Accurate and precise frequency synchronization is an essential requirement for different areas in telecommunication industry. Emerging practise for frequency synchronization is to utilize packet networks since it is highly cost effective. One method of distributing frequency over a variety of packet networks is based on well known IEEE 1588v2 PTP standard that uses a masterslave architecture. Accuracy and precision of the frequency synchronization over IEEE 1588v2 PTP is mainly deteriorated by the Packet Delay Variations (PDVs) experienced on the transmission path. This problem can be overcome by deploying so-called timing aware routing elements, however slaves' frequency accuracy and precision still heavily relies on the quality of the slave's clock synchronization algorithm. Hence, this work introduces an improved Kalman filter based algorithm for frequency synchronization over IEEE 1588v2 protocol that outperforms other prior art techniques. The algorithm's performance is evaluated using simulation, yet the network impairments (PDVs) are based on experimentally measured data.
Localization for indoor environments has gained considerable attention over the last decade. The most popular technique is based on location fingerprinting using received signal strength (RSS) mainly due to the fact that it exploits the available wireless infrastructure and that RSS fingerprints are readily available using different wireless standards (IEEE 802.11, etc.). This simplicity however incurs a cost in accuracy and researchers focus on improving the performance from a pattern recognition perspective. Recently improvement in performance has been demonstrated using physical layer channel-based fingerprints such as the Channel Transfer Function (CTF) and Channel Impulse Response (CIR) at a cost of increased storage and computation requirements. In this paper we experimentally evaluate the performance of a probabilistic physical layer fingerprint that is based on entropy of the magnitude and phase of the CTF. We will show through extensive frequency domain channel measurements in an indoor office environment that entropy can be a practical alternative to RSS fingerprinting; where it shares the latter's simplicity of structure (scalar) but outperforms RSS and complex CIR fingerprints. We further investigate the impact of realistic channel and system impairments such as small-scale fading (Doppler), Signal-to-noise ratio (SNR) and interference on the performance of the proposed fingerprint signature.
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