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Analytical Calculation of Rician K-Factor for Indoor Wireless Channel Models

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The tremendous increase in wireless data rate over the past few decades can be attributed to the use of higher frequency bands and increased density of access points along with… Click to show full abstract

The tremendous increase in wireless data rate over the past few decades can be attributed to the use of higher frequency bands and increased density of access points along with advanced signal processing in transceiver. With the use of higher bands and increased access point density, terrestrial wireless communication systems are encountering more and more line-of-sight conditions than systems of earlier era. Moreover modern era of communication systems is generally designed to adapt transmission parameters dynamically. Such adaptations are done based on estimation of channel statistics. The measure of line-of-sight (Rician K-factor) is one of such statistics. It plays a vital role in estimating fade statistics, which influences the bit error rate, spectral efficiency, level crossing rate, average fade duration and so on. These factors significantly influence design of communication systems. This paper focuses on analytical computation of Rician K-factor of multi-clustered propagation channel models including antenna gain-patterns. Rician K-factor in different wireless channel models, which are based on the indoor channel model given by Saleh and Valenzuela and the channel model for IEEE 802.11ad standard have been calculated and compared with simulations. We show that, channel model provided explicit Rician factor does not agree with actual K-factor experienced by a link when details such as directivity of clusters and antenna gains are considered. The difference results in erroneous estimate of system performance. It is seen that the estimates of the required signal to noise ratio per bit for a given modulation and coding schemes can be affected by as much as 4 dB in sub 6 GHz systems and as high as 5 dB for millimeter wave systems due to incorrect use of Rician K-factor for relevant links. Average channel capacity is affected by around 13% for high SNR links due to variation of Rician K-factor.

Keywords: channel models; rician factor; factor; wireless channel

Journal Title: IEEE Access
Year Published: 2017

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