DQN-Based Directional MAC Protocol in Wireless Ad Hoc Network in Internet of Things
- Authors
- Kim, Namkyu; Na, Woongsoo; Lakew, Demeke Shumeye; Dao, Nhu-Ngoc; Cho, Sungrae
- Issue Date
- Apr-2024
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Deafness; deep Q-network; Deep reinforcement learning; directional MAC; Internet of Things; Media Access Protocol; Millimeter wave communication; Protocols; Q-learning; Throughput
- Citation
- IEEE Internet of Things Journal, v.11, no.7, pp 12918 - 12928
- Pages
- 11
- Journal Title
- IEEE Internet of Things Journal
- Volume
- 11
- Number
- 7
- Start Page
- 12918
- End Page
- 12928
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/71360
- DOI
- 10.1109/JIOT.2023.3338562
- ISSN
- 2327-4662
- Abstract
- The use of directional antennas in high frequency bands (e.g., millimeter-wave) is essential to support applications requiring high throughput and low latency. However, communications using directional antennas require intricate scheduling by a central coordinator to avoid collision and deafness problems. Thus, in this study, we propose a directional medium access control (DMAC) protocol based on a deep Q-network (DQN) framework wireless ad hoc networks (WANETs) for Internet of Things (IoT). In our model, even though there is no central coordinating unit (e.g., edge/cloud server), each IoT device can intelligently avoid the collision and deafness through its learning agent. In addition, to maximize the throughput, we design a reinforcement learning (RL) architecture and propose a DQN-based DMAC such that each IoT device intelligently selects the time-slot and transmitting beam without any central coordinator. The proposed schemes are evaluated using carrier-sense multiple access (CSMA) and adaptive learning-based DMAC(AL-DMAC) protocols. The evaluation results reveal that the proposed double DQN scheme outperforms the existing schemes by approximately 54.1% and 57.2% in terms of the throughput. IEEE
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