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|Title:||Enhancing the performance of Wi-Fi system by exploiting physical layer information||Authors:||Xie, Yaxiong||Keywords:||DRNTU::Engineering||Issue Date:||2016||Source:||Xie, Y. (2016). Enhancing the performance of Wi-Fi system by exploiting physical layer information. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||Wi-Fi network has been ubiquitous nowadays and has changed our lifestyle. As a communication system, Wi-Fi delivers more than 50% of IP traffic. Consequently, the demand for higher transmission capacity has been increasing continuously and rapidly. The wireless spectrum for Wi-Fi communication is, however, finite. Therefore, one important research direction is to fully utilize the limited resources and at the same time improve the transmission throughput for Wi-Fi network. On the other hand, a lot of emerging applications, e.g., indoor navigation, human tracking, device free gesture control, are built on top of existing commercial Wi-Fi infrastructures to provide a variety of functionalities except communication. All those applications rely on Wi-Fi's capability of sensing the physical world: strong sensing capability will significantly improve the performance of existing applications and extend the scope of potential applications. Therefore, another important research direction is to enhance the sensing capability of existing Wi-Fi infrastructure. This thesis focuses on these two directions and exploits rich information in the PHY layer to build a betterWi-Fi system that has higher communication speed and stronger sensing capability. We observe that modern widebandWi-Fi communication has unevenly distributed bit BERs in a packet because of the frequency selective fading. Based on such an observation, we propose UnPKT, a system that can unequally protect Wi-Fi packet bits according to their BERs. By doing so, we can best match the effective transmission rate of each bit to channel condition, and improve throughput. We derive an accurate relationship between the frequency selective channel condition and the decoded packet bit BERs, all the way through the complex 802.11 PHY layer. A cluster-based protection scheme is proposed to protect packet bits using different MAC-layer FEC redundancies based on bit-wise BER estimation to augment wide band 802.11 transmissions. UnPKT is software-implementable and compatible with the existing 802.11 architecture. Extensive evaluations based on Atheros 9580 NICs and GNU-Radio platforms show the effectiveness of our design. UnPKT can achieve a significant goodput improvement over state-of-the-art approaches. When sensing the physical world usingWi-Fi, power delay profile is widely used in motionor localization-based applications as it characterizes multipath channel features. Recent studies show that the power delay profile may be derived from the CSI traces collected from commodity WiFi devices, but the performance is limited by two dominating factors. The resolution of the derived power delay profile is determined by the channel bandwidth, which is however limited on commodity WiFi. The collected CSI reflects the signal distortions due to both the channel attenuation and the hardware imperfection. A direct derivation of power delay profiles using raw CSI measures, as has been done in the literature, results in significant inaccuracy. Therefore, we build Splicer, a software-based system that derives high resolution power delay profiles by splicing the CSI measurements from multiple WiFi frequency bands. A set of key techniques has also been proposed to separate the mixed hardware errors from the collected CSI measurements. Splicer substantially improves the accuracy in profiling multipath characteristics, reducing the errors of multipath distance estimation to be less than 2m. Splicer can immediately benefit upper-layer applications. Our case study with recent single-AP localization achieves a median localization error of 0.95m.||URI:||https://hdl.handle.net/10356/69395||DOI:||10.32657/10356/69395||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Theses|
Updated on May 13, 2021
Updated on May 13, 2021
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