Adaptive Graph Convolutional Recurrent Neural Networks for System-Level Mobile Traffic Forecasting

Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges due to the tremendous temporal and spatial dynamics introduced by diverse Internet user behaviors and frequent traffic migration.Spatialtemporal graph modeling is an efficient approach for analyzing the spatial relations and temporal trends of mobile traffic in a large system.Previous research may not reflect the optimal dependency by ignoring inter-base station dependency or pre-determining the explicit geological distance as the interrelationship of base stations.To overcome the limitations of graph structure,this study proposes an adaptive graph convolutional network(AGCN)that captures the latent spatial dependency by developing self-adaptive dependency matrices and acquires temporal dependency using recurrent neural networks.Evaluated on two mobile network datasets,the experimental results demonstrate that this method outperforms other baselines and reduces the mean absolute error by 3.7%and 5.6%compared to time-series based approaches.

Mobile Internet Applications：Impact on Future Wireless Communications System

Based on the analysis of application status in real network,the trace model of some typical mobile Internet applications data is given and their impact on 2G/3G network is discussed in this paper.Furthermore,in order to support the mobile Internet application efficiently in future,the issues including the impact on the Long Term Evolution (LTE-A) system and some potential solutions for performance optimization are studied.Based on the trace data model of IM traffic,the performacne evaluaiton of LTE-A system shows that some specific configuration machanisms can play an important role in improving network system efficiency in the case of IM traffic.

Epidemic-Like Proximity-Based Traffic Offloading

Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mobile traffic offloaded from cellular networks.We introduce a gossip-style social cascade(GSC) model to model the epidemic-like information diffusion process in MSNets.For static-case and mobile-case networks,we establish an equivalent view and a temporal mapping of the information diffusion process,respectively.We further prove the submodularity in the information diffusion and propose a greedy algorithm to choose the seed users for traffic offloading,yielding a sub-optimal solution to the NP-hard traffic offloading maximization(TOM) problem.Experiments are carried out to study the offloading performance,illustrating that the greedy algorithm significantly outperforms the heuristic and random algorithms,and user mobility can help further reduce cellular load.

Book Review-5G Network Architecture

5G is the hottest topic in telecommunication area in recent years.ITU has defined 5G as IMT-2020,and presented the scenarios of IMT-2020.These scenarios for IMT-2020 can be concluded as follows：-Enhanced Mobile Broadband：Mobile Broadband addresses the human-centric use cases for access to multi-media content,services and data.The demand for mobile

Cellular traffic offloading utilizing set-cover based caching in mobile social networks

To cope with the explosive data demands, offloading cellular traffic through mobile social networks（MSNs） has become a promising approach to alleviate traffic load. Indeed, the repeated data transmission results in a great deal of unnecessary traffic. Existing solutions generally adopt proactive caching and achieve traffic shifting by exploiting opportunistic contacts. The key challenge to maximize the offloading utility needs leveraging the trade-off between the offloaded traffic and the users＇ delay requirement. Since current caching scheme rarely address this challenge, in this paper, we first quantitatively interpret the offloading revenues on the cellular operator side associated with the scale of caching users, then develop a centralized caching protocol to maximize the offloading revenues, which includes the selective algorithm of caching location based on set-cover, the cached-data dissemination strategy based on multi-path routing and the cache replacement policy based on data popularity. The experimental results on real-world mobility traces show that the proposed caching protocol outperforms existing schemes in offloading scenario.

Optimizing urban traffic control using a rational agent

This paper is devoted to developing and evaluating a set of technologies with the objective of designing a methodology for the implementation of sophisticated traffic lights by means of rational agents. These devices would be capable of optimizing the behavior of a junction with multiple traffic signals, reaching a higher level of autonomy without losing reliability, accuracy, or efficiency in the offered services. In particular, each rational agent in a traffic signal will be able to analyze the requirements and constraints of the road, in order to know its level of demand. With such information, the rational agent will adapt its light cycles with the view of accomplishing more fluid traffic patterns and minimizing the pollutant environmental emissions produced by vehicles while they are stopped at a red light, through using a case-based reasoning(CBR) adaptation. This paper also integrates a microscopic simulator developed to run a set of tests in order to compare the presented methodology with traditional traffic control methods. Two study cases are shown to demonstrate the efficiency of the introduced approach, increasing vehicular mobility and reducing harmful activity for the environment. For instance, in the first scenario, taking into account the studied traffic volumes, our approach increases mobility by 23% and reduces emissions by 35%. When the roads are managed by sophisticated traffic lights, a better level of service and considerable environmental benefits are achieved, demonstrating the utility of the presented approach.