federate circularly,the model is uploaded to the blockchain.In the second stage,other federates on the blockchain download the trained model for personalized training,both of which are based on knowledge distillation.Experimental results demonstrate that the DHFDL algorithmproposed in this paper can resist a higher proportion of malicious code compared to FedAvg and a Blockchain-based Federated Learning framework with Committee consensus(BFLC).Additionally,by combining asynchronous consensus with the FL model training process,the DHFDL training time is the shortest,and the training efficiency of decentralized FL is improved.