arXiv subjects

Hesham El Gamal

Publications and source records attributed to Hesham El Gamal.

At least 19 recordsLinked to original sources

arXiv2025

Incomplete Multiview Learning via Wyner Common Information

Incomplete multiview clustering is of high recent interest, fueled by the advancement of common information-based deep multiview learning. The practical scenarios where unpaired multiview data with missing values have wide applications in generative learning, cross-modal retrieval, and wireless device identification problems. Following the perspective that the shared information between the incomplete multiview data aligns with the cluster targets, recent works have generalized the well-known common information frameworks in information theory multiview learning problems, with improved performance reported. Different from previous works, we extend the frameworks to incomplete multiview clustering problems and propose an efficient solver: Wyner Incomplete MultiView Clustering (WyIMVC). Interestingly, the common randomness in WyIMVC allows for joint clustering and missing value inference in contrast to the compared methods in the literature. Moreover, leveraging the difference-of-convex structure of the formulated problems, we propose an efficient solver with a convergence guarantee independent of initialization. Empirically, our solver outperforms the state-of-the-art solvers in a range of incomplete multiview datasets with varying numbers of views and dimensions.

cs.IT↗

arXiv2024

Error Free Quantum Target Finding: When Sequential Detection Meets High Dimensional Entanglement

We present a new method for target finding and ranging in Lidar applications using high-dimensional Bell states. Combined with a sequential decision rule, this scheme asymptotically achieves zero error probability with finite energy expenditure. This result stems from the synergy of high-dimensional entanglement suppressing false positives and the sequential decision rule optimizing energy usage. It effectively provides a pathway to unbounded quantum advantage over classical methods and has substantial implications for high-precision sensing in noisy environments.

quant-ph↗

arXiv2024

High-Dimensional Bell States: A Paradigm Shift for Quantum Illumination

This paper solves the open problem of characterizing the performance of quantum illumination (QI) with discrete variable states. By devising a novel quantum measurement approach along with meticulous analysis, our investigation demonstrates that, in the limit as $M \rightarrow \infty$, the maximally entangled $M$ mode Bell state achieves optimal performance, matching the two-mode squeezed vacuum in a high-noise regime and exceeding it in low-noise. This result challenges the dominance of continuous variable states in photonic sensing applications and extends the novelty of QI to regimes where no quantum advantage was believed to exist. A closer analysis reveals that this advantage stems from retained entanglement in the transmitted Bell state, a paradigm-shifting discovery since interaction with the environment in optical systems is believed to break entanglement. The complete mathematical analysis of this work provides granular insights into the interaction between photonic systems and environmental noise, motivating further research into discrete variable quantum sensing.

quant-ph↗

arXiv2024

A novel multi-photon entangled state with enhanced resilience to path loss

In the realm of quantum information, entanglement stands as a cornerstone phenomenon. It underpins a vast array of quantum information processes, offering significant potential for advancements in quantum computing, communication, and sensing. This paper introduces a novel multi-photon entangled state, which generalizes the maximally entangled single-photon state and exhibits remarkable resilience to signal attenuation in photonic applications. We demonstrate the novelty of the proposed state through a simplified target detection model and illustrate its superior performance over traditional single-photon protocols, attributed to its higher entanglement level and enhanced noise suppression capabilities. Our findings suggest that the proposed multi-photon state holds significant promise for enhancing the efficiency and reliability of photonic applications subject to loss. This work lays the groundwork for future exploration into the practical applications of multi-photon entangled states in quantum technologies, potentially revolutionizing our approach to quantum sensing and beyond

quant-ph↗

arXiv2024

An Efficient Difference-of-Convex Solver for Privacy Funnel

We propose an efficient solver for the privacy funnel (PF) method, leveraging its difference-of-convex (DC) structure. The proposed DC separation results in a closed-form update equation, which allows straightforward application to both known and unknown distribution settings. For known distribution case, we prove the convergence (local stationary points) of the proposed non-greedy solver, and empirically show that it outperforms the state-of-the-art approaches in characterizing the privacy-utility trade-off. The insights of our DC approach apply to unknown distribution settings where labeled empirical samples are available instead. Leveraging the insights, our alternating minimization solver satisfies the fundamental Markov relation of PF in contrast to previous variational inference-based solvers. Empirically, we evaluate the proposed solver with MNIST and Fashion-MNIST datasets. Our results show that under a comparable reconstruction quality, an adversary suffers from higher prediction error from clustering our compressed codes than that with the compared methods. Most importantly, our solver is independent to private information in inference phase contrary to the baselines.

cs.LG↗

arXiv2024

Efficient Solvers for Wyner Common Information with Application to Multi-Modal Clustering

We propose two novel extensions of the Wyner common information optimization problem. Each relaxes one fundamental constraints in Wyner's formulation. The \textit{Variational Wyner Common Information} relaxes the matching constraint to the known distribution while imposing conditional independence to the feasible solution set. We derive a tight surrogate upper bound of the obtained unconstrained Lagrangian via the theory of variational inference, which can be minimized efficiently. Our solver caters to problems where conditional independence holds with significantly reduced computation complexity; On the other hand, the \textit{Bipartite Wyner Common Information} relaxes the conditional independence constraint whereas the matching condition is enforced on the feasible set. By leveraging the difference-of-convex structure of the formulated optimization problem, we show that our solver is resilient to conditional dependent sources. Both solvers are provably convergent (local stationary points), and empirically, they obtain more accurate solutions to Wyner's formulation with substantially less runtime. Moreover, them can be extended to unknown distribution settings by parameterizing the common randomness as a member of the exponential family of distributions. Our approaches apply to multi-modal clustering problems, where multiple modalities of observations come from the same cluster. Empirically, our solvers outperform the state-of-the-art multi-modal clustering algorithms with significantly improved performance.

cs.IT↗

arXiv2023

The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting

Wireless fingerprinting refers to a device identification method leveraging hardware imperfections and wireless channel variations as signatures. Beyond physical layer characteristics, recent studies demonstrated that user behaviors could be identified through network traffic, e.g., packet length, without decryption of the payload. Inspired by these results, we propose a multi-layer fingerprinting framework that jointly considers the multi-layer signatures for improved identification performance. In contrast to previous works, by leveraging the recent multi-view machine learning paradigm, i.e., data with multiple forms, our method can cluster the device information shared among the multi-layer features without supervision. Our information-theoretic approach can be extended to supervised and semi-supervised settings with straightforward derivations. In solving the formulated problem, we obtain a tight surrogate bound using variational inference for efficient optimization. In extracting the shared device information, we develop an algorithm based on the Wyner common information method, enjoying reduced computation complexity as compared to existing approaches. The algorithm can be applied to data distributions belonging to the exponential family class. Empirically, we evaluate the algorithm in a synthetic dataset with real-world video traffic and simulated physical layer characteristics. Our empirical results show that the proposed method outperforms the state-of-the-art baselines in both supervised and unsupervised settings.

cs.IT↗

arXiv2023

Efficient Alternating Minimization Solvers for Wyner Multi-View Unsupervised Learning

In this work, we adopt Wyner common information framework for unsupervised multi-view representation learning. Within this framework, we propose two novel formulations that enable the development of computational efficient solvers based on the alternating minimization principle. The first formulation, referred to as the {\em variational form}, enjoys a linearly growing complexity with the number of views and is based on a variational-inference tight surrogate bound coupled with a Lagrangian optimization objective function. The second formulation, i.e., the {\em representational form}, is shown to include known results as special cases. Here, we develop a tailored version from the alternating direction method of multipliers (ADMM) algorithm for solving the resulting non-convex optimization problem. In the two cases, the convergence of the proposed solvers is established in certain relevant regimes. Furthermore, our empirical results demonstrate the effectiveness of the proposed methods as compared with the state-of-the-art solvers. In a nutshell, the proposed solvers offer computational efficiency, theoretical convergence guarantees (local minima), scalable complexity with the number of views, and exceptional accuracy as compared with the state-of-the-art techniques. Our focus here is devoted to the discrete case and our results for continuous distributions are reported elsewhere.

cs.IT↗

arXiv2022

A Linearly Convergent Douglas-Rachford Splitting Solver for Markovian Information-Theoretic Optimization Problems

In this work, we propose solving the Information bottleneck (IB) and Privacy Funnel (PF) problems with Douglas-Rachford Splitting methods (DRS). We study a general Markovian information-theoretic Lagrangian that includes IB and PF into a unified framework. We prove the linear convergence of the proposed solvers using the Kurdyka-Łojasiewicz inequality. Moreover, our analysis is beyond IB and PF and applies to any convex-weakly convex pair objectives. Based on the results, we develop two types of linearly convergent IB solvers, with one improves the performance of convergence over existing solvers while the other can be independent to the relevance-compression trade-off. Moreover, our results apply to PF, yielding a new class of linearly convergent PF solvers. Empirically, the proposed IB solvers IB obtain solutions that are comparable to the Blahut-Arimoto-based benchmark and is convergent for a wider range of the penalty coefficient than existing solvers. For PF, our non-greedy solvers can characterize the privacy-utility trade-off better than the clustering-based greedy solvers.

cs.IT↗

arXiv2022

On the Multi-View Information Bottleneck Representation

In this work, we generalize the information bottleneck (IB) approach to the multi-view learning context. The exponentially growing complexity of the optimal representation motivates the development of two novel formulations with more favorable performance-complexity tradeoffs. The first approach is based on forming a stochastic consensus and is suited for scenarios with significant {\em representation overlap} between the different views. The second method, relying on incremental updates, is tailored for the other extreme scenario with minimal representation overlap. In both cases, we extend our earlier work on the alternating directional methods of multiplier (ADMM) solver and establish its convergence and scalability. Empirically, we find that the proposed methods outperform state-of-the-art approaches in multi-view classification problems under a broad range of modelling parameters.

cs.IT↗

arXiv2020

Towards Jointly Optimal Placement and Delivery: To Code or Not to Code in Wireless Caching Networks

Coded caching techniques have received significant attention lately due to their provable gains in reducing the cost of data delivery in wireless networks. These gains, however, have only been demonstrated under the assumption of a free placement phase. This unrealistic assumption poses a significant limitation, especially in cases where aggressive placement strategies can lead to a significant transmission cost that may even be higher than the corresponding cost of the delivery phase. In this paper, we relax this assumption and propose a general caching framework that captures the transmission cost of the two phases, and hence, results in minimizing the overall rate of the caching network. We model the dynamic nature of the network through a cost structure that allows for varying the network architecture and cost per transmission, across the placement and delivery phases. We start with the scenario where the individual users have no limit on the available caching memory and characterize the jointly optimal solution as a function of the different parameters in our cost structure. Then, we characterize the effect of memory constraints on the optimal solution in certain special cases. Interestingly, our results identify regions where the uncoded caching scheme outperforms its coded counterpart. Further, coded caching is shown to offer performance gains only when the network architecture during the placement phase is different from that during the delivery phase.

cs.IT↗

arXiv2019

Optimal Wireless Caching with Placement Cost

Coded caching has been shown to result in significant throughput gains, but its gains were proved only by assuming a placement phase with no transmission cost. A free placement phase is, however, an unrealistic assumption that could stand as an obstacle towards delivering the promise of coded caching. In [1], we relaxed this assumption by introducing a general caching framework that captures transmission costs for both delivery and placement phases under general assumptions on varying network architectures, memory constraints, and traffic patterns. Here, we leverage this general framework and focus on analyzing the effect of the placement communication cost on the overall throughput and the structure of the optimal caching scheme, under the assumptions of the worst case traffic pattern and unlimited memory at the end users. Interestingly, we find relevant network configurations where uncoded caching is the optimal solution.

cs.IT↗

arXiv2019

On the Performance of Mobility-Aware D2D Caching Networks

The increase in demand for spectrum-based services forms a bottleneck in wireless networks. Device-to-Device (D2D) caching networks tackle this problem by exploiting user's behavior predictability and the possibility of sharing data between them to alleviate the network congestion. However, capturing mobility statistics allows Service Providers (SPs) to enhance their caching strategies. In this work, we introduce a mobility-aware D2D caching network where SP harnesses user demand and mobility statistics to minimize the incurred service cost through an optimal caching policy. We investigate two caching schemes: centralized and decentralized caching schemes. In the centralized caching scheme, SP makes the caching decision towards its cost minimization to increase its profit. However, the complexity of optimal caching policy grows exponentially with the number of users. Therefore, we discuss a greedy caching algorithm which has a polynomial order complexity. We also use this greedy algorithm to establish upper and lower bounds on the proactive service gain achieved by the optimal caching policy. In the decentralized caching scheme, users take over and make their caching decisions, in a distributed fashion affected by the SP pricing policy, towards their payment minimization. We formulated the tension between the SP and users as a Stackelberg game. The Best response analysis was used to identify a sub-game perfect Nash equilibrium (SPNE) between users. The optimal solution of proposed model was found to depend on the SP reward preference, which affects the assigned memory in users' devices. We found some regimes for the reward value where the SPNE was non-unique. A fair allocation caching policy was adopted to choose one of these SPNEs. To understand the impact of user behavior, we investigated some special cases to explore how user's mobility statistics affect their caching decision.

cs.NI↗

arXiv2018

A Blockchain Example for Cooperative Interference Management

We present an example where a distributed coordinated protocol supported by a blockchain-enabled monetary mechanism leads to achieving optimal information theoretic degrees of freedom gains. The considered setting is that of a linear interference network, where cooperative transmission is allowed, but at no cost in terms of the overall backhaul load. In other words, the average number of messages assigned to a transmitter is one. We show that a simple monetary mechanism that consists only of one coin type can enable the achievability of the optimal centralized solution. The proposed greedy distributed algorithm relies on incentivizing the users to share their resources in one channel use, in return of credit they receive for maximizing their rate gains in future channel uses. This example is the first in its class and it opens the door for constructing a unified framework for blockchain-enabled monetary mechanisms for optimal interference management and spectrum sharing.

cs.IT↗

arXiv2017

On The Compound MIMO Wiretap Channel with Mean Feedback

Compound MIMO wiretap channel with double sided uncertainty is considered under channel mean information model. In mean information model, channel variations are centered around its mean value which is fed back to the transmitter. We show that the worst case main channel is anti-parallel to the channel mean information resulting in an overall unit rank channel. Further, the worst eavesdropper channel is shown to be isotropic around its mean information. Accordingly, we provide the capacity achieving beamforming direction. We show that the saddle point property holds under mean information model, and thus, compound secrecy capacity equals to the worst case capacity over the class of uncertainty. Moreover, capacity achieving beamforming direction is found to require matrix inversion, thus, we derive the null steering (NS) beamforming as an alternative suboptimal solution that does not require matrix inversion. NS beamformer is in the direction orthogonal to the eavesdropper mean channel that maintains the maximum possible gain in mean main channel direction. Extensive computer simulation reveals that NS performs very close to the optimal solution. It also verifies that, NS beamforming outperforms both maximum ratio transmission (MRT) and zero forcing (ZF) beamforming approaches over the entire SNR range. Finally, An equivalence relation with MIMO wiretap channel in Rician fading environment is established.

cs.CR↗

arXiv2016

On The Security of AoA Estimation

Angle of Arrival (AoA) estimation has found its way to a wide range of applications. Much attention have been paid to study different techniques for AoA estimation and its applications for jamming suppression, however, security vulnerability issues of AoA estimation itself under hostile activity have not been paid the same attention. In this paper, the problem of AoA estimation in Rician flat fading channel under jamming condition is investigated. We consider the scenario in which a receiver with multiple antenna is trying to estimate the AoA of the specular line of sight (LOS) component of signal received from a given single antenna transmitter using a predefined training sequence. A jammer equipped with multiple antennas is trying to interrupt the AoA estimation phase by sending an arbitrary signal. We derive the optimal jammer and receiver strategies in various scenarios based on the knowledge of the opponent strategies and the available information about the communication channel. In all scenarios, we derive the optimal jammer signal design as well as its optimal power allocation policy. The results show the optimality of the training based Maximum Likelihood (ML) AoA estimator in case of randomly generated jamming signal. We also show that, the optimal jammer strategy is to emit a signal identical to the predefined training sequence turning the estimation process into a highest power competition scenario in which the detected AoA is the one for the transmitting entity of higher power. The obtained results are supported by the provided computer simulation.

cs.IT↗

arXiv2016

The Impact of Stealthy Attacks on Smart Grid Performance: Tradeoffs and Implications

The smart grid is envisioned to significantly enhance the efficiency of energy consumption, by utilizing two-way communication channels between consumers and operators. For example, operators can opportunistically leverage the delay tolerance of energy demands in order to balance the energy load over time, and hence, reduce the total operational cost. This opportunity, however, comes with security threats, as the grid becomes more vulnerable to cyber-attacks. In this paper, we study the impact of such malicious cyber-attacks on the energy efficiency of the grid in a simplified setup. More precisely, we consider a simple model where the energy demands of the smart grid consumers are intercepted and altered by an active attacker before they arrive at the operator, who is equipped with limited intrusion detection capabilities. We formulate the resulting optimization problems faced by the operator and the attacker and propose several scheduling and attack strategies for both parties. Interestingly, our results show that, as opposed to facilitating cost reduction in the smart grid, increasing the delay tolerance of the energy demands potentially allows the attacker to force increased costs on the system. This highlights the need for carefully constructed and robust intrusion detection mechanisms at the operator.

math.OC↗

arXiv2014

Proactive Data Download and User Demand Shaping for Data Networks

In this work, we propose and study optimal proactive resource allocation and demand shaping for data networks. Motivated by the recent findings on the predictability of human behavior patterns in data networks, and the emergence of highly capable handheld devices, our design aims to smooth out the network traffic over time and minimize the data delivery costs. Our framework utilizes proactive data services as well as smart content recommendation schemes for shaping the demand. Proactive data services take place during the off-peak hours based on a statistical prediction of a demand profile for each user, whereas smart content recommendation assigns modified valuations to data items so as to render the users' demand less uncertain. Hence, our recommendation scheme aims to boost the performance of proactive services within the allowed flexibility of user requirements. We conduct theoretical performance analysis that quantifies the leveraged cost reduction through the proposed framework. We show that the cost reduction scales at the same rate as the cost function scales with the number of users. Further, we prove that \emph{demand shaping} through smart recommendation strictly reduces the incurred cost even below that of proactive downloads without recommendation.

cs.IT↗