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.

Optimization of nanofiber scaffold properties towards nerve guidance channel design

Nerve guidance channels are limited by lack of topographical guidance：Treatment of sizeable nerve gaps remains problematic following peripheral nerve injury.Functional outcomes are good when neurorrhaphy,or direct end-to-end suture repair,is possible.The problem arises when there is significant segmental loss,which can occur following trauma as well as oncological procedures.

Theoretical and Experimental Optimization of InGaAs Channels in GaAs PHEMT Structure

The ground-state energy level （GEL） and electron distribution of GaAs pseudomorphic high-electron-mobility transistors （PHEMTs） are analyzed by a self-consistent solution to the Schrodinger-Poisson equations. The indium composition and thickness of the InGaAs channel are optimized according to the GEL position. The GEL position is not in direct proportion to 1/d^2 （d is the channel thickness） by considering the influence of electron distribution in the InGaAs channel. Indium composition 0.22 and channel thickness 9 nm are obtained by considering the mismatch between InGaAs and AlGaAs. Several PHEMT samples are grown according to the theoretical results and mobility 6300 cm^2 /V.s is achieved.

Deep Learning Based Signal Detector for OFDM Systems

In this paper,we propose a novel deep learning(DL)-based receiver design for orthogonal frequency division multiplexing(OFDM)systems.The entire process of channel estimation,equalization,and signal detection is replaced by a neural network(NN),and hence,the detector is called a NN detector(N^(2)D).First,an OFDM signal model is established.We analyze both temporal and spectral characteristics of OFDM signals,which are the motivation for DL.Then,the generated data based on the simulation of channel statistics is used for offline training of bi-directional long short-term memory(Bi-LSTM)NN.Especially,a discriminator(F)is added to the input of Bi-LSTM NN to look for subcarrier transmission data with optimal channel gain(OCG),which can greatly improve the performance of the detector.Finally,the trained N^(2)D is used for online recovery of OFDM symbols.The performance of the proposed N^(2)D is analyzed theoretically in terms of bit error rate(BER)by Monte Carlo simulation under different parameter scenarios.The simulation results demonstrate that the BER of N^(2)D is obviously lower than other algorithms,especially at high signal-to-noise ratios(SNRs).Meanwhile,the proposed N^(2)D is robust to the fluctuation of parameter values.

Chanel Assignment Optimization Algorithm Based on Link Throughput and Node Priority

How to reduce interference among neighbor nodes in wireless mesh networks is still an important and key issue nowa- days. In this paper, an optimized channel assignment algorithm （OCA） is proposed to solve this problem based on link throughput and node priority. The effects of the numbers of network interface cards and channels on the network throughput are analyzed and evaluated, When there are seven of the numbers of both network interface cards and channels, the efficiency of utilizing network interface card and channel reaches highest. Compared with cen- tralized channel assignment algorithm （CCA）, the proposed algo- rithm has less packet loss rate and more network throughput sig- nificantly.