Swarm intelligence‐based radio resource management for V2V‐based D2D communication

Abstract

Internet of Things is a promising paradigm that provides the future network of interconnected devices. Device-to-Device (D2D) communication, which is considered as an enabler for vehicle-to-everything applications, has become an emerging technology to optimize network performance. In this paper, we study the Radio Resource Management (RRM) issue for D2D-based Vehicle-to-Vehicle communication. The RRM key role is to assure the proficient exploitation of available resources while serving users according to their quality of service parameters. An Ant Colony Optimization (ACO)-based Resource Allocation (ACORA) scheme is proposed in this paper. Swarm intelligence algorithm ACO is adopted to reduce the computational complexity while realizing satisfactory performance. Simulation results show promising performance of our proposed ACORA scheme. KEYWORDSD2D communication, Internet of Things, radio resource management, software defined networks | INTRODUCTIONInternet of Things (IoT) is an integrated element of future Internet containing existing and evolving networks as well as Internet progress. Conceptually, it could be defined as a global dynamic network infrastructure with self-management and self-configuration capabilities based on interoperable and standard communication protocols. 1 Device-to-Device (D2D) communication is an important part of the IoT, in which devices autonomously communicate and collaborate with each other, share, and forward information without centralized control in a multi-hop way. The ability to collect pertinent information in real time is the key to support the value of IoT, expediting the creation of an intelligent environment. Vehicle-to-Everything (V2X) is based on D2D communication. V2X is an emerging technology that enhances and leverages existing Long-Term Evolution (LTE) network elements and features to assure the communication between vehicles and the infrastructure as well as between vehicles. The attractiveness of this emergent technology is due to its ability to support safety use cases with stringent bandwidth, delay, and reliability requirements. 2 In release 12 3 and release 13 4 of LTE, V2X is introduced as part of the Proximity Services (ProSe). 5 It includes many applications, such as travel information, navigation and driver assistance, payment transactions, fleet management, congestion avoidance, safety, and traffic control.V2X communication consist of different kinds of communications: Vehicle-to-Vehicle (V2V), Vehicle-to-Network (V2N), Vehicle-to-Pedestrian (V2P), and Vehicle-to-Infrastructure (V2I). 6 As V2V communication has elevated density and speed, it introduces several technical challenges especially the latency and reliability requirements. Guaranteeing these Quality of Service (QoS) requirements for V2V services is still a challenge that must be tackled. IEEE 802.11p 7 was the first standard for V2V communication until 2016. The Third Generation Partnership Project (3GPP), introduced in release 14 of LTE, features addressing direct V2V communications. 8,9 The mai...

A Survey on Radio Resource Allocation for V2X Communication

“…Different from the underlaying resource allocation algorithms studied in the previous section, the authors in [42][43][44][45] proposed to dedicate specific resources for V2V communications.…”

“…In our previous work [45], a swarm intelligence resource allocation algorithm is proposed in order to improve network sum rate while satisfying the QoS requirements for both V-UEs and C-UEs. Firstly, we mathematically express the outage probability as the requirement of the V-UEs and the user fairness index as the requirement of the C-UEs.…”

Multiobjective optimization‐based radio resource allocation and sharing algorithm for D2D‐based V2V communication

“…In this section, we present the evaluation results of MO-ACORA-Sh as compared with two resource management algorithms: Efficient Resource Allocation for V2X Communications (ERAVC) 12 and Ant Colony Optimization (ACO)-based Resource Allocation (ACORA). 13 We assume a simulation environment with a number of users varying between 100 and 500, where 50% are V-UEs and 50% are C-UEs. V-UEs circulate in freeway scenario and C-UEs have random locations and random distribution into the cell.…”

Semi‐distributed resource management for underlay D2D communication with user's cooperation

“…As per the author's literature survey, most of the distributed algorithm are based on game theory that are not reliable to converge. Only few works consider the distributed solution without applying game theory in previous studies . A distributed matching is given in Holfeld and Jorswieck for many to many pairing where matching of channels and D2D pairs is done on the basis of local CSI without consideration of CUs in the system.…”

“…Further, a different approach is proposed in Khuntia and Hazra for channel and power allocation in underlay D2D to maximize overall sum rate by applying deep Q‐learning with no prior traffic knowledge at the BS. In Feki et al, the resource allocation approach is proposed for D2D‐based V2V communication system. Their proposed resource allocation scheme is based on ant colony optimization (ACO) with the aim of overall sum rate maximization while considering the QoS of vehicle users as well as CUs.…”