Name
Title | ||
|---|---|---|
Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication |
Abstract | ||
|---|---|---|
Current strategies for joint radar-communication (JRC) rely on prior knowledge of the communication and radar systems within the vehicle network. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help the vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a Markov Decision Process (MDP). We show that deep reinforcement learning results in superior performance compared to non-learning algorithms. In addition, experimental results show that the trained deep reinforcement learning agents are robust to changes in the number of vehicles in the environment. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/WCNC51071.2022.9771580 | 2022 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) |
Keywords | DocType | ISSN |
Deep reinforcement learning, resource allocation, joint radar-communication | Conference | 1525-3511 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jungwoo Lee | 1 | 1467 | 156.34 |
| Chen-Yi Lee | 2 | 1211 | 152.40 |
| Niyato Dusit | 3 | 9486 | 547.06 |
| yl guan | 4 | 0 | 0.34 |
| gd gonzález | 5 | 0 | 0.34 |