Soft-Defined Heterogeneous Vehicular Network: Architecture and Challenges

Heterogeneous Vehicular NETworks (HetVNETs) can meet various quality-of-service (QoS) requirements for intelligent transport system (ITS) services by integrating different access networks coherently. However, the current network architecture for HetVNET cannot efficiently deal with the increasing demands of rapidly changing network landscape. Thanks to the centralization and flexibility of the cloud radio access network (Cloud-RAN), soft-defined networking (SDN) can conveniently be applied to support the dynamic nature of future HetVNET functions and various applications while reducing the operating costs. In this paper, we first propose the multi-layer Cloud RAN architecture for implementing the new network, where the multi-domain resources can be exploited as needed for vehicle users. Then, the high-level design of soft-defined HetVNET is presented in detail. Finally, we briefly discuss key challenges and solutions for this new network, corroborating its feasibility in the emerging fifth-generation (5G) era

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Vehicle to vehicle (V2V) wireless communications

This work focuses on the vehicle-to-vehicle (V2V) communication, its current challenges, future perspective and possible improvement.V2V communication is characterized by the dynamic environment, high mobility, nonpredective scenario, propagation effects, and also communicating antenna's positions. This peculiarity of V2V wireless communication makes channel modelling and the vehicular propagation quite challenging. In this work, firstly we studied the present context of V2V communication also known as Vehicular Ad-hoc Netwok (VANET) including ongoing researches and studies particularly related to Dedicated Short Range Communication (DSRC), specifically designed for automotive uses with corresponding set of protocols and standards. Secondly, we focused on communication models and improvement of these models to make them more suitable, reliable and efficient for the V2V environment. As specifies the standard, OFDM is used in V2V communication, Adaptable OFDM transceiver was designed. Some parameters as performance analytics are used to compare the improvement with the actual situation. For the enhancement of physical layer of V2V communication, this work is focused in the study of MIMO channel instead of SISO. In the designed transceiver both SISO and MIMO were implemented and studied successfully

Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks

Connected and automated vehicles will enable advanced traffic safety and efficiency applications thanks to the dynamic exchange of information between vehicles, and between vehicles and infrastructure nodes. Connected vehicles can utilize IEEE 802.11p for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. However, a widespread deployment of connected vehicles and the introduction of connected automated driving applications will notably increase the bandwidth and scalability requirements of vehicular networks. This paper proposes to address these challenges through the adoption of heterogeneous V2V communications in multi-link and multi-RAT vehicular networks. In particular, the paper proposes the first distributed (and decentralized) context-aware heterogeneous V2V communications algorithm that is technology and application agnostic, and that allows each vehicle to autonomously and dynamically select its communications technology taking into account its application requirements and the communication context conditions. This study demonstrates the potential of heterogeneous V2V communications, and the capability of the proposed algorithm to satisfy the vehicles' application requirements while approaching the estimated upper bound network capacity