Time- and Frequency-Varying KK-Factor of Non-Stationary Vehicular Channels for Safety Relevant Scenarios

Vehicular communication channels are characterized by a non-stationary time- and frequency-selective fading process due to fast changes in the environment. We characterize the distribution of the envelope of the first delay bin in vehicle-to-vehicle channels by means of its Rician $K$-factor. We analyze the time-frequency variability of this channel parameter using vehicular channel measurements at 5.6 GHz with a bandwidth of 240 MHz for safety-relevant scenarios in intelligent transportation systems (ITS). This data enables a frequency-variability analysis from an IEEE 802.11p system point of view, which uses 10 MHz channels. We show that the small-scale fading of the envelope of the first delay bin is Ricean distributed with a varying $K$-factor. The later delay bins are Rayleigh distributed. We demonstrate that the $K$-factor cannot be assumed to be constant in time and frequency. The causes of these variations are the frequency-varying antenna radiation patterns as well as the time-varying number of active scatterers, and the effects of vegetation. We also present a simple but accurate bi-modal Gaussian mixture model, that allows to capture the $K$-factor variability in time for safety-relevant ITS scenarios.Comment: 26 pages, 12 figures, submitted to IEEE Transactions on Intelligent Transportation Systems for possible publicatio

Sparsity in the Delay-Doppler Domain for Measured 60 GHz Vehicle-to-Infrastructure Communication Channels

We report results from millimeter wave vehicle-to-infrastructure (V2I) channel measurements conducted on Sept. 25, 2018 in an urban street environment, down-town Vienna, Austria. Measurements of a frequency-division multiplexed multiple-input single-output channel have been acquired with a time-domain channel sounder at 60 GHz with a bandwidth of 100 MHz and a frequency resolution of 5 MHz. Two horn antennas were used on a moving transmitter vehicle: one horn emitted a beam towards the horizon and the second horn emitted an elevated beam at 15-degrees up-tilt. This configuration was chosen to assess the impact of beam elevation on V2I communication channel characteristics: propagation loss and sparsity of the local scattering function in the delay-Doppler domain. The measurement results within urban speed limits show high sparsity in the delay-Doppler domain.Comment: submitted to IEEE International Conference on Communication

Physical-statistical modeling of dynamic indoor power delay profiles

This paper presents a physical-statistical radio channel power delay profiles model for room-to-room communication systems combining the room electromagnetic theory for modeling deterministic channel components with a geometry-based stochastic channel model with time-variant statistics for modeling stochastic components. The deterministic channel component, i.e., mean power delay spectrum, is comprised of specularly reflected paths plus diffuse components due to scattering and diffraction. The specular components are modeled with a set Dirac function, whereas the diffuse components modeling approach is a room electromagnetic theory-based model. Dynamic indoor communication channels are characterized by a non-stationary time-and delay-fading process due to changes in the environment. We analyze and model the time-delay variability of channels using K-factor for small-scale variations and the t-location scale distribution parameters for large-scale variations. It turns out that these parameters cannot be assumed to be constant in time and delay. After modeling of time-delay variations of the first order statistics, we generate channel realizations with appropriate second order statistics. As the result, the presented model enables to describe the evolution of the power delay profile in the time domain

Measurement of Airport Operations Using a Low-Cost Transponder Data Receiver and Collection Unit

Accurate counts of aircraft operations at unmonitored or partially-monitored general aviation airports are important due to their role in the allocation of funds for airport development and improvement. While the Federal Aviation Administration annually invests approximately $1B in small commercial and general aviation airports, fewer than 270 of these 2,950 airports have either full- or part-time air traffic personnel available to register operations counts. Aircraft operations at airports with limited personnel may be counted using temporary acoustic, pneumatic, or video devices, and observations from contract staff. The related sample sizes are inherently small, leading to inaccuracies in the extrapolation of long-term totals. In some cases, the counts may simply be estimated unscientifically by airport managers. Data from aircraft transponders, critical for the safe and efficient management of airspace, may also be used to accurately count airport operations. This data may be collected by a receiver and analyzed with appropriate algorithms. While a majority of the data records (Basic Mode S and Mode C) do not include aircraft positions, a small portion (Extended Mode S) contain position information from which aircraft distances may be directly computed. This dissertation describes a method by which these known distances may be used to calibrate an adaptive digital filter that can be used to estimate distances for the remainder of the aircraft that do not transmit position information. The resulting distance estimates, which exhibit an average error of 0.77 nm per transponder record within 5.0 nm of the receiver, may then be used in conjunction with aircraft altitude and other parameters to identify and register airport operations. Over 16 million data records from three receiver installations at two general aviation airports with collection periods varying from eight to 180 days were used to evaluate the algorithms. The automated operations counts were compared with official air traffic control tower counts obtained from the FAA’s Air Traffic Activity Data System (ATADS) database. A 180-day evaluation found the algorithm provided counts within 2.2% of 52,750 operations; shorter-term comparisons were accurate to within 10% of the FAA counts. The method therefore appears to be an effective and inexpensive means of establishing accurate operations counts at airports with limited personnel