Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks

Reinforcement Learning(RL)techniques are being studied to solve the Demand and Capacity Balancing(DCB)problems to fully exploit their computational performance.A locally gen-eralised Multi-Agent Reinforcement Learning(MARL)for real-world DCB problems is proposed.The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management(ATFM)region to quickly obtain a satisfactory solution.In this method,agents of all flights in a scenario form a multi-agent decision-making system based on partial observation.The trained agent with the customised neural network can be deployed directly on the corresponding flight,allowing it to solve the DCB problem jointly.A cooperation coefficient is introduced in the reward function,which is used to adjust the agent’s cooperation preference in a multi-agent system,thereby controlling the distribution of flight delay time allocation.A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated.Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method.From a statis-tical point of view,it is proven that the proposed method is generalised within the scope of the flights and sectors of interest,and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods.The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.

Delay and Capacity Trade-offs in Mobile Wireless Networks with Infrastructure Support

In this paper,we investigate the trade-offs between delay and capacity in mobile wireless networks with infrastructure support.We consider three different mobility models,independent and identically distributed （i.i.d） mobility model,random walk mobility model with constant speed and L＇evy flight mobility model.For i.i.d mobility model and random walk mobility model with the speed θ（1/n～（1/2））,,we get the theoretical results of the average packet delay when capacityis θ（1）,θ（1/n～（1/2）） individually,where n is the number of nodes.We find that the optimal average packet delay is achieved whencapacity λ（n） 〈（1/（2.n.log_2（1/（（1-e）-（k/n））＋1））,where K is the number of gateways.It is proved that average packet delay D（n） dividedby capacity λ（n） is bounded below by （n/（k·w））.When ω（n～（1/2））≤KO（n（（1-η）·（α＋1））/2）ln n） when K=o（n～η）（0≤η〈1）.We also provethat when ω（1/2）≤K）.

A dynamic network Qo S control mechanism based on traffic prediction

A dynamic network Qo S control mechanism was proposed based on traffic prediction. It first predicts network traffic flow and then dynamically distributes network resources, which makes full use of network flow self-similarity and chaos. So it can meet changing network needs very well. The simulation results show that the dynamic Qo S control mechanism based on prediction has better network performance than that based on measurement.

DAWN: A Density Adaptive Routing for Deadline-Based Data Collection in Vehicular Delay Tolerant Networks

Vehicular Delay Tolerant Networks （DTN） use moving vehicles to sample and relay sensory data for urban areas, making it a promising low-cost solution for the urban sensing and infotainment applications. However, routing in the DTN in real vehicle fleet is a great challenge due to uneven and fluctuant node density caused by vehicle mobility patterns. Moreover, the high vehicle density in urban areas makes the wireless channel capacity an impactful factor to network performance. In this paper, we propose a local capacity constrained density adaptive routing algorithm for large scale vehicular DTN in urban areas which targets to increase the packet delivery ratio within deadline, namely Density Adaptive routing With Node deadline awareness （DAWN）. DAWN enables the mobile nodes awareness of their neighbor density, to which the nodes＇ transmission manners are adapted so as to better utilize the limited capacity and increase the data delivery probability within delay constraint based only on local information. Through simulations on Manhattan Grid Mobility Model and the real GPS traces of 4960 taxi cabs for 30 days in the Beijing city, DAWN is demonstrated to outperform other classical DTN routing schemes in performance of delivery ratio and coverage within delay constraint. These simulations suggest that DAWN is practically useful for the vehicular DTN in urban areas.

基于LTE网络的物联网的随机时延分析(英文)

An Long Term Evolution (LTE) network based mobile Internet of Things (IoT) is modeled and analyzed with the probabilistic delay distribution as the main interest. Stochastic network calculus is relied on to conduct the analysis. Two typical traffic models, i.e., Compound Poisson and Aggregated ON-OFF Source, are analyzed. The wireless fading channel is modeled as a Gilbert-Elliot channel. Numerical results are presented, where the probabilistic delay distribution and guaranteed capacity under certain delay constraint are shown and discussed.