Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach
Abstract
:1. Introduction
- The suggested approach mixes an optimization technique with a deep learning algorithm to create a new, effective way to improve connectivity between AVs and everything (X).
- To determine the reliability and efficiency of the communication between AVs and everything, an optimization problem is established utilizing the Lagrange optimization technique and a 1D Convolutional Neural Network (1D-CNN) network.
- The suggested approach attempts to improve communication between AVs and destinations (AV2X) in terms of energy efficiency (EE) and achievable data rate (R). This can be accomplished by identifying the shortest possible distance between AVs and anything in order to maximize total EE and R. Alternately, other factors should be taken into account like transmission power, interference distances that could arise from existing transmission devices sharing the same spectrum, the necessary signal-to-interference-plus-noise ratio (SINRth), and path loss.
- Through the deep learning model, AVs will be able predict the maximum suitable permissible transmission distance while taking into consideration various environmental conditions.
- The proposed approach is examined in terms of energy efficiency and overall achievable data rate under various environmental conditions, such as transmission power, transmission ranges, and needed SINRth values. These discoveries allow for the enhancement of AV networks’ performance.
2. Related Work
3. Materials and Methods
3.1. System Model and Problem Formulation
3.1.1. AV2X Communication Scenario
3.2. Dataset Generation
3.3. Proposed Deep Learning Model
4. Results and Discussion
5. Conclusions
Funding
Data Availability Statement
Conflicts of Interest
Glossary
List of symbols | |
System bandwidth | |
I2X | Interference at any destination |
I2Xmax | The maximum permitted interference |
C1, C2 | Lagrangian optimization constraints |
dRXi | The transmission distance between relay-vehicle and the required destination between everything of the i-th |
The maximum interfered D2D transmission power (Dtx). | |
The maximum interfered CUE transmission power | |
The maximum interfered V2V transmission power | |
The channel gain coefficient between transmission D2D device (Dtx) and the destination | |
The channel gain coefficient between CUE and the destination | |
The channel gain coefficient between transmitted V2V (Vtx) and the destination | |
Autonomous vehicle transmission | |
Maximum autonomous vehicle transmission | |
Signal-to-interference-plus-noise ratio between autonomous vehicle-relay-vehicle (AR) | |
The combined signal-to-interference-plus-noise ratio between autonomous vehicle-everything (AX) and relay-vehicle-everything (RX) | |
The channel gain between autonomous vehicle and relay-vehicle | |
The channel gain between autonomous vehicle and everything | |
The channel gain between relay vehicle and everything | |
I1 | The interference that occurs between autonomous vehicle and relay-vehicle link |
I2 | The interference that occurs between autonomous vehicle and everything link |
I3 | The interference that occurs between relay vehicle and everything link |
N | The noise power |
The achievable data rate between relay-vehicle and everything | |
The achievable data rate between autonomous vehicle and everything | |
PA | Autonomous vehicle transmission power |
Po | Internal circuitry power |
EE | System energy efficiency |
R | Overall achievable data rate |
The interfere transmission device (Dtx) transmission power | |
The interfere transmission CUE transmission power | |
The interfere transmission vehicle (Vtx) transmission power | |
The channel gain coefficient between interfere transmission device (Dtx) and everything | |
The channel gain coefficient interfere transmission CUE and everything | |
The channel gain coefficient between interfere transmission vehicle (Vtx) and everything | |
PLo | The path loss constant |
SINRth | The required signal-to-interference-plus-noise-ratio threshold |
n | The total number of recorded data |
The actual value | |
The predicted value | |
λ1, λ2 | non-negative Lagrangian multipliers |
α | Path loss exponent |
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Technique Used | Optimization Problem | Deep Learning Technique | Metric for System Evaluation | Investigation Scenario | |
---|---|---|---|---|---|
[4] | Multihop relaying between AV2X; direct AV2X and V2V communications. | Improving system QoS by determining the best autonomous inter-vehicle position from which to connect with or relay information to any destination. | N/A |
| Communication between AV2X is relayed with direct communication between V2V. |
[17] | Multihop V2V communication. | Finding the maximum distance between the emergency vehicle and the nearest vehicle through Lagrance optimization. | N/A |
| V2V communication is used to find the nearest vehicle to communicate with in order to get information about road traffic conditions. |
[18] | Cooperative communications for a combined V2I with V2V approach. | Minimizing total energy consumed per bit given a target outage probability, or maximizing end-to-end throughput | N/A |
|
|
[19] | Direct V2V communication. | N/A | N/A |
| Investigate the effect of dust and sand on the quality of the received signal throughout the communication between V2V. |
[20] | A distributed deep learning technique is used to control interference power. | Maximizing V2X system performance; determining the optimal needed interference power. | 1D-CNN |
| V2X communication, in which vehicles on the road share information with everything. |
[21] | Direct communication between V2X. | N/A | Data-driven |
| Communication between vehicles and everything to collect data from various sources to increase driver awareness and decrease collision. |
[22] | Direct communication between V2X | N/A | CNN |
| Direct communication between vehicles and everything to collect data from various source. |
[24] | Direct V2V and communicates with infrastructure through relays. | N/A | LSTM |
|
|
[25] | Direct communication between AV and infrastructure. | N/A | N/A |
| Direct communication between AV and infrastructure and between UE and infrastructure. |
[26] | Direct communication between V2X | N/A | N/A |
| Direct communication between vehicles and everything to collect data from various sources. |
[27] | Direct communication. | N/A | SDN |
| From legacy systems, a flexible foundation of 5G automobile services. |
[28] | Direct V2V communication and communication with I through NodeB. |
| N/A |
| A single cellular vehicle network is thought to exist where VUEs achieve V2V communication. Furthermore, the New Radio Uu interface is used to deliver V2I messages. |
Proposed model | Communication between AV2X or using any other vehicle to relay the information to any destination. | Lagrange optimization is used in order to maximize energy efficiency and achievable data rate. | 1D-CNN |
| AV2X communication can be relayed or can be direct based on the system requirements. |
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Osman, R.A. Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach. Electronics 2023, 12, 4023. https://doi.org/10.3390/electronics12194023
Osman RA. Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach. Electronics. 2023; 12(19):4023. https://doi.org/10.3390/electronics12194023
Chicago/Turabian StyleOsman, Radwa Ahmed. 2023. "Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach" Electronics 12, no. 19: 4023. https://doi.org/10.3390/electronics12194023