- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Wi-Fi is one of the candidate technologies for the Internet of Things (IoT), and today connects billions of devices world-wide in dense networks to offer Internet connectivity in a partially or fully automated manner. In order to provide seamless and high quality service, wireless local area networks (WLANs) can adopt dynamic channel access technologies such as dynamic bandwidth or channel hopping schemes in order to avoid interference for better link quality. However, in dense networks, the dynamic channel access leads to a higher probability of adjacent channel interference (ACI). The efficiency of IEEE 802.11-based WLANs using multi-channel and wide dynamic ranges is thus severely degraded by ACIs in dense networks. In this paper, we analyze the ACI effect on WLANs and propose an interference-aware self-optimizing (IASO) Wi-Fi design that incorporates a multi-channel multi-level carrier sense and adaptive initial gain control scheme. This scheme controls carrier sensing thresholds in each band for multi-level sensors, as well as initial gains for amplifiers. The proposed scheme reduces false carrier sensing and avoids saturation of amplifiers while simultaneously improving the dynamic range of the receiver. Our prototype evaluation results demonstrate that the proposed scheme can improve the dynamic range of the receiver by approximately 45 dB and 30 dB for a low data rate and a high data rate mode, respectively, compared with the conventional receiver designs. Furthermore, network emulation results demonstrate that the IASO Wi-Fi can improve the average throughput, latency, and energy efficiency by approximately 32% (24%), 41% (43%), and 13% (17%), respectively, compared with the conventional receiver designs (and channel hopping techniques) in dynamically varying interfered channel conditions.
Key considerations in architecting the next generation of WLAN are robust performance and energy efficiency. We expect both high throughput and long range services will be required to ubiquitous networking demands of IoT applications. In this paper, we analyzed the ACI effect and proposed IASO Wi-Fi for high efficiency opera- tion in dense networks for WLANs. In the existing literature, there has not yet been an investigation of adaptive initial gain control and multi-channel multi-level carrier sensing, or an implementa- tion thereof to mitigate the ACI effect in WLAN receivers. The pro-posed IASO Wi-Fi is designed to mitigate interference and provides substantial improvements in the dynamic range of the receiver, throughput, latency, and energy efficiency by enhancing the carrier sensor, and by utilizing the gain controller in networks with dense stations and BSSs that potentially interfere with each other. The FPGA prototype experiment and network emulation results con- firm the superior performance of the IASO scheme in compari- son with the existing schemes under dynamically varying inter- fered channel conditions. The proposed IASO Wi-Fi can be applied for IoT to achieve high efficiency wireless networking in the real world of highly dense network environments with large numbers of APs and stations. As future work, we plan to extend this work to combine the IASO scheme and dynamic sensitivity control for greater improvements under various channel conditions, and study an interference-aware power saving scheme to improve power ef- ficiency of the receiver.