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 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 A Machine Learning Approach for 5G SINR Prediction

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1660
Author(s):  
Ruzat Ullah ◽  
Safdar Nawaz Khan Marwat ◽  
Arbab Masood Ahmad ◽  
Salman Ahmed ◽  
Abdul Hafeez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mobile Networks ◽  
Radio Resource Management ◽  
Radio Resource ◽  
Machine Learning Approach ◽  
Channel Conditions ◽  
Radio Resources ◽  
Auto Regressive ◽  
Artificial Neural Network Ann ◽  
Radio Resource Scheduling

Artificial Intelligence (AI) and Machine Learning (ML) are envisaged to play key roles in 5G networks. Efficient radio resource management is of paramount importance for network operators. With the advent of newer technologies, infrastructure, and plans, spending significant radio resources on estimating channel conditions in mobile networks poses a challenge. Automating the process of predicting channel conditions can efficiently utilize resources. To this point, we propose an ML-based technique, i.e., an Artificial Neural Network (ANN) for predicting SINR (Signal-to-Interference-and-Noise-Ratio) in order to mitigate the radio resource usage in mobile networks. Radio resource scheduling is generally achieved on the basis of estimated channel conditions, i.e., SINR with the help of Sounding Reference Signals (SRS). The proposed Non-Linear Auto Regressive External/Exogenous (NARX)-based ANN aims to minimize the rate of sending SRS and achieves an accuracy of R = 0.87. This can lead to vacating up to 4% of the spectrum, improving bandwidth efficiency and decreasing uplink power consumption.

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 Investigating and Modeling of Cooperative Vehicle-to-Vehicle Safety Stopping Distance

2021 ◽  
Vol 13 (3) ◽  
pp. 68
Author(s):  
Steven Knowles Flanagan ◽  
Zuoyin Tang ◽  
Jianhua He ◽  
Irfan Yusoff
Keyword(s):  
High Reliability ◽  
Base Station ◽  
Ieee 802.11P ◽  
Packet Losses ◽  
The Road ◽  
V2v Communication ◽  
Stopping Distance ◽  
Vehicle To Vehicle ◽  
V2v Communications ◽  
On The Road

Dedicated Short-Range Communication (DSRC) or IEEE 802.11p/OCB (Out of the Context of a Base-station) is widely considered to be a primary technology for Vehicle-to-Vehicle (V2V) communication, and it is aimed toward increasing the safety of users on the road by sharing information between one another. The requirements of DSRC are to maintain real-time communication with low latency and high reliability. In this paper, we investigate how communication can be used to improve stopping distance performance based on fieldwork results. In addition, we assess the impacts of reduced reliability, in terms of distance independent, distance dependent and density-based consecutive packet losses. A model is developed based on empirical measurements results depending on distance, data rate, and traveling speed. With this model, it is shown that cooperative V2V communications can effectively reduce reaction time and increase safety stop distance, and highlight the importance of high reliability. The obtained results can be further used for the design of cooperative V2V-based driving and safety applications.

scholarly journals Empirical Models for the Realistic Generation of Cooperative Awareness Messages in Vehicular Networks

2020 ◽  
Vol 69 (5) ◽  
pp. 5713-5717 ◽  
Author(s):  
Rafael Molina-Masegosa ◽  
Miguel Sepulcre ◽  
Javier Gozalvez ◽  
Friedbert Berens ◽  
Vincent Martinez
Keyword(s):  
Vehicular Networks ◽  
Empirical Models ◽  
Cooperative Awareness Messages ◽  
Cooperative Awareness
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