Time Series Measurement of IEEE 802.11ad Signal Power Involving Human Blockage with HMM-Based State Estimation

This paper presents a measurement of time-varying attenuation of IEEE802.11ad wireless LAN (WLAN) signals in 60GHz band induced by human blockage. The present measurement is a novel approach to obtain the attenuation, where a commercially available IEEE802.11ad access point (AP) and station (STA) are employed and the measurement is conducted under intermittent packet transmission. This paper also presents a hidden Markov model (HMM)-based signal power estimation scheme so that the attenuation is estimated from data obtained with a microwave spectrum analyzer which cannot detect signals of IEEE 802.11ad WLAN in itself. In this scheme, whether 11ad WLAN signals exist or not at each sampling instant is estimated based on HMM. Before the application of HMM, the scheme detects the number of HMM states via Bayesian information criterion and, thereby, prevents model over-fitting and consequent invalid power estimation. Our experiment revealed that the IEEE802.11ad WLAN signal attenuates by 5dB in a duration of 51.5ms when a human moves across the path between the AP and the STA at a velocity of 0.5 m/s. This result is consistent with a previous report about an IEEE 802.11ad WLAN channel model.

[1]  Martin Jacob,et al.  A dynamic 60 GHz radio channel model for system level simulations with MAC protocols for IEEE 802.11ad , 2010, IEEE International Symposium on Consumer Electronics (ISCE 2010).

[2]  Masahiro Morikura,et al.  Machine-Learning-Based Throughput Estimation Using Images for mmWave Communications , 2017, 2017 IEEE 85th Vehicular Technology Conference (VTC Spring).

[3]  G. E. Zein,et al.  Influence of the human activity on wide-band characteristics of the 60 GHz indoor radio channel , 2004, IEEE Transactions on Wireless Communications.

[4]  Masahiro Morikura,et al.  Proactive Base Station Selection Based on Human Blockage Prediction Using RGB-D Cameras for mmWave Communications , 2014, GLOBECOM 2014.

[5]  A. Girotra,et al.  Performance Analysis of the IEEE 802 . 11 Distributed Coordination Function , 2005 .

[6]  Ada S. Y. Poon,et al.  Detecting Human Blockage and Device Movement in mmWave Communication System , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[7]  Farooq Khan,et al.  mmWave mobile broadband (MMB): Unleashing the 3–300GHz spectrum , 2011, 34th IEEE Sarnoff Symposium.

[8]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[9]  Masahiro Morikura,et al.  Proactive traffic control based on human blockage prediction using RGBD cameras for millimeter-wave communications , 2015, 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC).

[10]  Masahiro Morikura,et al.  Performance modeling of camera-assisted proactive base station selection for human blockage problem in mmWave communications , 2016, 2016 IEEE Wireless Communications and Networking Conference.

[11]  Joseph R. Cavallaro,et al.  Viturbo: a reconfigurable architecture for Viterbi and turbo decoding , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[12]  Pasi Fränti,et al.  Knee Point Detection in BIC for Detecting the Number of Clusters , 2008, ACIVS.

[13]  Katsuyuki Haneda,et al.  Channel Models and Beamforming at Millimeter-Wave Frequency Bands , 2015, IEICE Trans. Commun..

[14]  Ting Wang,et al.  A twin cylinder model for moving human body shadowing in 60GHz WLAN , 2015, 2015 21st Asia-Pacific Conference on Communications (APCC).

[15]  H. Akaike.  A new look at the statistical model identification , 1974 .

[16]  Masahiro Morikura,et al.  Proactive Handover Based on Human Blockage Prediction Using RGB-D Cameras for mmWave Communications , 2016, IEICE Trans. Commun..

[17]  Theodore S. Rappaport,et al.  Millimeter-Wave Human Blockage at 73 GHz with a Simple Double Knife-Edge Diffraction Model and Extension for Directional Antennas , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).

[18]  Teruyuki Miyajima,et al.  Feasibility of RSSI based access network detection for multi-band WLAN using 2.4/5GHz and 60GHz , 2014, 2014 International Symposium on Wireless Personal Multimedia Communications (WPMC).