Learning Decentralized Wireless Resource Allocations with Graph Neural Networks

We consider the broad class of decentralized optimal resource allocation problems in wireless networks, which can be formulated as a constrained statistical learning problems with a localized information structure. We develop the use of Aggregation Graph Neural Networks (Agg-GNNs), which process a sequence of delayed and potentially asynchronous graph aggregated state information obtained locally at each transmitter from multi-hop neighbors. We further utilize model-free primal-dual learning methods to optimize performance subject to constraints in the presence of delay and asynchrony inherent to decentralized networks. We demonstrate a permutation equivariance property of the resulting resource allocation policy that can be shown to facilitate transference to dynamic network configurations. The proposed framework is validated with numerical simulations that exhibit superior performance to baseline strategies.Comment: 13 pages, 13 figure

Études des systèmes de communications sans-fil dans un environnement rural difficile

Les systèmes de communication sans fil, ayant de nombreux avantages pour les zones rurales, peuvent aider la population à bien s'y établir au lieu de déménager vers les centres urbains, accentuant ainsi les problèmes d’embouteillage, de pollution et d’habitation. Pour une planification et un déploiement efficace de ces systèmes, l'atténuation du signal radio et la réussite des liens d’accès doivent être envisagées. Ce travail s’intéresse à la provision d’accès Internet sans fil dans le contexte rural canadien caractérisé par sa végétation dense et ses variations climatiques extrêmes vu que les solutions existantes sont plus concentrées sur les zones urbaines. Pour cela, nous étudions plusieurs cas d’environnements difficiles affectant les performances des systèmes de communication. Ensuite, nous comparons les systèmes de communication sans fil les plus connus. Le réseau sans fil fixe utilisant le Wi-Fi ayant l’option de longue portée est choisi pour fournir les communications aux zones rurales. De plus, nous évaluons l'atténuation du signal radio, car les modèles existants sont conçus, en majorité, pour les technologies mobiles en zones urbaines. Puis, nous concevons un nouveau modèle empirique pour les pertes de propagation. Des approches utilisant l’apprentissage automatique sont ensuite proposées, afin de prédire le succès des liens sans fil, d’optimiser le choix des points d'accès et d’établir les limites de validité des paramètres des liens sans fil fiables. Les solutions proposées font preuve de précision (jusqu’à 94 % et 8 dB RMSE) et de simplicité, tout en considérant une multitude de paramètres difficiles à prendre en compte tous ensemble avec les solutions classiques existantes. Les approches proposées requièrent des données fiables qui sont généralement difficiles à acquérir. Dans notre cas, les données de DIGICOM, un fournisseur Internet sans fil en zone rurale canadien, sont utilisées. Wireless communication systems have many advantages for rural areas, as they can help people settle comfortably and conveniently in these regions instead of relocating to urban centers causing various overcrowding, habitation, and pollution problems. For effective planning and deployment of these technologies, the attenuation of the radio signal and the success of radio links must be precisely predicted. This work examines the provision of wireless internet access in the Canadian rural context, characterized by its dense vegetation and its extreme climatic variations, since existing solutions are more focused on urban areas. Hence, we study several cases of difficult environments affecting the performances of communication systems. Then, we compare the best-known wireless communication systems. The fixed wireless network using Wi-Fi, having the long-range option, is chosen to provide wireless access to rural areas. Moreover, we evaluate the attenuation of the radio signal, since the existing path loss models are generally designed for mobile technologies in urban areas. Then, we design a new path loss empirical model. Several approaches are then proposed by using machine learning to predict the success of wireless links, optimize the choice of access points and establish the validity limits for the pertinent parameters of reliable wireless connections. The proposed solutions are characterized by their accuracy (up to 94% and 8 dB RMSE) and simplicity while considering a wide range of parameters that are difficult to consider all together with conventional solutions. These approaches require reliable data, which is generally difficult to acquire. In our case, the dataset from DIGICOM, a rural Canadian wireless internet service provider, is used

Content Caching and Delivery in Heterogeneous Vehicular Networks

Connected and automated vehicles (CAVs), which enable information exchange and content delivery in real time, are expected to revolutionize current transportation systems for better driving safety, traffic efficiency, and environmental sustainability. However, the emerging CAV applications such as content delivery pose stringent requirements on latency, throughput, reliability, and global connectivity. The current wireless networks face significant challenges to satisfy the requirements due to scarce radio spectrum resources, inflexibility to dynamic traffic demands, and geographic-constrained fixed infrastructure deployment. To empower multifarious CAV content delivery, heterogeneous vehicular networks (HetVNets), which integrate the terrestrial networks with aerial networks formed by unmanned aerial vehicles (UAVs) and space networks constituting of low Earth orbit (LEO) satellites, can guarantee reliable, flexible, cost-effective, and globally seamless service provisioning. In addition, edge caching is a promising solution to facilitate content delivery by caching popular files in the HetVNet access points (APs) to relieve the backhaul traffic with a lower delivery delay. The main technical issues are: 1) to fully reveal the potential of HetVNets for content delivery performance enhancement, content caching scheme design in HetVNets should jointly consider network characteristics, vehicle mobility patterns, content popularity, and APs’ caching capacities; 2) to fully exploit the controllable mobility and agility of UAVs to support dynamic vehicular content demands, the caching scheme and trajectory design for UAVs should be jointly optimized, which has not been well addressed due to their intricate inter-coupling relationships; and 3) for caching-based content delivery in HetVNets, a cooperative content delivery scheme should be designed to enable the cooperation among different network segments with ingenious utilization of heterogeneous network resources. In this thesis, we design the content caching and delivery schemes in the caching-enabled HetVNet to address the three technical issues. First, we study the content caching in HetVNets with fixed terrestrial APs including cellular base stations (CBSs), Wi-Fi roadside units (RSUs), and TV white space (TVWS) stations. To characterize the intermittent network connection caused by limited network coverage and high vehicle mobility, we establish an on-off model with service interruptions to describe the vehicular content delivery process. Content coding then is leveraged to resist the impact of unstable network connections and enhance caching efficiency. By jointly considering file characteristics and network conditions, the content placement is formulated as an integer linear programming (ILP) problem. Adopting the idea of the student admission model, the ILP problem is then transformed into a many-to-one matching problem between content files and HetVNet APs and solved by our proposed stable-matching-based caching scheme. Simulation results demonstrate that the proposed scheme can achieve near-optimal performances in terms of delivery delay and offloading ratio with a low complexity. Second, UAV-aided caching is considered to assist vehicular content delivery in aerial-ground vehicular networks (AGVN) and a joint caching and trajectory optimization (JCTO) problem is investigated to jointly optimize content caching, content delivery, and UAV trajectory. To enable real-time decision-making in highly dynamic vehicular networks, we propose a deep supervised learning scheme to solve the JCTO problem. Specifically, we first devise a clustering-based two-layered (CBTL) algorithm to solve the JCTO problem offline. With a given content caching policy, we design a time-based graph decomposition method to jointly optimize content delivery and UAV trajectory, with which we then leverage the particle swarm optimization algorithm to optimize the content caching. We then design a deep supervised learning architecture of the convolutional neural network (CNN) to make online decisions. With the CNN-based model, a function mapping the input network information to output decisions can be intelligently learnt to make timely inferences. Extensive trace-driven experiments are conducted to demonstrate the efficiency of CBTL in solving the JCTO problem and the superior learning performance with the CNN-based model. Third, we investigate caching-assisted cooperative content delivery in space-air-ground integrated vehicular networks (SAGVNs), where vehicular content requests can be cooperatively served by multiple APs in space, aerial, and terrestrial networks. In specific, a joint optimization problem of vehicle-to-AP association, bandwidth allocation, and content delivery ratio, referred to as the ABC problem, is formulated to minimize the overall content delivery delay while satisfying vehicular quality-of-service (QoS) requirements. To address the tightly-coupled optimization variables, we propose a load- and mobility-aware ABC (LMA-ABC) scheme to solve the joint optimization problem as follows. We first decompose the ABC problem to optimize the content delivery ratio. Then the impact of bandwidth allocation on the achievable delay performance is analyzed, and an effect of diminishing delay performance gain is revealed. Based on the analysis results, the LMA-ABC scheme is designed with the consideration of user fairness, load balancing, and vehicle mobility. Simulation results demonstrate that the proposed LMA-ABC scheme can significantly reduce the cooperative content delivery delay compared to the benchmark schemes. In summary, we have investigated the content caching in terrestrial networks with fixed APs, joint caching and trajectory optimization in the AGVN, and caching-assisted cooperative content delivery in the SAGVN. The proposed schemes and theoretical results should provide useful guidelines for future research in the caching scheme design and efficient utilization of network resources in caching-enabled heterogeneous wireless networks