Browsing by Author "Foh, Chuan Heng"

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Citation - WoS: 8
Citation - Scopus: 10
Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communications
(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Kose, Abdulkadir; Lee, Haeyoung; Foh, Chuan Heng; Shojafar, Mohammad
Millimeter wave (mmWave) has been recognized as one of key technologies for 5G and beyond networks due to its potential to enhance channel bandwidth and network capacity. The use of mmWave for various applications including vehicular communications has been extensively discussed. However, applying mmWave to vehicular communications faces challenges of high mobility nodes and narrow coverage along the mmWave beams. Due to high mobility in dense networks, overlapping beams can cause strong interference which leads to performance degradation. As a remedy, beam switching capability in mmWave can be utilized. Then, frequent beam switching and cell change become inevitable to manage interference, which increase computational and signalling complexity. In order to deal with the complexity in interference control, we develop a new strategy called Multi-Agent Context Learning (MACOL), which utilizes Contextual Bandit to manage interference while allocating mmWave beams to serve vehicles in the network. Our approach demonstrates that by leveraging knowledge of neighbouring beam status, the machine learning agent can identify and avoid potential interfering transmissions to other ongoing transmissions. Furthermore, we show that even under heavy traffic loads, our proposed MACOL strategy is able to maintain low interference levels at around 10%.
Citation - WoS: 1
Citation - Scopus: 5
Contextual Multi-Armed Bandit Based Beam Allocation in mmWave V2x Communication Under Blockage
(IEEE, 2023) Cassillas, Arturo Medina; Koset, Abdulkadir; Leet, Haeyoung; Foh, Chuan Heng; Leowt, Chee Yen
Due to its low latency and high data rates support, mmWave communication has been an important player for vehicular communication. However, this carries some disadvantages such as lower transmission distances and inability to transmit through obstacles. This work presents a Contextual Multi-Armed Bandit Algorithm based beam selection to improve connection stability in next generation communications for vehicular networks. The algorithm, through machine learning (ML), learns about the mobility contexts of the vehicles (location and route) and helps the base station make decisions on which of its beam sectors will provide connection to a vehicle. In addition, the proposed algorithm also smartly extends, via relay vehicles, beam coverage to outage vehicles which are either in NLOS condition due to blockages or not served any available beam. Through a set of experiments on the city map, the effectiveness of the algorithm is demonstrated, and the best possible solution is presented.