The evaluation results show that the proposed system improves channel capacity under dynamic station combinations, avoids unnecessary re-optimization under transient blockages, and triggers re-optimization after sustained environmental changes such as continuous blockage and device relocation.
Abstract
While indoor Internet of Things (IoT) and sensor networks increasingly rely on Wi-Fi access points (APs) to collect high-bandwidth data streams from multiple devices, conventional APs rely on static antenna deployments, whose fixed orientations are often suboptimal in dynamic propagation environments. To overcome this limitation, this paper proposes a mechanical Wi-Fi antenna control system that adaptively optimizes its 3D antenna orientation for dynamic multi-station scenarios. The proposed system autonomously actuates its physical antennas in response to perceived radio environments by combining state-specific black-box optimizers and capacity-based environment change detection. The evaluation results show that the proposed system improves channel capacity under dynamic station combinations, avoids unnecessary re-optimization under transient blockages, and triggers re-optimization after sustained environmental changes such as continuous blockage and device relocation.
The capacity limitation of wireless networks can be overcome using MIMO architecture, but it causes power consumption while operating a static base station. RF chains are continuing in operation for all time, even when there is no cell utilization. To overcome this, we are designing a dynamic physical layer which has a...
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