arXiv subjects

Athina P. Petropulu

Publications and source records attributed to Athina P. Petropulu.

At least 37 records · Page 2Linked to original sources

arXiv2021

Towards Multi-Functional 6G Wireless Networks: Integrating Sensing, Communication and Security

Integrated sensing and communication (ISAC) has recently emerged as a candidate 6G technology, aiming to unify the two key operations of the future network in spectrum/energy/cost efficient way. ISAC involves communicating information to receivers and simultaneously sensing targets, while both operations use the same waveforms, the same transmitter and ultimately the same network infrastructure. Nevertheless, the inclusion of information signalling into the probing waveform for target sensing raises unique and difficult challenges from the perspective of information security. At the same time, the sensing capability incorporated in the ISAC transmission offers unique opportunities to design secure ISAC techniques. This overview paper discusses these unique challenges and opportunities for the next generation of ISAC networks. We first briefly discuss the fundamentals of waveform design for sensing and communication. Then, we detail the challenges and contradictory objectives involved in securing ISAC transmission, along with state-of-the-art approaches to address them. We then identify the new opportunity of using the sensing capability to obtain knowledge of the targets, as an enabling approach against known weaknesses of PHY security. Finally, we illustrate a low-cost secure ISAC architecture, followed by a series of open research topics. This family of sensing-aided secure ISAC techniques brings a new insight on providing information security, with an eye on robust and hardware-constrained designs tailored for low-cost ISAC devices.

eess.SP↗

arXiv2020

Joint Beamforming and Dynamic Relay Positioning for mmWave Urban Communications

The use of millimeter wave (mmWave) spectrum for commercial wireless communications is expected to offer data rates in the order of Gigabits-per-second, thus able to support future applications such as Vehicle-to-Vehicle or Vehicle-to-Infrastructure communication. However, especially in urban settings, mmWave signal propagation is sensitive to blockage by surrounding objects, resulting in significant signal attenuation. One approach to mitigate the effect of attenuation is through multi-hop communication with the help of relays. Leveraging the unique characteristics of the mmWave medium, we consider a single-source/destination $2$-hop system, where a cluster of spatially distributed and reconfigurable relays is used to cooperatively amplify-and-forward the source signal to the destination. Our system evolves in time slots, during which not only are optimal beamforming weights centrally determined, but also future relay positions for the subsequent time slot are optimally selected, jointly maximizing the expected signal-to-interference+noise ratio at the destination. Optimal predictive relay positioning is achieved by formulating a 2-stage stochastic programming problem, which is efficiently approximated via a conditional sample-average-approximation surrogate, and solved in a purely distributed fashion across relays. The efficacy of the proposed near-optimal positioning policy is corroborated by comparison against a randomized relay positioning policy, clearly confirming its superiority.

eess.SP↗

arXiv2020

A Deep Learning Framework for Optimization of MISO Downlink Beamforming

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for real-time implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems.

cs.IT↗

arXiv2019

Cooperative Beamforming with Predictive Relay Selection for Urban mmWave Communications

While millimeter wave (mmWave) communications promise high data rates, their sensitivity to blockage and severe signal attenuation presents challenges in their deployment in urban settings. To overcome these effects, we consider a distributed cooperative beamforming system, which relies on static relays deployed in clusters with similar channel characteristics, and where, at every time instance, only one relay from each cluster is selected to participate in beamforming to the destination. To meet the quality-of-service guarantees of the network, a key prerequisite for beamforming is relay selection. However, as the channels change with time, relay selection becomes a resource demanding task. Indeed, estimation of channel state information for all candidate relays, essential for relay selection, is a process that takes up bandwidth, wastes power and introduces latency and interference in the network. We instead propose a unique, predictive scheme for resource efficient relay selection, which exploits the special propagation patterns of the mmWave medium, and can be executed distributively across clusters, and in parallel to optimal beamforming-based communication. The proposed predictive scheme efficiently exploits spatiotemporal channel correlations with current and past networkwide Received Signal Strength (RSS), the latter being invariant to relay cluster size, measured sequentially during the operation of the system. Our numerical results confirm that our proposed relay selection strategy outperforms any randomized selection policy that does not exploit channel correlations, whereas, at the same time, it performs very close to an ideal scheme that uses complete, cluster size dependent RSS, and offers significant savings in terms of channel estimation overhead, providing substantially better network utilization, especially in dense topologies, typical in mmWave networks.

eess.SP↗

arXiv2017

Spatially Controlled Relay Beamforming: $2$-Stage Optimal Policies

The problem of enhancing Quality-of-Service (QoS) in power constrained, mobile relay beamforming networks, by optimally and dynamically controlling the motion of the relaying nodes, is considered, in a dynamic channel environment. We assume a time slotted system, where the relays update their positions before the beginning of each time slot. Modeling the wireless channel as a Gaussian spatiotemporal stochastic field, we propose a novel $2$-stage stochastic programming problem formulation for optimally specifying the positions of the relays at each time slot, such that the expected QoS of the network is maximized, based on causal Channel State Information (CSI) and under a total relay transmit power budget. This results in a schema where, at each time slot, the relays, apart from optimally beamforming to the destination, also optimally, predictively decide their positions at the next time slot, based on causally accumulated experience. Exploiting either the Method of Statistical Differentials, or the multidimensional Gauss-Hermite Quadrature Rule, the stochastic program considered is shown to be approximately equivalent to a set of simple subproblems, which are solved in a distributed fashion, one at each relay. Optimality and performance of the proposed spatially controlled system are also effectively assessed, under a rigorous technical framework; strict optimality is rigorously demonstrated via the development of a version of the Fundamental Lemma of Stochastic Control, and, performance-wise, it is shown that, quite interestingly, the optimal average network QoS exhibits an increasing trend across time slots, despite our myopic problem formulation. Numerical simulations are presented, experimentally corroborating the success of the proposed approach and the validity of our theoretical predictions.

math.OC↗

arXiv2016

MIMO Secret Communications Against an Active Eavesdropper

This paper considers a scenario in which an Alice-Bob pair wishes to communicate in secret in the presence of an active Eve, who is capable of jamming as well as eavesdropping in Full-Duplex (FD) mode. As countermeasure, Bob also operates in FD mode, using a subset of its antennas to act as receiver, and the remaining antennas to act as jammer and transmit noise. With a goal to maximize the achievable secrecy degrees of freedom (S.D.o.F.) of the system, we provide the optimal transmit/receive antennas allocation at Bob, based on which we determine in closed form the maximum achievable S.D.o.F.. We further investigate the adverse scenario in which Eve knows Bob's transmission strategy and optimizes its transmit/receive antennas allocation in order to minimize the achievable S.D.o.F.. For that case we find the worst-case achievable S.D.o.F.. We also provide a method for constructing the precoding matrices of Alice and Bob, based on which the maximum S.D.o.F. can be achieved. Numerical results validate the theoretical findings and demonstrate the performance of the proposed method in realistic settings.

cs.IT↗

arXiv2016

Uniform {\varepsilon}-Stability of Distributed Nonlinear Filtering over DNAs: Gaussian-Finite HMMs

In this work, we study stability of distributed filtering of Markov chains with finite state space, partially observed in conditionally Gaussian noise. We consider a nonlinear filtering scheme over a Distributed Network of Agents (DNA), which relies on the distributed evaluation of the likelihood part of the centralized nonlinear filter and is based on a particular specialization of the Alternating Direction Method of Multipliers (ADMM) for fast average consensus. Assuming the same number of consensus steps between any two consecutive noisy measurements for each sensor in the network, we fully characterize a minimal number of such steps, such that the distributed filter remains uniformly stable with a prescribed accuracy level, {\varepsilon} \in (0,1], within a finite operational horizon, T, and across all sensors. Stability is in the sense of the \ell_1-norm between the centralized and distributed versions of the posterior at each sensor, and at each time within T. Roughly speaking, our main result shows that uniform {\varepsilon}-stability of the distributed filtering process depends only loglinearly on T and (roughly) the size of the network, and only logarithmically on 1/{\varepsilon}. If this total loglinear bound is fulfilled, any additional consensus iterations will incur a fully quantified further exponential decay in the consensus error. Our bounds are universal, in the sense that they are independent of the particular structure of the Gaussian Hidden Markov Model (HMM) under consideration.

math.ST↗

arXiv2016

Asymptotically Optimal Discrete Time Nonlinear Filters From Stochastically Convergent State Process Approximations

We consider the problem of approximating optimal in the Minimum Mean Squared Error (MMSE) sense nonlinear filters in a discrete time setting, exploiting properties of stochastically convergent state process approximations. More specifically, we consider a class of nonlinear, partially observable stochastic systems, comprised by a (possibly nonstationary) hidden stochastic process (the state), observed through another conditionally Gaussian stochastic process (the observations). Under general assumptions, we show that, given an approximating process which, for each time step, is stochastically convergent to the state process, an approximate filtering operator can be defined, which converges to the true optimal nonlinear filter of the state in a strong and well defined sense. In particular, the convergence is compact in time and uniform in a completely characterized measurable set of probability measure almost unity, also providing a purely quantitative justification of Egoroff's Theorem for the problem at hand. The results presented in this paper can form a common basis for the analysis and characterization of a number of heuristic approaches for approximating optimal nonlinear filters, such as approximate grid based techniques, known to perform well in a variety of applications.

math.ST↗

arXiv2016

Grid Based Nonlinear Filtering Revisited: Recursive Estimation & Asymptotic Optimality

We revisit the development of grid based recursive approximate filtering of general Markov processes in discrete time, partially observed in conditionally Gaussian noise. The grid based filters considered rely on two types of state quantization: The \textit{Markovian} type and the \textit{marginal} type. We propose a set of novel, relaxed sufficient conditions, ensuring strong and fully characterized pathwise convergence of these filters to the respective MMSE state estimator. In particular, for marginal state quantizations, we introduce the notion of \textit{conditional regularity of stochastic kernels}, which, to the best of our knowledge, constitutes the most relaxed condition proposed, under which asymptotic optimality of the respective grid based filters is guaranteed. Further, we extend our convergence results, including filtering of bounded and continuous functionals of the state, as well as recursive approximate state prediction. For both Markovian and marginal quantizations, the whole development of the respective grid based filters relies more on linear-algebraic techniques and less on measure theoretic arguments, making the presentation considerably shorter and technically simpler.

math.ST↗

arXiv2016

Mobile Beamforming & Spatially Controlled Relay Communications

We consider stochastic motion planning in single-source single-destination robotic relay networks, under a cooperative beamforming framework. Assuming that the communication medium constitutes a spatiotemporal stochastic field, we propose a 2-stage stochastic programming formulation of the problem of specifying the positions of the relays, such that the expected reciprocal of their total beamforming power is maximized. Stochastic decision making is made on the basis of random causal CSI. Recognizing the intractability of the original problem, we propose a lower bound relaxation, resulting to a nontrivial optimization problem with respect to the relay locations, which is equivalent to a small set of simple, tractable subproblems. Our formulation results in spatial controllers with a predictive character; at each time slot, the new relay positions should be such that the expected power reciprocal at the next time slot is maximized. Quite interestingly, the optimal control policy to the relaxed problem is purely selective; under a certain sense, only the best relay should move.

eess.SY↗

arXiv2016

Improving Wireless Physical Layer Security via Exploiting Co-Channel Interference

This paper considers a scenario in which a source-destination pair needs to establish a confidential connection against an external eavesdropper, aided by the interference generated by another source-destination pair that exchanges public messages. The goal is to compute the maximum achievable secrecy degrees of freedom (S.D.o.F) region of a MIMO two-user wiretap network. First, a cooperative secrecy transmission scheme is proposed, whose feasible set is shown to achieve all S.D.o.F. pairs on the S.D.o.F. region boundary. In this way, the determination of the S.D.o.F. region is reduced to a problem of maximizing the S.D.o.F. pair over the proposed transmission scheme. The maximum achievable S.D.o.F. region boundary points are obtained in closed form, and the construction of the precoding matrices achieving the maximum S.D.o.F. region boundary is provided. The obtained analytical expressions clearly show the relation between the maximum achievable S.D.o.F. region and the number of antennas at each terminal.

cs.IT↗

arXiv2015

Optimum Design for Coexistence Between Matrix Completion Based MIMO Radars and a MIMO Communication System

Recently proposed multiple input multiple output radars based on matrix completion (MIMO-MC) employ sparse sampling to reduce the amount of data that need to be forwarded to the radar fusion center, and as such enable savings in communication power and bandwidth. This paper proposes designs that optimize the sharing of spectrum between a MIMO-MC radar and a communication system, so that the latter interferes minimally with the former. First, the communication system transmit covariance matrix is designed to minimize the effective interference power (EIP) to the radar receiver, while maintaining certain average capacity and transmit power for the communication system. Two approaches are proposed, namely a noncooperative and a cooperative approach, with the latter being applicable when the radar sampling scheme is known at the communication system. Second, a joint design of the communication transmit covariance matrix and the MIMO-MC radar sampling scheme is proposed, which achieves even further EIP reduction.

cs.IT↗

arXiv2015

Sequential Channel State Tracking & SpatioTemporal Channel Prediction in Mobile Wireless Sensor Networks

We propose a nonlinear filtering framework for approaching the problems of channel state tracking and spatiotemporal channel gain prediction in mobile wireless sensor networks, in a Bayesian setting. We assume that the wireless channel constitutes an observable (by the sensors/network nodes), spatiotemporal, conditionally Gaussian stochastic process, which is statistically dependent on a set of hidden channel parameters, called the channel state. The channel state evolves in time according to a known, non stationary, nonlinear and/or non Gaussian Markov stochastic kernel. This formulation results in a partially observable system, with a temporally varying global state and spatiotemporally varying observations. Recognizing the intractability of general nonlinear state estimation, we advocate the use of grid based approximate filters as an effective and robust means for recursive tracking of the channel state. We also propose a sequential spatiotemporal predictor for tracking the channel gains at any point in time and space, providing real time sequential estimates for the respective channel gain map, for each sensor in the network. Additionally, we show that both estimators converge towards the true respective MMSE optimal estimators, in a common, relatively strong sense. Numerical simulations corroborate the practical effectiveness of the proposed approach.

stat.AP↗

arXiv2014

MIMO-MC Radar: A MIMO Radar Approach Based on Matrix Completion

In a typical MIMO radar scenario, transmit nodes transmit orthogonal waveforms, while each receive node performs matched filtering with the known set of transmit waveforms, and forwards the results to the fusion center. Based on the data it receives from multiple antennas, the fusion center formulates a matrix, which, in conjunction with standard array processing schemes, such as MUSIC, leads to target detection and parameter estimation. In MIMO radars with compressive sensing (MIMO-CS), the data matrix is formulated by each receive node forwarding a small number of compressively obtained samples. In this paper, it is shown that under certain conditions, in both sampling cases, the data matrix at the fusion center is low-rank, and thus can be recovered based on knowledge of a small subset of its entries via matrix completion (MC) techniques. Leveraging the low-rank property of that matrix, we propose a new MIMO radar approach, termed, MIMO-MC radar, in which each receive node either performs matched filtering with a small number of randomly selected dictionary waveforms or obtains sub-Nyquist samples of the received signal at random sampling instants, and forwards the results to a fusion center. Based on the received samples, and with knowledge of the sampling scheme, the fusion center partially fills the data matrix and subsequently applies MC techniques to estimate the full matrix. MIMO-MC radars share the advantages of the recently proposed MIMO-CS radars, i.e., high resolution with reduced amounts of data, but unlike MIMO-CS radars do not require grid discretization. The MIMO-MC radar concept is illustrated through a linear uniform array configuration, and its target estimation performance is demonstrated via simulations.

cs.IT↗

arXiv2013

Matrix Completion in Colocated MIMO Radar: Recoverability, Bounds & Theoretical Guarantees

It was recently shown that low rank matrix completion theory can be employed for designing new sampling schemes in the context of MIMO radars, which can lead to the reduction of the high volume of data typically required for accurate target detection and estimation. Employing random samplers at each reception antenna, a partially observed version of the received data matrix is formulated at the fusion center, which, under certain conditions, can be recovered using convex optimization. This paper presents the theoretical analysis regarding the performance of matrix completion in colocated MIMO radar systems, exploiting the particular structure of the data matrix. Both Uniform Linear Arrays (ULAs) and arbitrary 2-dimensional arrays are considered for transmission and reception. Especially for the ULA case, under some mild assumptions on the directions of arrival of the targets, it is explicitly shown that the coherence of the data matrix is both asymptotically and approximately optimal with respect to the number of antennas of the arrays involved and further, the data matrix is recoverable using a subset of its entries with minimal cardinality. Sufficient conditions guaranteeing low matrix coherence and consequently satisfactory matrix completion performance are also presented, including the arbitrary 2-dimensional array case.

cs.IT↗

arXiv2013

Improving Physical Layer Secrecy Using Full-Duplex Jamming Receivers

This paper studies secrecy rate optimization in a wireless network with a single-antenna source, a multi-antenna destination and a multi-antenna eavesdropper. This is an unfavorable scenario for secrecy performance as the system is interference-limited. In the literature, assuming that the receiver operates in half duplex (HD) mode, the aforementioned problem has been addressed via use of cooperating nodes who act as jammers to confound the eavesdropper. This paper investigates an alternative solution, which assumes the availability of a full duplex (FD) receiver. In particular, while receiving data, the receiver transmits jamming noise to degrade the eavesdropper channel. The proposed self-protection scheme eliminates the need for external helpers and provides system robustness. For the case in which global channel state information is available, we aim to design the optimal jamming covariance matrix that maximizes the secrecy rate and mitigates loop interference associated with the FD operation. We consider both fixed and optimal linear receiver design at the destination, and show that the optimal jamming covariance matrix is rank-1, and can be found via an efficient 1-D search. For the case in which only statistical information on the eavesdropper channel is available, the optimal power allocation is studied in terms of ergodic and outage secrecy rates. Simulation results verify the analysis and demonstrate substantial performance gain over conventional HD operation at the destination.

cs.IT↗

arXiv2013

On the Coherence Properties of Random Euclidean Distance Matrices

In the present paper we focus on the coherence properties of general random Euclidean distance matrices, which are very closely related to the respective matrix completion problem. This problem is of great interest in several applications such as node localization in sensor networks with limited connectivity. Our results can directly provide the sufficient conditions under which an EDM can be successfully recovered with high probability from a limited number of measurements.

cs.IT↗

arXiv2013

Target Estimation in Colocated MIMO Radar via Matrix Completion

We consider a colocated MIMO radar scenario, in which the receive antennas forward their measurements to a fusion center. Based on the received data, the fusion center formulates a matrix which is then used for target parameter estimation. When the receive antennas sample the target returns at Nyquist rate, and assuming that there are more receive antennas than targets, the data matrix at the fusion center is low-rank. When each receive antenna sends to the fusion center only a small number of samples, along with the sample index, the receive data matrix has missing elements, corresponding to the samples that were not forwarded. Under certain conditions, matrix completion techniques can be applied to recover the full receive data matrix, which can then be used in conjunction with array processing techniques, e.g., MUSIC, to obtain target information. Numerical results indicate that good target recovery can be achieved with occupancy of the receive data matrix as low as 50%.

cs.IT↗