arXiv subjects

Sergey Loyka

Publications and source records attributed to Sergey Loyka.

At least 19 recordsLinked to original sources

arXiv2023

Optimal Power Assignment for MIMO Channels Under Joint Total and Per-Group Power Constraints

In this paper we consider a communication system with one transmitter and one receiver. The transmit antennas are partitioned into disjoint groups, and each group must satisfy an average power constraint in addition to the standard overall one. The optimal power allocation (OPA) for the transmit antennas is obtained for the following cases: (i) fixed multiple-input multiple-output (MIMO) orthogonal channel, (ii) i.i.d. fading MIMO orthogonal channel, and (iii) i.i.d. Rayleigh fading multiple-input single-output (MISO) and MIMO channels. The channel orthogonality is encountered in the practical case of the massive MIMO channel under favorable propagation conditions. The closed-form solution to the OPA for a fixed channel is found using the Karush-Kuhn-Tucker (KKT) conditions and it is similar to the standard water-filling procedure while the effect of the per-group average power constraint is added. For a fading channel, an algorithm is proposed to give the OPA, and the algorithm's convergence is proved via a majorization inequality and a Schur-concavity property.

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arXiv2022

The Secrecy Capacity of The Gaussian Wiretap Channel with Rate-Limited Help

The Gaussian wiretap channel with rate-limited help, available at the legitimate receiver (Rx) or/and transmitter (Tx), is studied under various channel configurations (degraded, reversely degraded and non-degraded). In the case of Rx help and all channel configurations, the rate-limited help results in a secrecy capacity boost equal to the help rate irrespective of whether the help is secure or not, so that the secrecy of help does not provide any capacity increase. The secrecy capacity is positive for the reversely-degraded channel (where the no-help secrecy capacity is zero) and no wiretap coding is needed to achieve it. More noise at the legitimate receiver can sometimes result in higher secrecy capacity. The secrecy capacity with Rx help is not increased even if the helper is aware of the message being transmitted. The same secrecy capacity boost also holds if non-secure help is available to the transmitter (encoder), in addition to or instead of the same Rx help, so that, in the case of the joint Tx/Rx help, one help link can be omitted without affecting the capacity. If Rx/Tx help links are independent of each other, then the boost in the secrecy capacity is the sum of help rates and no link can be omitted without a loss in the capacity. Non-singular correlation of the receiver and eavesdropper noises does not affect the secrecy capacity and non-causal help does not bring in any capacity increase over the causal one.

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arXiv2022

On the Capacity of Gaussian MIMO Channels with Memory

The operational capacity of Gaussian MIMO channels with memory was obtained by Brandenburg and Wyner in [9] under certain mild assumptions on the channel impulse response and its noise covariance matrix, which essentuially require channel memory to be not too strong. This channel was also considered by Tsybakov in [10] and its information capacity was obtained in some cases. It was further conjectured, based on numerical evidence, that these capacities are the same in all cases. This conjecture is proved here. An explicit closed-form expression for the optimal input power spectral density matrix is also given. The obtained result is further extended to the case of joint constraints, including per-antenna and interference power constraints as well as energy harvesting constraints. These results imply the information-theoretic optimality of OFDM-type transmission systems for such channels with memory.

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arXiv2021

Favorable Propagation for Massive MIMO with Circular and Cylindrical Antenna Arrays

Massive MIMO systems with uniform circular and cylindrical antenna arrays are studied. Favorable propagation property is rigorously shown to hold asymptotically in LOS environment for a fixed antenna spacing and under some mild conditions. The analysis is based on a novel method using a Bessel function expansion, a new bounding technique and a simplified representation of inter-user interference for array geometries obeying Kronecker structure.

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arXiv2021

Time-Invariant Feedback Strategies Do Not Increase Capacity of AGN Channels Driven by Stable and Certain Unstable Autoregressive Noise

The capacity of additive Gaussian noise (AGN) channels, $Y_t=X_t+V_t, t=1, \ldots, n$, $\frac{1}{n} {\bf E}\big\{\sum_{t=1}^n |X_t|^2 \big\}\leq κ, κ\in [0,\infty)$, with time-invariant channel input feedback strategies, is characterized and conditions are identified for entropy rates, and limit of average power to exist, when the noise is described by {\it stable and unstable} autoregressive models, AR$(c)$, $V_t=cV_{t-1}+ W_t, V_0=v_0, t=1, \ldots, n$, where $c\in (-\infty,\infty)$, $W_t, t=1,\ldots, n$, is a zero mean, variance $K_W$, independent Gaussian sequence, independent of $V_0$. For stable AR$(c), c\in (-1,1)$ the conditions are necessary and sufficient for asymptotic stationarity of the processes $(X_t, Y_t), t=1, 2, \ldots$. New closed form capacity formulas and lower bounds are derived, for the AR$(c), c\in (-\infty,\infty)$ noise, which are fundamentally different from existing formulas in the literature, and illustrate multiple regimes of capacity, as a function of the parameters $(c,K_W,κ)$, as follows.\\ 1) feedback increases capacity for the regime, $c^2 \in (1, \infty),$ for $κ> \frac{K_W\big(1+\sqrt{4c^2-3}\big)}{2\big(c^2-1\big)^2}$, \\ 2) feedback does not increase capacity for the regime $c^2 \in (1, \infty)$, for $κ\leq \frac{K_W\big(1+\sqrt{4c^2-3}\big)}{2\big(c^2-1\big)^2}$, and \\ 3) feedback does not increase capacity for the regime $c \in [-1,1]$, for $ κ\in [0,\infty)$.

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arXiv2020

Simple Closed-Form Approximations for Achievable Information Rates of Coded Modulation Systems

The intuitive sphere-packing argument is used to obtain analytically-tractable closed-form approximations for achievable information rates of coded modulation transmission systems, for which only analytically-intractable expressions are available in the literature. These approximations provide a number of insights, possess useful properties and facilitate design/optimization of such systems. They apply to constellations of various cardinalities (including large ones), are simple yet reasonably accurate over the whole signal-to-noise ratio range, and compare favorably to the achieved rates of recent state-of-the art experiments.

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arXiv2020

Comments on "Precoding and Artificial Noise Design for Cognitive MIMOME Wiretap Channels"

Several gaps and errors in [1] are identified and corrected. While accommodating these corrections, a rigours proof is given that the successive convex approximation algorithm in [1] for secrecy rate maximization (SRM) does generate an increasing and bounded sequence of true secrecy rates and hence converges. It is further shown that its convergence point is a KKT point of the original SRM problem and, if the original problem is convex, this convergence point is globally-optimal, which is not necessarily the case in general. An interlacing property of the sequences of the true and approximate secrecy rates is established.

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arXiv2020

Algorithms for Globally-Optimal Secure Signaling over Gaussian MIMO Wiretap Channels Under Interference Constraints

Multi-user Gaussian MIMO wiretap channel is considered under interference power constraints (IPC), in addition to the total transmit power constraint (TPC). Algorithms for \textit{global} maximization of its secrecy rate are proposed. Their convergence to the secrecy capacity is rigorously proved and a number of properties are established analytically. Unlike known algorithms, the proposed ones are not limited to the MISO case and are proved to converge to a \textit{global} rather than local optimum in the general MIMO case, even when the channel is not degraded. In practice, the convergence is fast as only a small to moderate number of Newton steps is required to achieve a high precision level. The interplay of TPC and IPC is shown to result in an unusual property when an optimal point of the max-min problem does not provide an optimal transmit covariance matrix in some (singular) cases. To address this issue, an algorithm is developed to compute an optimal transmit covariance matrix in those singular cases. It is shown that this algorithm also solves the dual (nonconvex) problems of \textit{globally} minimizing the total transmit power subject to the secrecy and interference constraints; it provides the minimum transmit power and respective signaling strategy needed to achieve the secrecy capacity, hence allowing power savings.

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arXiv2020

On The Capacity of Gaussian MIMO Channels Under Interference Constraints (full version)

Gaussian MIMO channel under total transmit and multiple interference power constraints (TPC and IPCs) is considered. A closed-form solution for its optimal transmit covariance matrix is obtained in the general case (up to dual variables). A number of more explicit closed-form solutions are obtained in some special cases, including full-rank and rank-1 (beamforming) solutions, which differ significantly from the well-known water-filling solutions (e.g. signaling on the channel eigenmodes is not optimal anymore and the capacity can be zero for non-zero transmit power). A whitening filter is shown to be an important part of optimal precoding under interference constraints. Capacity scaling with transmit power is studied: its qualitative behaviour is determined by a natural linear-algebraic structure induced by MIMO channels of multiple users. A simple rank condition is given to characterize the cases where spectrum sharing is possible. An interplay between the TPC and IPCs is investigated, including the transition from power-limited to interference-limited regimes. A number of unusual properties of an optimal covariance matrix under IPCs are pointed out and a bound on its rank is established. Partial null forming known in the adaptive antenna array literature is shown to be optimal from the information-theoretic perspective as well in some cases.

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arXiv2020

Optimal Location of Cellular Base Station via Convex Optimization

An optimal base station (BS) location depends on the traffic (user) distribution, propagation pathloss and many system parameters, which renders its analytical study difficult so that numerical algorithms are widely used instead. In this paper, the problem is studied analytically. First, it is formulated as a convex optimization problem to minimize the total BS transmit power subject to quality-of-service (QoS) constraints, which also account for fairness among users. Due to its convex nature, Karush-Kuhn-Tucker (KKT) conditions are used to characterize a globally-optimum location as a convex combination of user locations, where convex weights depend on user parameters, pathloss exponent and overall geometry of the problem. Based on this characterization, a number of closed-form solutions are obtained. In particular, the optimum BS location is the mean of user locations in the case of free-space propagation and identical user parameters. If the user set is symmetric (as defined in the paper), the optimal BS location is independent of pathloss exponent, which is not the case in general. The analytical results show the impact of propagation conditions as well as system and user parameters on optimal BS location and can be used to develop design guidelines.

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arXiv2020

The Capacity and Optimal Signaling for Gaussian MIMO Channels Under Interference Constraints (full version)

Gaussian MIMO channel under total transmit and interference power constraints (TPC and IPC) is considered. A closed-form solution for the optimal transmit covariance matrix in the general case is obtained using the KKT-based approach (up to dual variables). While closed-from solutions for optimal dual variables are possible in special cases, an iterative bisection algorithm (IBA) is proposed to find the optimal dual variables in the general case and its convergence is proved for some special cases. Numerical experiments illustrate its efficient performance. Bounds for the optimal dual variables are given, which facilitate numerical solutions. An interplay between the TPC and IPC is studied, including the transition from power-limited to interference-limited regimes as the total transmit power increases. Sufficient and necessary conditions for each constraint to be redundant are given. A number of explicit closed-form solutions are obtained, including full-rank and rank-1 (beamforming) cases as well as the case of identical eigenvectors (typical for massive MIMO settings). A bound on the rank of optimal covariance is established. A number of unusual properties of optimal covariance matrix are pointed out.

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arXiv2018

On The Capacity of Gaussian MIMO Channels Under The Joint Power Constraints

The capacity and optimal signaling over a fixed Gaussian MIMO channel are considered under the joint total and per-antenna power constraints (TPC and PAC). While the general case remains an open problem, a closed-form full-rank solution is obtained along with its sufficient and necessary conditions. The conditions for each constraint to be inactive are established. The high and low-SNR regimes are studied. Isotropic signaling is shown to be optimal in the former case while rank-1 signaling (beamforming) is not necessarily optimal in the latter case. Unusual properties of optimal covariance under the joint constraints are pointed out.

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arXiv2017

Optimal Estimation via Nonanticipative Rate Distortion Function and Applications to Time-Varying Gauss-Markov Processes

In this paper, we develop {finite-time horizon} causal filters using the nonanticipative rate distortion theory. We apply the {developed} theory to {design optimal filters for} time-varying multidimensional Gauss-Markov processes, subject to a mean square error fidelity constraint. We show that such filters are equivalent to the design of an optimal \texttt{\{encoder, channel, decoder\}}, which ensures that the error satisfies {a} fidelity constraint. Moreover, we derive a universal lower bound on the mean square error of any estimator of time-varying multidimensional Gauss-Markov processes in terms of conditional mutual information. Unlike classical Kalman filters, the filter developed is characterized by a reverse-waterfilling algorithm, which ensures {that} the fidelity constraint is satisfied. The theoretical results are demonstrated via illustrative examples.

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arXiv2016

Feedback Does Not Increase the Capacity of Compound Channels with Additive Noise

A discrete compound channel with memory is considered, where no stationarity, ergodicity or information stability is required, and where the uncertainty set can be arbitrary. When the discrete noise is additive but otherwise arbitrary and there is no cost constraint on the input, it is shown that the causal feedback does not increase the capacity. This extends the earlier result obtained for general single-state channels with full transmitter (Tx) channel state information (CSI) to the compound setting. It is further shown that, for this compound setting and under a mild technical condition on the additive noise, the addition of the full Tx CSI does not increase the capacity either, so that the worst-case and compound channel capacities are the same. This can also be expressed as a saddle-point in the information-theoretic game between the transmitter (who selects the input distribution) and the nature (who selects the channel state), even though the objective function (the inf-information rate) is not convex/concave in the right way. Cases where the Tx CSI does increase the capacity are identified. Conditions under which the strong converse holds for this channel are studied. The ergodic behaviour of the worst-case noise in otherwise information-unstable channel is shown to be both sufficient and necessary for the strong converse to hold, including feedback and no feedback cases.

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arXiv2016

Optimal Signaling for Secure Communications over Gaussian MIMO Wiretap Channels

Optimal signalling over the Gaussian MIMO wire-tap channel is studied under the total transmit power constraint. A closed-form solution for an optimal transmit covariance matrix is obtained when the channel is strictly degraded. In combination with the rank-1 solution, this provides the complete characterization of the optimal covariance for the case of two transmit antennas. The cases of weak eavesdropper and high SNR are considered. It is shown that the optimal covariance does not converge to a scaled identity in the high-SNR regime. Necessary optimality conditions and a tight upper bound on the rank of an optimal covariance matrix are established for the general case, along with a lower bound to the secrecy capacity, which is tight in a number of scenarios.

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arXiv2016

Rank-Deficient Solutions for Optimal Signaling over Wiretap MIMO Channels

Capacity-achieving signaling strategies for the Gaussian wiretap MIMO channel are investigated without the degradedness assumption. In addition to known solutions, a number of new rank-deficient solutions for the optimal transmit covariance matrix are obtained. The case of a weak eavesdropper is considered in detail and the optimal covariance is established in an explicit, closed form with no extra assumptions. This provides lower and upper bounds to the secrecy capacity in the general case with a bounded gap, which are tight for a weak eavesdropper or/and low SNR. Closed form solutions are also obtained for isotropic and omnidirectional eavesdroppers, based on which lower and upper bounds to the secrecy capacity are established in the general case. Sufficient and necessary conditions for optimality of 3 popular transmission techniques, namely the zero-forcing (ZF), the standard water-filling (WF) over the channel eigenmodes and the isotropic signaling (IS), are established for the MIMO wiretap channel. These solutions are appealing due to their lower complexity. In particular, no wiretap codes are needed for the ZF transmission, and no precoding or feedback is needed for the isotropic signaling.

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arXiv2016

A General Formula for Compound Channel Capacity

A general formula for the capacity of arbitrary compound channels with the receiver channel state information is obtained using the information density approach. No assumptions of ergodicity, stationarity or information stability are made and the channel state set is arbitrary. A direct (constructive) proof is given. To prove achievability, we generalize Feinstein Lemma to the compound channel setting, and to prove converse, we generalize Verdu-Han Lemma to the same compound setting. A notion of a uniform compound channel is introduced and the general formula is shown to reduce to the familiar $\sup-\inf$ expression for such channels. As a by-product, the arbitrary varying channel capacity is established under maximum error probability and deterministic coding. Conditions are established under which the worst-case and compound channel capacities are equal so that the full channel state information at the transmitter brings in no advantage. The compound inf-information rate plays a prominent role in the general formula. Its properties are studied and a link between information-unstable and information-stable regimes of a compound channel is established. The results are extended to include $\varepsilon$-capacity of compound channels. Sufficient and necessary conditions for the strong converse to hold are given.

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arXiv2016

Capacity Achieving Distributions & Information Lossless Randomized Strategies for Feedback Channels with Memory: The LQG Theory of Directed Information-Part II

A methodology is developed to realized optimal channel input conditional distributions, which maximize the finite-time horizon directed information, for channels with memory and feedback, by information lossless randomized strategies. The methodology is applied to general Time-Varying Multiple Input Multiple Output (MIMO) Gaussian Linear Channel Models (G-LCMs) with memory, subject to average transmission cost constraints of quadratic form. The realizations of optimal distributions by randomized strategies are shown to exhibit a decomposion into a deterministic part and a random part. The decomposition reveals the dual role of randomized strategies, to control the channel output process and to transmit new information over the channels. Moreover, a separation principle is shown between the computation of the optimal deterministic part and the random part of the randomized strategies. The dual role of randomized strategies generalizes the Linear-Quadratic-Gaussian (LQG) stochastic optimal control theory to directed information pay-offs. The characterizations of feedback capacity are obtained from the per unit time limits of finite-time horizon directed information, without imposing á priori assumptions, such as, stability of channel models or ergodicity of channel input and output processes. For time-invariant MIMO G-LCMs with memory, it is shown that whether feedback increases capacity, is directly related to the channel parameters and the transmission cost function, through the solutions of Riccati matrix equations, and moreover for unstable channels, feedback capacity is non-zero, provided the power exceeds a critical level.

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