Andrey Ivanov

Today, high-speed navigation has become crucial in air traffic and self-driving cars, where frequency-modulated continuous wave radars are widely used as low-cost range sensors. Such sensors allow calculating the range-Doppler map to increase the detection range for small drones. This map requires computing a 2-D fast Fourier transform (FFT) of the time-chirp matrix to find spectral peaks above the noise floor. Unfortunately, with every newly received chirp, the 2D-FFT must be recomputed, which is computationally expensive; moreover, it may cause performance degradation when detecting fast-moving objects. In this letter, we propose a low-cost algorithm that avoids large-size FFT calculations and instead calculates blockwise 2D-FFTs with further tracking of local maxima, thus lowering complexity and improving sensitivity of radar sensors in high-speed scenarios.

This letter proposes three novel approaches to calculate hybrid precoding using projection maximization. These methods address power imbalances in hybrid precoding by optimizing the analog precoder to better approximate the subspace occupied by users. One of them is optimization-based and employs a Riemannian gradient descent on the complex sphere manifold, and the others are more practical and provide computationally efficient relaxations. Simulation results using the QuaDRiGa 3GPP line-of-sight channel model with 256 transmitting antennas and up to 16 users demonstrate that the proposed methods achieve spectral efficiency close to the fully digital system, significantly outperforming other methods.

In this letter, we propose a new approach to justify a round-off error’s impact on the accuracy of the linear multi-antenna receiver based on QR decomposition. The minimum mean square error (MMSE) detector employs this decomposition to implement the interference rejection combining (IRC) algorithm that requires the inversion of an interference + noise matrix. We suggested a stochastic approach to calculate the resulting round-off error that is significantly more accurate than the existing numerical bounds. It was validated in realistic scenarios using a standard half-precision format. Experimental data show that our method predicts errors that are quite similar to those obtained from simulations. The suggested method can be used in many other applications of QR decomposition to examine the resulting roundoff inaccuracy.

In this letter, we propose a new approach to justify a roundoff error’s impact on the accuracy of the linear multi-antenna receiver based on Cholesky decomposition. This decomposition is used in the minimum mean square error (MMSE) algorithm to invert the interference-plus-noise matrix. We suggest a stochastic approach to calculate the resulting roundoff error of a low-bitwidth MMSE detection that employs a quantized channel estimate and an inaccurate interference-plus-noise matrix inversion. The proposed method allows for a detailed roundoff analysis of the linear detection. It was validated in realistic scenarios using a standard half-precision format. Experimental data show that our method predicts errors that are quite similar to those obtained from simulations. The suggested method can be used to study how quantization noise and other sources of error (such as channel estimation, interpolation, and interference-plus-noise matrix sampling) affect the accuracy of detection.

In this paper, a physical approach is proposed to describe external (air-induced) passive intermodulation (PIM) for a frequency-division duplexing (FDD) multiple-input multiple-output (MIMO) system with an arbitrary number of transceiver chains. It is caused by metallic objects with nonlinear characteristics (“rusty bolt” effect) located near the base station (fence, billboard). PIM penetrates the antenna receiving chain and degrades the uplink signal. Generally, PIM cancellation in MIMO systems represents a complicated task that traditionally requires numerous hardware experiments in numerous environment setups. We present a novel approach for replacing experiments with full-cycle physical modeling of external PIM in the MIMO system. The proposed model includes all steps starting from the generation of transmitting signals through the electromagnetic modeling of the antenna array and open space to a set of variable nonlinear sources and back to the receivers. Its performance was validated with synthetic signals equivalent to the measured data in the literature. Thus, this work contributes to more efficient PIM generation by decreasing the number of hardware experiments while increasing the diversity of PIM scenarios, especially for large MIMO systems and multiple PIM sources of any nature.

In this paper, we propose a new approach to justify a roundoff error impact on the accuracy of the linear least squares (LS) solution using QR decomposition. This decomposition is widely employed to invert a matrix in the linear detector of the multi-user multi-antenna receiver. Compared to existing numerical results, the suggested stochastic bound is far closer to actual errors. It was put through a half-precision format test and verified in real-world scenarios generated by the Quadriga 2.0 tool and in random randsvd ensemble. The results of experiments show that our method predicts errors that are extremely similar to those observed in simulations. The suggested method can be used to examine the roundoff error in many other applications that require matrix inverse operations.