arXiv subjects

Vladimir I. Okhmatovski

Publications and source records attributed to Vladimir I. Okhmatovski.

3 recordsLinked to original sources

arXiv2025

Information Theoretic Analysis of a Dual-Band MIMO Cellphone Antenna with ANSYS HFSS SBR+

Historically, the design of antenna arrays has evolved separately from Shannon theory. Shannon theory adopts a probabilistic approach in the design of communication systems, while antenna design approaches have relied on the deterministic Maxwell theory alone. In this paper, we investigate an information-theoretic analysis approach which we apply to evaluate the design of a dual-band, dual-polarized multiple-input multiple-output (MIMO) array on a cellphone. To this end, we use ANSYS HFSS, a commercial electromagnetic (EM) simulation software suitable for the numerical optimization of antenna systems. HFSS is used to obtain an accurate model of the cellphone MIMO antenna array and HFSS SBR+ is utilized to obtain channel matrices for a large number of users. Taking advantage of linear and optimal processing at the cellphone, we estimate the outage probability curves. The curves are then used to determine the diversity gain in a moderate signal-to-noise ratio (SNR) regime and the multiplexing gain at a high SNR regime. This approach is then compared with the method of estimating the diversity gain from the envelope correlation coefficients or the beam-coupling matrix showing substantial differences in the two methodologies.

cs.IT↗

arXiv2025

Capacity Based Design of Slot Array Antennas

Historically, the design of antenna arrays has evolved separately from Shannon theory. Shannon theory adopts a probabilistic approach in the design of communication systems, while antenna design approaches have relied on deterministic Maxwell theory alone. In this paper, we introduce a new approach to the design of antenna arrays based on information theoretic metrics. To this end, we develop a statistical model suitable for the numerical optimization of antenna systems. The model is utilized to obtain the signal-to-noise ratio (SNR), find the optimal power allocation scheme, and establish the associated Shannon capacity. We demonstrate the utility of the new approach on a connected array of slot antennas. To find the impedance matrix of the slot array, we further develop a fast numerical technique based on the analytical form of the spectrum of magnetic current. The utilized spectral approach, albeit its simplicity, shows good match compared with full wave electromagnetic simulation.

stat.AP↗

arXiv2022

A Quantum Computer Amenable Sparse Matrix Equation Solver

Quantum computation offers a promising alternative to classical computing methods in many areas of numerical science, with algorithms that make use of the unique way in which quantum computers store and manipulate data often achieving dramatic improvements in performance over their classical counterparts. The potential efficiency of quantum computers is particularly important for numerical simulations, where the capabilities of classical computing systems are often insufficient for the analysis of real-world problems. In this work, we study problems involving the solution of matrix equations, for which there currently exists no efficient, general quantum procedure. We develop a generalization of the Harrow/Hassidim/Lloyd algorithm by providing an alternative unitary for eigenphase estimation. This unitary, which we have adopted from research in the area of quantum walks, has the advantage of being well defined for any arbitrary matrix equation, thereby allowing the solution procedure to be directly implemented on quantum hardware for any well-conditioned system. The procedure is most useful for sparse matrix equations, as it allows for the inverse of a matrix to be applied with $\mathcal{O}\left(N_{nz}\log\left(N\right)\right)$ complexity, where $N$ is the number of unknowns, and $N_{nz}$ is the total number of nonzero elements in the system matrix. This efficiency is independent of the matrix structure, and hence the quantum procedure can outperform classical methods for many common system types. We show this using the example of sparse approximate inverse (SPAI) preconditioning, which involves the application of matrix inverses for matrices with $N_{nz}=\mathcal{O}\left(N\right)$.

quant-ph↗