scholarly journals Open Sub-Granting Radio Resources in Overlay D2D-Based V2V Communications

2021 ◽  
Author(s):  
Dariush Mohammad Soleymani ◽  
Mohammad Reza Gholami ◽  
Giovanni Del Galdo ◽  
Jens Mueckenheim ◽  
Andreas Mitschele-thiel
Keyword(s):  
Dynamic Environment ◽  
D2d Communication ◽  
Maximization Problem ◽  
Radio Resource Allocation ◽  
Radio Resource ◽  
Radio Resources ◽  
Machine Type Communication ◽  
Case Simulation ◽  
Heuristics Algorithm ◽  
V2v Communications

Abstract Capacity, reliability, and latency are seen as key requirements of new emerging applications, namely Vehicle-to-Everything (V2X) and Machine Type Communication (MTC) in future cellular networks. Device-to-Device (D2D) communication is envisaged to be the enabler to accomplish the requirements for the applications as mentioned earlier. Due to the scarcity of radio resources, a hierarchical radio resource allocation, namely the sub-granting scheme, has been considered for the overlay D2D communication. In this paper, we investigate the assignment of underutilized radio resources from D2D communication to Device-to-Infrastructure (D2I) communication, which are moving in a dynamic environment. The sub-granting assignment problem is cast as a maximization problem of the uplink cell throughput. Firstly, we evaluate the sub-granting signaling overhead due to mobility in a centralized sub-granting resource algorithm, Dedicated Sub-Granting Radio Resource (DSGRR), and then a distributed heuristics algorithm, Open Sub-Granting Radio Resource (OSGRR) is proposed and compared with the DSGRR algorithm and no sub-granting case. Simulation results show improved cell throughput for the OSGRR compared with other algorithms. Besides, it is observed that the overhead incurred by the OSGRR is less than the DSGRR while the achieved cell throughput is yet close to the maximum achievable uplink cell throughput.

scholarly journals Sub-Granting Radio Resources in Overlay D2D-Based V2V Communications

2020 ◽  
Author(s):  
Moahammad Soleymani Dariush ◽  
Mohammad reza Gholami ◽  
Jens Mueckenheim ◽  
Andreas Mitschele-Thiel
Keyword(s):  
Dynamic Environment ◽  
D2d Communication ◽  
Throughput Maximization ◽  
Maximization Problem ◽  
Radio Resource Allocation ◽  
Radio Resource ◽  
Machine Type ◽  
Radio Resources ◽  
Machine Type Communication ◽  
V2v Communications

Abstract Capacity, reliability, and latency are seen as key requirements of new emerging applications, namely Vehicleto- Everthings (V2X) and Machine Type Communication (MTC) in future cellular networks. Device-to-Device (D2D) communication is envisaged to be the enabler to accomplish the requirements for the aforementioned applications. Due to the scarcity of radio resources, hierarchical radio resource allocation, namely the sub-granting scheme, has been considered for the overlay D2D communication. In this paper, we investigate the assignment of un-utilized radio resources to Device-to-Infrastructure (D2I) users, i.e., beneficiary user, for moving users in a dynamic environment. The sub-granting assignment problem is mathematically cast as the uplink cell throughput maximization problem. To this end, two heuristics are proposed: 1) Dedicated Sub-Granting Radio Resource (DSGRR) in a centralized manner, and 2) Open Sub-Granting Radio Resource (OSGRR) in a distributed fashion. Simulation results show improved cell throughput for the OSGRR compared with the DSGRR yet less overhead while having reasonable tightness to the maximum achievable uplink throughput.

scholarly journals Machine Learning for Disseminating Cooperative Awareness Messages in Cellular V2V Communications

2021 ◽  
Author(s):  
Luca Lusvarghi ◽  
Maria Luisa Merani
Keyword(s):  
Machine Learning ◽  
Resource Assignment ◽  
Radio Resource ◽  
Vehicle To Vehicle ◽  
Generation Times ◽  
Radio Resources ◽  
V2v Communications ◽  
Message Broadcasting ◽  
Cooperative Awareness Messages ◽  
Cooperative Awareness

<div>This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them.</div><div>Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects.</div>

scholarly journals Radio Resource Allocation and Power Control Scheme in V2V Communications Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 34529-34540
Author(s):  
Yanzhao Hou ◽  
Xunchao Wu ◽  
Xiaosheng Tang ◽  
Xiaoqi Qin ◽  
Mingyu Zhou
Keyword(s):  
Resource Allocation ◽  
Power Control ◽  
Radio Resource Allocation ◽  
Radio Resource ◽  
Communications Network ◽  
Control Scheme ◽  
V2v Communications

scholarly journals Efficient radio resource allocation scheme for 5G networks with device-to-device communication

2021 ◽  
Vol 11 (6) ◽  
pp. 5588
Author(s):  
Fareha Nizam ◽  
Mardeni Roslee ◽  
Zubaida Yusoff ◽  
Prince Ugochukwu Nmenme ◽  
Keshvinder Singh ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Outer Region ◽  
D2d Communication ◽  
Fractional Frequency Reuse ◽  
Total System ◽  
Radio Resource Allocation ◽  
Radio Resource ◽  
Multiple Assignment ◽  
Device To Device ◽  
Sum Rate

<p>A vital technology in the next-generation cellular network is device-to-device (D2D) communication. Cellular user enabled with D2D communication provides high spectral efficiency and further increases the coverage area of the cell, especially for the end-cell users and blind spot areas. However, the implementation of D2D communication increases interference among the cellular and D2D users. In this paper, we proposed a radio resource allocation (RRA) algorithm to manage the interference using fractional frequency reuse (FFR) scheme and Hungarian algorithm. The proposed algorithm is divided into three parts. First, the FFR scheme allocates different frequency bands among the cell (inner and outer region) for both the cellular and the D2D users to reduce the interference. Second, the Hungarian weighted bipartite matching algorithm is used to allocate the resources to D2D users with the minimum total system interference, while maintaining the total system sum rate. The cellular users share the resources with more than one D2D pair. Lastly, the local search technique of swapping is used for further allocation to minimize the interference. We implemented two types of assignments, fair multiple assignment, and restricted multiple assignment. We compared our results with existing algorithms which verified that our proposed algorithm provides outstanding results in aspects like interference reduction and system sum rate. For restricted multiple assignment, 60-70% of the D2D users are allocated in average cases.</p>

scholarly journals Resource allocation strategies in cognitive radio D2D communication networks

2021 ◽  
Author(s):  
Ajmery Sultana
Keyword(s):  
Resource Allocation ◽  
Cognitive Radio ◽  
Power Allocation ◽  
Cellular Networks ◽  
Base Station ◽  
D2d Communication ◽  
Maximization Problem ◽  
Radio Resource ◽  
Allocation Algorithm ◽  
Allocation Strategies

Device-to-device (D2D) communication is developed as a new paradigm to enhance net- work performance according to LTE and WiMAX advanced standards. On the other hand, cognitive radio (CR) approach provides efficient spectral usage using intelligent wireless nodes. In this thesis, a number of optimal resource allocation strategies for D2D communi- cation networks are investigated using the CR approach. As a first step, the CR approach in radio access networks is introduced. In the second step, the taxonomy of the RA process in CRNs is provided. For radio resource allocation (RRA), the most crucial task is to associate a user with a particular serving base station, to assign the channel and to allocate the power efficiently. In this thesis, a subcarrier assignment scheme and a power allocation algorithm using geometric water-filling (GWF) is presented for orthogonal frequency division multiplexing (OFDM) based CRNs. This algorithm is proved to maximize the sum rate of secondary users by allocating power more efficiently. Then, the RA problem is studied to jointly employ CR technology and D2D communication in cellular networks in terms of spectral efficiency (SE) and energy efficiency (EE). In the first case, in terms of SE, a two-stage approach is considered to allocate the radio resource efficiently where a new adaptive subcarrier allocation (ASA) scheme is designed first and then a novel power allocation (PA) scheme is developed utilizing proven GWF approach that can compute exact solution with less computation. In the second case, in terms of EE, the power allocation problem of cellular networks that co-exist with D2D communication considering both underlay and overlay CR approaches are investigated. A proven power allocation algorithm based on GWF approach is utilized to solve the EE maximization problem which results in an “exact" and “low complexity" solution.

scholarly journals Machine Learning for Disseminating Cooperative Awareness Messages in Cellular V2V Communications

2021 ◽  
Author(s):  
Luca Lusvarghi ◽  
Maria Luisa Merani
Keyword(s):  
Machine Learning ◽  
Resource Assignment ◽  
Radio Resource ◽  
Vehicle To Vehicle ◽  
Generation Times ◽  
Radio Resources ◽  
V2v Communications ◽  
Message Broadcasting ◽  
Cooperative Awareness Messages ◽  
Cooperative Awareness

<div>This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them.</div><div>Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects.</div>

Radio Resource Allocation Algorithm Based on Bargaining Game Theory for LTE System

2014 ◽  
Vol 644-650 ◽  
pp. 1527-1530
Author(s):  
Han Yin ◽  
Duo Zhang
Keyword(s):  
Game Theory ◽  
Resource Allocation ◽  
Rapid Development ◽  
Bargaining Game ◽  
Radio Resource Allocation ◽  
Radio Resource ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Radio Resources ◽  
Bargaining Game Theory

With the rapid development of wireless communication technologies, users could get many kinds of services and applications now. And as the number of users and the amount of traffic are growing, the contradiction between the infinite demand of users and the finite radio resources is getting increasingly apparent. According to this situation, this paper propose a radio resource allocation algorithm based on bargaining game theory for fourth generation long term evolution (LTE) system, with which the network could balance the situations of users in different classes and enhance the utility of users. The simulation results show that the proposed algorithm could allocate the radio resources efficiently and provide users with higher utility.

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