Channel Measurement And Modelling For Short-Range Millimeter-Wave Wireless Communications

Due to the explosive growth of smart phones and tablets, data rates of mobile Internet access technologies have increased dramatically during the last decade. The millimeter-wave frequency band is considered to be a promising candidate for the new-generation wireless local area network (WLAN) systems due to its availability of unused very wide bandwidth. Wireless channel measurements and models are essential for developing a new WLAN system at a new frequency band. In this study, the channel measurement and modeling of large-scale, small-scale, and multiple-input and multiple-output (MIMO) characteristics of Q band in three typical indoor environments are investigated. The main work is described as follows.First, the fading characteristics of wireless communications are reviewed, several general approaches of channel modelling were introduced, and statistical modeling and ray-tracing modeling techniques are involved in our measurement. This work is divided into three separate parts as large-scale fading measurement, small-scale fading measurement and MIMO measurement, according to the classification of wireless channel fading characteristics.Next, large amount of measurement work is carried out in typical indoor environments, fading characteristics are obtained in different scenario setups, fading constant, path loss exponent and fluctuation deviation are derived from least square fitting of the measured data. The deviation of the fitted path loss model and the local mean power are statistically analyzed in comparison with the log normal distribution, the correctness of this model is verified with K-S test.Furthermore, the frequency domain measurement technique is used in the measurement of small scale fading characteristics, channel impulse responses with specific AOD and AOA information are obtained by applying azimuth scanning using horn antennas, parameters such as root-mean-square delay spread and angle spread are then calculated. Inter-clusters and intra-clusters are identified by using an automatic clustering algorithm in the S-V modelling sense. The S-V modelling parameters are derived from the statistical census of the temporal and spatial characteristics.Finally, the correlation coefficients of indoor MIMO channel are obtained by using the virtual MIMO measurement technique, and this measurement is based on the small-scale fading measurement method, the interval between antenna element which is the key parameter for the MIMO channel is investigated intensively to see how the coefficient changes.