Optimal Association for mmWave Communication in Indoor Environment:A Vision-guided Learning Approach
- In large indoor environments using millimeter-wave (mmWave) frequencies, efficient base station (BS) connectivity management is essential to maintain seamless connectivity and maximize network performance. Due to the high sensitivity of the mmWave band to environmental obstacles and user mobility, traditional BS connectivity mechanisms that rely on signal strength and threshold-based decisions often do not perform optimally.
- This can result in frequent BS switching, degraded quality of service (QoS), and increased network overhead.
- To address these issues, this paper proposes a novel approach for more intelligent and proactive BS association in mmWave communication environments by utilizing computer vision (CV) to predict the location, blocking situation, and data rate of user equipment (UE).
- The proposed model utilizes visual data from RGB cameras to accurately track the movement of UEs in real-time.
- This visual information is integrated with network parameters to estimate the data rate, taking into account the unique propagation characteristics of mmWave signals, such as line-of-sight (LoS) sensitivity and high path loss.
- It also uses a deep reinforcement learning (DRL) algorithm to optimize BS association and balance network load, data rate, and QoS to improve overall system performance. This approach aims to minimize unnecessary BS switches and improve network efficiency in dynamic mmWave environments.