Georgij Serebryakov

This paper presents a probabilistic analytical model for the fifth generation New Radio (5G NR) Vehicle-to-Vehicle communication under autonomous resource selection, also known as Sensing-Based Semi-Persistent Scheduling. The highway scenario specified by the third partnership project (3GPP) consortium is studied. The model introduces the joint probability distribution function of allocated time-frequency resources and the spacing between vehicles using the same time-frequency resource, enabling analytical descriptions of interference, sensing zone size, and packet reception ratio. Half-duplex, hidden-node, and resource reselection collisions are explicitly characterized. The analysis also reveals a distance-dependent behavior of half-duplex collisions, which has not been reported in prior studies. The model provides an upper-bound estimate of performance of Sensing-Based Semi-Persistent Scheduling while offering insights into aspects that are difficult to capture through simulations. Applications include performance evaluation and optimization of New Radio Vehicle-to-Vehicle systems in diverse deployment scenarios. The model may also assist in the design of more efficient resource allocation strategies.

The paper is dedicated to Direction of Arrival (DOA) estimation of multiple sources using a single port phased antenna array. We consider the case of the received signal with randomly varying phase that may be caused by both hardware constraints and waveform design. The well-known subspace-based algorithms such as Multiple Signal Classification (MUSIC) show good performance with digital antenna arrays but they cannot be applied under the mentioned restrictions. Moreover, the existing beamspace approaches intended for phased antenna arrays cannot also be applied because of the phase variation. In this paper we propose a novel phase-independent beamspace MUSIC algorithm that uses the spatial power spectrum measured with a finite number of beams as the input. It builds an approximation of the signal covariance matrix and applies the MUSIC’s approach for DOA estimation. The simulation results shows that the developed algorithm has a superresolution ability and allows one to distinguish a weak source masked by the sidelobe power leakage of the stronger one.

The paper is dedicated to the multipath angle of arrival (AOA) estimation problem in millimeter wave (mmWave) 5G NR communication system. The case of a phased antenna array with a single digital port is considered. In this scenario, the conventional highly accurate subspace-based algorithms cannot be applied because of hardware restrictions. We proposed a novel subspace-based algorithm called power-based root minimal polynomial method (PR-MPM) that uses the spatial power spectrum to get an approximation of the signal correlation matrix. The power spectrum is measured via the conventional beam sweeping procedure over a finite number of directions. The efficiency of the proposed method is studied using the high-realistic ray-tracing-based channel model applied in mmWave IEEE 802.11ay standard. Simulation results show that AOAs can be precisely estimated using only single-port power measurement.