Nikola Zlatanov

Nikola Zlatanov

This paper characterizes random spherical codebooks over the real additive white Gaussian noise channel in the high signal-to-noise ratio (SNR) regime when the blocklength is fixed and the codebook size grows with SNR. In this regime, the random spherical ensemble exhibits a sharp error-probability transition governed by the intrinsic dimension of the sphere and the codebook-growth scale. Below the critical codebook-growth scale, the ensemble-average error probability vanishes; at the critical scale, it converges to a nontrivial limit; and above that scale, it approaches one. By inverting this transition law, we obtain the high-SNR expansion of the ensemble-achievable data rate for a prescribed error probability. This rate has the same leading high-SNR growth as the corresponding converse rate bound, while reliability enters through the constant-order terms. Consequently, the ratio of the ensemble-achievable rate to the converse rate bound tends to one as the SNR increases. Their additive difference, however, generally approaches a positive blocklength- and reliability-dependent limit. We characterize this limiting rate-bound gap jointly as a function of blocklength and error probability. For every fixed error probability, the gap vanishes as the blocklength increases. We further identify the reliability scalings under which a decreasing error probability prevents the additive gap from vanishing in the large-blocklength limit.

Debamita Ghosh, Manjesh K. Hanawal, Nikola Zlatanov

Holographic metasurface transceivers (HMT) is an emerging technology for enhancing the coverage and rate of wireless communication systems. However, acquiring accurate channel state information in HMT-assisted wireless communication systems is critical for achieving these goals. In this paper, we propose an algorithm for learning the optimal phase-shifts at a HMT for the far-field channel model. Our proposed algorithm exploits the structure of the channel gains in the far-field regions and learns the optimal phase-shifts in presence of noise in the received signals. We prove that the probability that the optimal phase-shifts estimated by our proposed algorithm deviate from the true values decays exponentially in the number of pilot signals. Extensive numerical simulations validate the theoretical guarantees and also demonstrate significant gains as compared to the state-of-the-art policies.

Debamita Ghosh, Haseen Rahman, Manjesh K. Hanawal et al.

Millimeter wave (mmWave) communications have a broad spectrum and can support data rates in the order of gigabits per second, as envisioned in 5G systems. However, they cannot be used for long distances due to their sensitivity to attenuation loss. To enable their use in the 5G network, it requires that the transmission energy be focused in sharp pencil beams. As any misalignment between the transmitter and receiver beam pair can reduce the data rate significantly, it is important that they are aligned as much as possible. To find the best transmit-receive beam pair, recent beam alignment (BA) techniques examine the entire beam space, which might result in a large amount of BA latency. Recent works propose to adaptively select the beams such that the cumulative reward measured in terms of received signal strength or throughput is maximized. In this paper, we develop an algorithm that exploits the unimodal structure of the received signal strengths of the beams to identify the best beam in a finite time using pure exploration strategies. Strategies that identify the best beam in a fixed time slot are more suitable for wireless network protocol design than cumulative reward maximization strategies that continuously perform exploration and exploitation. Our algorithm is named Unimodal Bandit for Best Beam (UB3) and identifies the best beam with a high probability in a few rounds. We prove that the error exponent in the probability does not depend on the number of beams and show that this is indeed the case by establishing a lower bound for the unimodal bandits. We demonstrate that UB3 outperforms the state-of-the-art algorithms through extensive simulations. Moreover, our algorithm is simple to implement and has lower computational complexity.

Nikola Zlatanov, Amin Gohari, Farzad Shahrivari et al.

Classical estimation outputs a single point estimate of an unknown $d$-dimensional vector from an observation. In this paper, we study \emph{$k$-list estimation}, in which a single observation is used to produce a list of $k$ candidate estimates and performance is measured by the expected squared distance from the true vector to the closest candidate. We compare this centralized setting with a symmetric decentralized MMSE benchmark in which $k$ agents observe conditionally i.i.d.\ measurements and each agent outputs its own MMSE estimate. On the centralized side, we show that optimal $k$-list estimation is equivalent to fixed-rate $k$-point vector quantization of the posterior distribution and, under standard regularity conditions, admits an exact high-rate asymptotic expansion with explicit constants and decay rate $k^{-2/d}$. On the decentralized side, we derive lower bounds in terms of the small-ball behavior of the single-agent MMSE error; in particular, when the conditional error density is bounded near the origin, the benchmark distortion cannot decay faster than order $k^{-2/d}$. We further show that if the error density vanishes at the origin, then the decentralized benchmark is provably unable to match the centralized $k^{-2/d}$ exponent, whereas the centralized estimator retains that scaling. Gaussian specializations yield explicit formulas and numerical experiments corroborate the predicted asymptotic behavior. Overall, the results show that, in the scaling with $k$, one observation combined with $k$ carefully chosen candidates can be asymptotically as effective as -- and in some regimes strictly better than -- this MMSE-based decentralized benchmark with $k$ independent observations.

Sergey Isaev, Nikola Zlatanov

This paper presents a phase-difference-based scheme for three-dimensional (3D) line-of-sight (LoS) user localization using a uniform planar array (UPA), applicable to both near-field and far-field regimes under the exact spherical-wave model. Unlike the previously studied two-dimensional (2D) uniform linear array (ULA) case, the 3D UPA case requires jointly exploiting the two array axes in order to recover the user's range, azimuth, and zenith angle. Adjacent-antenna phase-differences are first estimated from uplink pilots and then summed along the array axes to obtain unwrapped phase-differences between widely separated antenna elements. These summed phase-differences enable the construction of multiple three-equation systems whose solutions yield the user's range, azimuth, and zenith angle. We quantify the number of such equation systems, provide a representative closed-form estimator that uses only three phase-difference sums, and propose an all-data nonlinear least-squares estimator that exploits all available sums. Numerical results show that the least-squares estimator, when initialized by the closed-form estimate, achieves Cramér--Rao bound accuracy. Moreover, unlike state-of-the-art baseline schemes, whose performance depends on well-tuned hyperparameters, the proposed estimators are hyperparameter-free.

Vladislav Katkov, Nikola Zlatanov

We present a theoretical framework for recovering the amplitude and carrier phase of a single received RF field with a Rydberg-atom receiver, without injecting an RF local oscillator (LO) into the atoms. The key enabling mechanism is a static DC bias applied to the vapor cell: by Stark-mixing a near-degenerate Rydberg pair, the bias activates an otherwise absent upper optical pathway and closes a phase-sensitive loop within a receiver driven only by the standard probe/coupling pair and the received RF field. For a spatially uniform bias, we derive an effective four-level rotating-frame Hamiltonian of Floquet form and show that the periodic steady state obeys an exact harmonic phase law, so that the $n$th probe harmonic carries the factor $e^{inΦ_S}$. This yields direct estimators for the signal phase and amplitude from a demodulated probe harmonic, with amplitude recovery obtained by inverting an injective harmonic response map. In the high-SNR regime, we derive explicit RMSE laws and use them to identify distinct phase-optimal and amplitude-optimal bias-controlled mixing angles, together with a weighted joint-design criterion and a balanced compromise angle that equalizes the fractional phase and amplitude penalties. We then extend the analysis to nonuniform DC bias through quasistatic spatial averaging and show that bias inhomogeneity reduces coherent gain for phase readout while also reshaping the amplitude-response slope. Numerical examples validate the phase law, illustrate response-map inversion and mixing-angle trade-offs, and quantify the penalties induced by bias nonuniformity. The results establish a minimal route to coherent Rydberg reception of a single RF signal without an auxiliary RF LO in the atoms.

Sina Aeeneh, Nikola Zlatanov, Jiangshan Yu

Majority voting is a simple mathematical function that returns the value that appears most often in a set. As a popular decision fusion technique, the majority voting function (MVF) finds applications in resolving conflicts, where a number of independent voters report their opinions on a classification problem. Despite its importance and its various applications in ensemble learning, data crowd-sourcing, remote sensing, and data oracles for blockchains, the accuracy of the MVF for the general multi-class classification problem has remained unknown. In this paper, we derive a new upper bound on the accuracy of the MVF for the multi-class classification problem. More specifically, we show that under certain conditions, the error rate of the MVF exponentially decays toward zero as the number of independent voters increases. Conversely, the error rate of the MVF exponentially grows with the number of independent voters if these conditions are not met. We first explore the problem for independent and identically distributed voters where we assume that every voter follows the same conditional probability distribution of voting for different classes, given the true classification of the data point. Next, we extend our results for the case where the voters are independent but non-identically distributed. Using the derived results, we then provide a discussion on the accuracy of the truth discovery algorithms. We show that in the best-case scenarios, truth discovery algorithms operate as an amplified MVF and thereby achieve a small error rate only when the MVF achieves a small error rate, and vice versa, achieve a large error rate when the MVF also achieves a large error rate. In the worst-case scenario, the truth discovery algorithms may achieve a higher error rate than the MVF. Finally, we confirm our theoretical results using numerical simulations.

Nikola Zlatanov

This paper proposes a narrowband fully-analog $N$-antenna transmitter that emulates the functionality of a narrowband fully-digital $N$-antenna transmitter. Specifically, in symbol interval $m$, the proposed fully-analog transmitter synthesizes an arbitrary complex excitation vector $\boldsymbol{x}[m]\in\mathbb{C}^N$ with prescribed total power $\|\boldsymbol{x}[m]\|_2^2=P$ from a single RF tone, using only tunable phase-control elements embedded in a passive interferometric programmable network. The programmable network is excited through one input port while the remaining $N-1$ input ports are impedance matched. In the ideal lossless case, the network transfer is unitary and therefore redistributes RF power among antenna ports without dissipative amplitude control. The synthesis task is posed as a unitary state-preparation problem: program a unitary family so that $\boldsymbol{V}(\boldsymbolφ[m])\boldsymbol{e}_1=\boldsymbol{c}[m]$, where $\boldsymbol{c}[m]=\boldsymbol{x}[m]/\sqrt{P}$ and $\|\boldsymbol{c}[m]\|_2=1$. We provide a parameter-minimal realization and a closed-form programming rule: a balanced binary magnitude-splitting tree allocates the desired per-antenna magnitudes $|c_n|$ using $N-1$ tunable split ratios, and a per-antenna output phase bank assigns the target phases using $N$ tunable phase shifts. The resulting architecture uses exactly $2N-1$ real tunable degrees of freedom and admits a deterministic $O(N)$ programming procedure with no iterative optimization, enabling symbol-by-symbol updates. Using representative COTS components, we model the compute-excluded RF-front-end DC power of the proposed fully-analog transmitter and compare it against an equivalent COTS fully-digital array. For $N\le 16$, the comparison indicates significant RF-front-end power savings for the fully-analog architecture under a common delivered antenna-port power normalization.

Nikola Zlatanov

In this paper, I present a completely new type of upper and lower bounds on the right-tail probabilities of continuous random variables with unbounded support and with semi-bounded support from the left. The presented upper and lower right-tail bounds depend only on the probability density function (PDF), its first derivative, and two parameters that are used for tightening the bounds. These tail bounds hold under certain conditions that depend on the PDF, its first and second derivatives, and the two parameters. The new tail bounds are shown to be tight for a wide range of continuous random variables via numerical examples.

Debamita Ghosh, Manjesh K. Hanawal, Nikola Zlatanov

We study wireless power transmission by an energy source to multiple energy harvesting nodes with the aim to maximize the energy efficiency. The source transmits energy to the nodes using one of the available power levels in each time slot and the nodes transmit information back to the energy source using the harvested energy. The source does not have any channel state information and it only knows whether a received codeword from a given node was successfully decoded or not. With this limited information, the source has to learn the optimal power level that maximizes the energy efficiency of the network. We model the problem as a stochastic Multi-Armed Bandits problem and develop an Upper Confidence Bound based algorithm, which learns the optimal transmit power of the energy source that maximizes the energy efficiency. Numerical results validate the performance guarantees of the proposed algorithm and show significant gains compared to the benchmark schemes.

Farzad Shahrivari, Nikola Zlatanov

In this paper, we investigate the problem of classifying feature vectors with mutually independent but non-identically distributed elements. First, we show the importance of this problem. Next, we propose a classifier and derive an analytical upper bound on its error probability. We show that the error probability goes to zero as the length of the feature vectors grows, even when there is only one training feature vector per label available. Thereby, we show that for this important problem at least one asymptotically optimal classifier exists. Finally, we provide numerical examples where we show that the performance of the proposed classifier outperforms conventional classification algorithms when the number of training data is small and the length of the feature vectors is sufficiently high.