V2X Meets NOMA: Non-Orthogonal Multiple Access for 5G Enabled Vehicular Networks

Benefited from the widely deployed infrastructure, the LTE network has recently been considered as a promising candidate to support the vehicle-to-everything (V2X) services. However, with a massive number of devices accessing the V2X network in the future, the conventional OFDM-based LTE network faces the congestion issues due to its low efficiency of orthogonal access, resulting in significant access delay and posing a great challenge especially to safety-critical applications. The non-orthogonal multiple access (NOMA) technique has been well recognized as an effective solution for the future 5G cellular networks to provide broadband communications and massive connectivity. In this article, we investigate the applicability of NOMA in supporting cellular V2X services to achieve low latency and high reliability. Starting with a basic V2X unicast system, a novel NOMA-based scheme is proposed to tackle the technical hurdles in designing high spectral efficient scheduling and resource allocation schemes in the ultra dense topology. We then extend it to a more general V2X broadcasting system. Other NOMA-based extended V2X applications and some open issues are also discussed.Comment: Accepted by IEEE Wireless Communications Magazin

Resource Allocation for Device-to-Device Communications Underlaying Heterogeneous Cellular Networks Using Coalitional Games

Heterogeneous cellular networks (HCNs) with millimeter wave (mmWave) communications included are emerging as a promising candidate for the fifth generation mobile network. With highly directional antenna arrays, mmWave links are able to provide several-Gbps transmission rate. However, mmWave links are easily blocked without line of sight. On the other hand, D2D communications have been proposed to support many content based applications, and need to share resources with users in HCNs to improve spectral reuse and enhance system capacity. Consequently, an efficient resource allocation scheme for D2D pairs among both mmWave and the cellular carrier band is needed. In this paper, we first formulate the problem of the resource allocation among mmWave and the cellular band for multiple D2D pairs from the view point of game theory. Then, with the characteristics of cellular and mmWave communications considered, we propose a coalition formation game to maximize the system sum rate in statistical average sense. We also theoretically prove that our proposed game converges to a Nash-stable equilibrium and further reaches the near-optimal solution with fast convergence rate. Through extensive simulations under various system parameters, we demonstrate the superior performance of our scheme in terms of the system sum rate compared with several other practical schemes.Comment: 13 pages, 12 figure

A bipartite graph based proportional fair scheduling strategy to improve throughput with multiple resource blocks

The fifth-generation wireless communication is expected to provide a huge amount of capacity to cater to the need of an increasing number of mobile consumers, which can be satisfied by device-to-device (D2D) communication. Reusing the cellular user’s resources in an efficient manner helps to increase the spectrum efficiency of the network but it leads to severe interference. The important point in reusing cellular user resources is that D2D communication should not affect the cellular user’s efficiency. After achieving this requirement, the focus is now turned toward the allocation of resources to D2D communication. This resource allocation strategy is to be designed in such a way that it will not affect communication among the cellular user (CU). This scheme improves various performance objectives. This paper aims at designing a proportional fair resource allocation algorithm based on the bipartite graph which maintains the quality of service (QoS) of CUs while providing D2D communication. This algorithm can be merged with any other scheme of resource allocation for improving QoS and adopting changing channels. In this scheme, a D2D pair can be allocated with one or more than one resource blocks. The MATLAB simulations analyze the performance of the proposed scheme

Dynamic Popular Content Distribution in Vehicular Networks using Coalition Formation Games

Driven by both safety concerns and commercial interests, vehicular ad hoc networks (VANETs) have recently received considerable attentions. In this paper, we address popular content distribution (PCD) in VANETs, in which one large popular file is downloaded from a stationary roadside unit (RSU), by a group of on-board units (OBUs) driving through an area of interest (AoI) along a highway. Due to high speeds of vehicles and deep fadings of vehicle-to-roadside (V2R) channels, some of the vehicles may not finish downloading the entire file but only possess several pieces of it. To successfully send a full copy to each OBU, we propose a cooperative approach based on coalition formation games, in which OBUs exchange their possessed pieces by broadcasting to and receiving from their neighbors. Simulation results show that our proposed approach presents a considerable performance improvement relative to the non-cooperative approach, in which the OBUs broadcast randomly selected pieces to their neighbors as along as the spectrum is detected to be unoccupied

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

Popular Content Distribution in Public Transportation Using Artificial Intelligence Techniques

Outdoor wireless networks suffer nowadays from an increasing data traffic demand which comes at the time where almost no vacant frequency spectrum has been left. A vast majority of this demand comes from popular content generated by video streaming and social media sites. In the future, other sources will generate even more demand with emerging applications such as virtual reality, connected cars and environmental sensing. While a significant progress has been made to address this network saturation in indoor environments, current outdoor solutions, based on fixed network deployments, are expensive to build and maintain. They tend to be immobile and therefore are inflexible in coping with the dynamics of outdoor data demand. On the other hand, Vehicular Ad-hoc NETworks (VANETs) are in nature more scalable, dynamic, flexible, and therefore more promising in terms of addressing such demand. This is especially feasible if we take advantage of public transportation vehicles and stops. These vehicles and stops are often owned and operated by the same administrative entity which overcomes the routing selfishness issue. Moreover, the mobility patterns of these vehicles are highly predictable given their regular schedules; their locations are publicly-sharable and their location distribution is uniform throughout space and time. Given these factors, a system that utilizes public transportation vehicles and stops to build a reliable, scalable and dynamic VANET for wireless network offloading in outdoor environments is proposed. This is done by exploiting the predictability demonstrated by such vehicles using an Artificial-Intelligence (AI) based system for wireless network offloading via popular content distribution. The AI techniques used are the Upper Popularity Bound (UPB) collaborative and group-based recommender based on multi-armed bandits for content recommendation and bayesian optimization based on batch-based Random Forest (RF) regression for content routing. They are used after analyzing the mobility data of public transportation vehicles and stops. This analysis includes both preprocessing and processing the data in order to select the optimal set of stops and clustering vehicles and stops based on cumulative contact duration thresholds. The final system has shown the promising networking potential of public transportation. It incorporates a recommender that has shown a versatile performance under different consumer interest and network capacity scenarios. It has also demonstrated a superior performance using a bayesian optimization technique that offloads as high as 95% of the wireless network load in an interference and collision free manner