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.

A perceptual and predictive batch-processing memory scheduling strategy for a CPU-GPU heterogeneous system

When multiple central processing unit(CPU)cores and integrated graphics processing units(GPUs)share off-chip main memory,CPU and GPU applications compete for the critical memory resource.This causes serious resource competition and has a negative impact on the overall performance of the system.We describe the competition for shared-memory resources in a CPU-GPU heterogeneous multi-core architecture,and a sharedmemory request scheduling strategy based on perceptual and predictive batch-processing is proposed.By sensing the CPU and GPU memory request conditions in the request buffer,the proposed scheduling strategy estimates the GPU latency tolerance and reduces mutual interference between CPU and GPU by processing CPU or GPU memory requests in batches.According to the simulation results,the scheduling strategy improves CPU performance by8.53%and reduces mutual interference by 10.38%with low hardware complexity.