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.

Dynamic channel reservation scheme based on priorities in LEO satellite systems

According to low earth orbit（LEO） satellite systems with users of different levels, a dynamic channel reservation scheme based on priorities is proposed. Dynamic calculation of the thresholds for reserved channels is the key of this strategy. In order to obtain the optimal thresholds, the traffic is predicted based on the high-speed deterministic movement property of LEO satellites firstly. Then, a channel allocation model based on Markov is established. Finally, the solution of the model is obtained based on the genetic algorithm. Without user location, this strategy effectively reduces handover failures and improves channel utilization by adjusting dynamically the thresholds according to traffic conditions. The simulation results show that the system＇s overall quality of service can be improved by this strategy.

The Dynamic Channel Allocation Scheme Based on Stratification and Simulated Annealing Method

This paper proposes a dynamic channel allocation scheme based on cognitive radio （CR）. Firstly, the channel probing based on MMSE criterion is implemented, with which the probability distribution of channels in use by the primary user is given. Next, take the distances between the CR users and the primary user as basis to stratify the CR users, among the layers; the simulated annealing （SA） algorithm is used to implement the channel assigmnent. This algorithm differs from the well-known 0-1 matrix based allocation scheme, and keeps a good tradeoff between complexity, capacity as well as the fairness problems. The simulation results show that this algorithm can improve the allocation efficiency effectively.

Dynamic Channel Allocation in Sectored Cellular Systems

It is known that dynamic channel assignment(D CA ) strategy outperforms the fixed channel assignment(FCA) strategy in omni-direc tional antenna cellular systems. One of the most important methods used in DCA w as channel borrowing. But with the emergence of cell sectorization and spatial d ivision multiple access(SDMA) which are used to increase the capacity of cel lular systems, the channel assignment faces a series of new problems. In this pa per, a dynamic channel allocation scheme based on sectored cellular systems is p roposed. By introducing intra-cell channel borrowing (borrowing channels from n eighboring sectors) and inter-cell channel borrowing (borrowing channels from n eighboring cells) methods, previous DCA strategies, including compact pattern ba sed channel borrowing(CPCB) and greedy based dynamic channel assignment(GDCA) schemes proposed by the author, are improved significantly. The computer simu lation shows that either intra-cell borrowing scheme or inter-cell borrowing s cheme is efficient enough to uniform and non-uniform traffic service distributi ons.

Dynamic MmWave Channel Reconstruction of MIMO OTA Testing for Handover Scenarios

The evaluation of handover performance is essential for ensuring seamless user experience under innovative application scenarios in the fifth generation(5G)and beyond era,including autonomous driving,mobile augmented and virtual reality.However,due to the hardware constrains of a sectored multiprobe anechoic chamber(SMPAC),switching among multiple channel models is of low precision with a high cost in traditional over-the-air(OTA)test solutions.In this paper,we present an efficient and repeatable emulation strategy to reconstruct dynamic millimeter-wave(mm Wave)channels in laboratories for multiple-input multiple-output(MIMO)mobile devices.Firstly,we propose a novel evaluation metric,called average power angular spectrum similarity percentage(APSP),which minimizes the unexpected impact induced by the indefinite condition of adaptive antenna arrays in mm Wave terminals during handover process.Moreover,we propose a partitioned probe configuration strategy by designing a beam directivitybased switching circuit,which enables quick changes of probe configurations in SMPAC.Simulation results demonstrate the effectiveness of the proposed algorithms,thus providing a guideline for the reconstruction of the dynamic channel in different scenarios with resource limitation.

Q-learning for dynamic channel assignment in cognitive wireless local area network with fibre-connected distributed antennas

Cognitive wireless local area network with fibre-connected distributed antennas （CWLAN-FDA） is a promising and efficient architecture that combines radio over fiber, cognitive radio and distributed antenna technologies to provide high speed/high capacity wireless access at a reasonable cost. In this paper, a Q-learning approach is applied to implement dynamic channel assignment （DCA） in CWLAN-FDA. The cognitive access points （CAPs） select and assign the best channels among the industrial, scientific, and medical （ISM） band for data packet transmission, given that the objective is to minimize external interference and acquire better network-wide performance. The Q-learning method avoids solving complex optimization problem while being able to explore the states of a CWLAN-FDA system during normal operations. Simulation results reveal that the proposed strategy is effective in reducing outage probability and improving network throughput.

Langevin approach with rescaled noise for stochastic channel dynamics in Hodgkin–Huxley neurons

The Langevin approach has been applied to model the random open and closing dynamics of ion channels. It has long been known that the gate-based Langevin approach is not sufficiently accurate to reproduce the statistics of stochastic channel dynamics in Hodgkin–Huxley neurons. Here, we introduce a modified gate-based Langevin approach with rescaled noise strength to simulate stochastic channel dynamics. The rescaled independent gate and identical gate Langevin approaches improve the statistical results for the mean membrane voltage, inter-spike interval, and spike amplitude.

Dynamic Expression of Hyperpolarization-activated Cyclic Nucleotide-gated Cation Channel 4 Involved in Microwave Induced Pacemaker Cell Injuries

To investigate the mechanisms of microwave induced pacemaker cell injuries, Wistar rats and the primary pacemaker cells of newborn Wistar rats were exposed to microwave at average power density of 50 mW/cm2. Slower spontaneous beating rate, intercellular Ca2＋ aggregation and cell membrane perforation were detected immediately after the exposure. Moreover, hyperpolarizationactivated cyclic nucleotide-gated cation channel 4 （HCN4） was down-regulated immediately after the exposure and up-regulated at 12 h after the exposure. In the sinoatrial node （SAN） of the rats,

Traffic Modeling and Analysis of Cellular Networks with Dynamic Channel Allocation

Dynamic channel allocation can reduce the blocking probability of cellular networks. This paper aims atestimating the blocking probability for cellular networki with dynamic channel allocation. Traffic analysis modelsare presented to evaluate performance of cellular networkS, The blocking probabilities versus traffic offered to eachcell are analyzed and simulated Comparisons between analysis and simulation results are made.

Dynamic random channel reservation protocol

Demand assignment MAC protocols have been used widely in wireless networks. It can effectively utilize wireless bandwidth. Some strategies can he used by demand assignment MAC protocols to further improve their efficiency. The concept of transmit probability is introduced. This concept allows a request slot to be assigned to many different traffic classes at the same time. Based on it, the dynamic random channel reservation （DRCR） protocol is proposed. The DRCR protocol operates dynamically by observing the traffic conditions. It uses information about the recent traffic conditions to assign transmit probability with which an mobile station can select request slots with lower traffic. The performance of DRCR is evaluated and compared with RSCA. The results show that DRCR is more stable than RSCA, it offers shorter delays of requests than RSCA and can relieve heavily stressed traffic classes faster than RSCA.

Frequency Switches at Transition Temperature in Voltage-Gated Ion Channel Dynamics of Neural Oscillators

Understanding of the mechanisms of neural phase transitions is crucial for clarifying cognitive processes in the brain. We investigate a neural oscillator that undergoes different bifurcation transitions from the big saddle homoclinic orbit type to the saddle node on an invariant circle type, and the saddle node on an invariant circle type to the small saddle homoclinic orbit type. The bifurcation transitions are accompanied by an increase in thermodynamic temperature that affects the voltage-gated ion channel in the neural oscillator. We show that nonlinear and thermodynamical mechanisms are responsible for different switches of the frequency in the neural oscillator. We report a dynamical role of the phase response curve in switches of the frequency, in terms of slopes of frequency-temperature curve at each bifurcation transition. Adopting the transition state theory of voltagegated ion channel dynamics, we confirm that switches of the frequency occur in the first-order phase transition temperature states and exhibit different features of their potential energy derivatives in the ion channel. Each bifurcation transition also creates a discontinuity in the Arrhenius plot used to compute the time constant of the ion channel.

Deep Learning-Based Symbol Detection for Time-Varying Nonstationary Channels

The highly dynamic channel(HDC)in an extremely dynamic environment mainly has fast timevarying nonstationary characteristics.In this article,we focus on the most difficult HDC case,where the channel coherence time is less than the symbol period.To this end,we propose a symbol detector based on a long short-term memory(LSTM)neural network.Taking the sampling sequence of each received symbol as the LSTM unit's input data has the advantage of making full use of received information to obtain better performance.In addition,using the basic expansion model(BEM)as the preprocessing unit significantly reduces the number of neural network parameters.Finally,the simulation part uses the highly dynamic plasma sheath channel(HDPSC)data measured from shock tube experiments.The results show that the proposed BEM-LSTM-based detector has better performance and does not require channel estimation or channel model information.

Cooperative Channel Assignment for VANETs Based on Dual Reinforcement Learning

Dynamic channel assignment(DCA)is significant for extending vehicular ad hoc network(VANET)capacity and mitigating congestion.However,the un-known global state information and the lack of centralized control make channel assignment performances a challenging task in a distributed vehicular direct communication scenario.In our preliminary field test for communication under V2X scenario,we find that the existing DCA technology cannot fully meet the communication performance requirements of VANET.In order to improve the communication performance,we firstly demonstrate the feasibility and potential of reinforcement learning(RL)method in joint channel selection decision and access fallback adaptation design in this paper.Besides,a dual reinforcement learning(DRL)-based cooperative DCA(DRL-CDCA)mechanism is proposed.Specifically,DRL-CDCA jointly optimizes the decision-making behaviors of both the channel selection and back-off adaptation based on a multi-agent dual reinforcement learning framework.Besides,nodes locally share and incorporate their individual rewards after each communication to achieve regional consistency optimization.Simulation results show that the proposed DRL-CDCA can better reduce the one-hop packet delay,improve the packet delivery ratio on average when compared with two other existing mechanisms.

DYNAMIC RESOURCE ALLOCATION SCHEME UNDER TRAFFIC CONDITION IN SATELLITE SYSTEMS

As the traffic distribution in China mainland is far from uniform, a new traffic model in China mainland is presented on the basis of per-capita Gross Domestic Product (GDP) and density of population. Based on this characteristic traffic model, a new Traffic Dependent Dynamic Channel Allocation and Reservation (TDDCAR) technique is proposed, the simulation model is built, and the strategies' performance is evaluated through computer simulation. The simulation results show that, compared to the conventional Fixed Channel Allocation (FCA), TDDCAR estimates the traffic conditions in every spot beam and frequently adjusts the traffic according to current traffic conditions. It has achieved a significant improvement in new call blocking probability, handover blocking probability, and fair index, particularly, in heavy traffic conditions. The building of traffic model in China mainland and the analysis of the simulation results has been a key foundation for the study of resource allocation schemes in the future.

Structural ensemble dynamics based closure model for wall-bounded turbulent flow

Wall-bounded turbulent flow involves the development of multi-scale turbulent eddies, as well as a sharply varying boundary layer. Its theoretical descriptions are yet phenomenological. We present here a new framework called structural ensemble dynamics （SED）, which aims at using systematically all relevant statistical properties of turbulent structures for a quantitative description of ensemble means. A new set of closure equations based on the SED approach for a turbulent channel flow is presented. SED order functions are defined, and numerically determined from data of direct numerical simulations （DNS）. Computational results show that the new closure model reproduces accurately the solution of the original Navier-Stokes simulation, including the mean velocity profile, the kinetic energy of the streamwise velocity component, and every term in the energy budget equation. It is suggested that the SED-based studies of turbulent structure builds a bridge between the studies of physical mechanisms of turbulence and the development of accurate model equations for engineering predictions.

Analysis of dynamic wave model for flood routing in natural rivers

Flooding is a common natural disaster that causes enormous economic, social, and human losses. Of various flood routing methods, the dynamic wave model is one of the best approaches for the prediction of the characteristics of floods during their propagations in natural rivers because all of the terms of the momentum equation are considered in the model. However, no significant research has been conducted on how the model sensitivity affects the accuracy of the downstream hydrograph. In this study, a comprehensive analysis of the input parameters 9f the dynamic wave model was performed through field applications in natural rivers and routing experiments in artificial channels using the graphical multi-parametric sensitivity analysis （GMPSA）. The results indicate that the effects of input parameter errors on the output results are more significant in special situations, such as lower values of Manning＇s roughness coefficient and/or a steeper bed slope on the characteristics of a design hydrograph, larger values of the skewness factor and/or time to peak on the channel characteristics, larger values of Manning＇s roughness coefficient and/or the bed slope on the space step, and lower values of Manning＇s roughness coefficient and/or a steeper bed slope on the time step and weighting factor.

High-efficiency calculation method for watershed rainfallrunoff routing using one-dimensional dynamic wave equations

Efficiency in solving the Saint-Venant equations for watershed rainfall-runoff routing is important in flood hydrology. This paper presents a high-efficiency numerical solution of one-dimensional dynamic wave equations(HEDWE) for watershed rainfall-runoff routing, in which the full momentum equation is written as a quadratic equation with only one unknown variable Q, water depth is derived from the continuity equation using the two-step predictorcorrector method, and the discrete scheme is the explicit upwind scheme. The results of numerical tests showed the HEDWE approach has several major advantages. 1) It is a stable numerical method, even for an initially dry area. 2) Its computational efficiency is higher than 4.76E+05 times/s. 3) It can be used for overland flow, river flow, and combinations thereof. The primary disadvantages of the HEDWE approach are its unsuitability for rapidly varying flow, such as dam-break floods.

Learning-Based Admission Control for Low-Earth-Orbit Satellite Communication Networks

Satellite communications has been regarded as an indispensable technology for future mobile networks to provide extremely high data rates,ultra-reliability,and ubiquitous coverage.However,the high dynamics caused by the fast movement of low-earth-orbit(LEO)satellites bring huge challenges in designing and optimizing satellite communication systems.Especially,admission control,deciding which users with diversified service requirements are allowed to access the network with limited resources,is of paramount importance to improve network resource utilization and meet the service quality requirements of users.In this paper,we propose a dynamic channel reservation strategy based on the Actor-Critic algorithm(AC-DCRS)to perform intelligent admission control in satellite networks.By carefully designing the longterm reward function and dynamically adjusting the reserved channel threshold,AC-DCRS reaches a long-run optimal access policy for both new calls and handover calls with different service priorities.Numerical results show that our proposed AC-DCRS outperforms traditional channel reservation strategies in terms of overall access failure probability,the average call success rate,and channel utilization under various dynamic traffic conditions.

Cooperative channel assignment for VANETs based on multiagent reinforcement learning

Dynamic channel assignment(DCA)plays a key role in extending vehicular ad-hoc network capacity and mitigating congestion.However,channel assignment under vehicular direct communication scenarios faces mutual influence of large-scale nodes,the lack of centralized coordination,unknown global state information,and other challenges.To solve this problem,a multiagent reinforcement learning(RL)based cooperative DCA(RLCDCA)mechanism is proposed.Specifically,each vehicular node can successfully learn the proper strategies of channel selection and backoff adaptation from the real-time channel state information(CSI)using two cooperative RL models.In addition,neural networks are constructed as nonlinear Q-function approximators,which facilitates the mapping of the continuously sensed input to the mixed policy output.Nodes are driven to locally share and incorporate their individual rewards such that they can optimize their policies in a distributed collaborative manner.Simulation results show that the proposed multiagent RL-CDCA can better reduce the one-hop packet delay by no less than 73.73%,improve the packet delivery ratio by no less than 12.66%on average in a highly dense situation,and improve the fairness of the global network resource allocation.

Theoretical and simulation studies on voltage-gated sodium channels

Voltage-gated sodium （Nav） channels are indispensable membrane elements for the generation and propagation of electric signals in excitable cells. The successes in the crystallographic studies on prokaryotic Nay chan- nels in recent years greatly promote the mechanistic investigation of these proteins and their eukaryotic counterparts. In this paper, we mainly review the pro- gress in computational studies, especially the simula- tion studies, on these proteins in the past years.