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Ravi Tandon

Ravi Tandon appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Information Theorymath.ITMachine LearningCryptography and SecurityDistributed, Parallel, and Cluster ComputingNetworking and Internet ArchitectureComputer VisionDatabasesInformation RetrievalArtificial Intelligenceeess.SPMultiagent Systemsphysics.soc-ph

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Published work

40 published item(s)

preprint2026arXiv

Information Theoretic Optimal Surveillance for Epidemic Prevalence in Networks

Estimating the true prevalence of an epidemic outbreak is a key public health problem. This is challenging because surveillance is usually resource intensive and biased. In the network setting, prior work on cost sensitive disease surveillance has focused on choosing a subset of individuals (or nodes) to minimize objectives such as probability of outbreak detection. Such methods do not give insights into the outbreak size distribution which, despite being complex and multi-modal, is very useful in public health planning. We introduce TESTPREV, a problem of choosing a subset of nodes which maximizes the mutual information with disease prevalence, which directly provides information about the outbreak size distribution. We show that, under the independent cascade (IC) model, solutions computed by all prior disease surveillance approaches are highly sub-optimal for TESTPREV in general. We also show that TESTPREV is hard to even approximate. While this mutual information objective is computationally challenging for general networks, we show that it can be computed efficiently for various network classes. We present a greedy strategy, called GREEDYMI, that uses estimates of mutual information from cascade simulations and thus can be applied on any network and disease model. We find that GREEDYMI does better than natural baselines in terms of maximizing the mutual information as well as reducing the expected variance in outbreak size, under the IC model.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Interpretable Probability Estimation with LLMs via Shapley Reconstruction

Large Language Models (LLMs) demonstrate potential to estimate the probability of uncertain events, by leveraging their extensive knowledge and reasoning capabilities. This ability can be applied to support intelligent decision-making across diverse fields, such as financial forecasting and preventive healthcare. However, directly prompting LLMs for probability estimation faces significant challenges: their outputs are often noisy, and the underlying predicting process is opaque. In this paper, we propose PRISM: Probability Reconstruction via Shapley Measures, a framework that brings transparency and precision to LLM-based probability estimation. PRISM decomposes an LLM's prediction by quantifying the marginal contribution of each input factor using Shapley values. These factor-level contributions are then aggregated to reconstruct a calibrated final estimate. In our experiments, we demonstrate PRISM improves predictive accuracy over direct prompting and other baselines, across multiple domains including finance, healthcare, and agriculture. Beyond performance, PRISM provides a transparent prediction pipeline: our case studies visualize how individual factors shape the final estimate, helping build trust in LLM-based decision support systems.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Semantic Smoothing via Novel View Synthesis for Robust SAR Image Classification

Deep neural networks are vulnerable to adversarial perturbations, limiting deployment in safety-critical applications such as synthetic aperture radar (SAR) automatic target recognition (ATR). Randomized smoothing improves robustness by averaging predictions over noisy inputs, but isotropic noise often fails to preserve the semantic structure of SAR imagery. We propose semantic smoothing, a defense that replaces noised-based perturbations with structured randomized transformations generated by a novel view synthesis model. For SAR, we condition on acquisition geometry to synthesize multiple plausible radar views. Predictions across generated randomized views are aggregated to form a robust classifier. Experiments show that semantic smoothing improves robustness against standard attacks, such as FGSM and PGD, and SAR-specific attacks, such as OTSA and SMGAA, while also increasing clean classification accuracy. These results demonstrate that randomized smoothing via semantically preserving geometric transformations is a promising alternative to isotropic noise for adversarial defense in structured sensing domains.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Unsupervised Change Detection using DRE-CUSUM

This paper presents DRE-CUSUM, an unsupervised density-ratio estimation (DRE) based approach to determine statistical changes in time-series data when no knowledge of the pre-and post-change distributions are available. The core idea behind the proposed approach is to split the time-series at an arbitrary point and estimate the ratio of densities of distribution (using a parametric model such as a neural network) before and after the split point. The DRE-CUSUM change detection statistic is then derived from the cumulative sum (CUSUM) of the logarithm of the estimated density ratio. We present a theoretical justification as well as accuracy guarantees which show that the proposed statistic can reliably detect statistical changes, irrespective of the split point. While there have been prior works on using density ratio based methods for change detection, to the best of our knowledge, this is the first unsupervised change detection approach with a theoretical justification and accuracy guarantees. The simplicity of the proposed framework makes it readily applicable in various practical settings (including high-dimensional time-series data); we also discuss generalizations for online change detection. We experimentally show the superiority of DRE-CUSUM using both synthetic and real-world datasets over existing state-of-the-art unsupervised algorithms (such as Bayesian online change detection, its variants as well as several other heuristic methods).

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation

In this paper, we study the problem of federated learning over a wireless channel with user sampling, modeled by a Gaussian multiple access channel, subject to central and local differential privacy (DP/LDP) constraints. It has been shown that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong DP guarantees for the users. Specifically, the central DP privacy leakage has been shown to scale as $\mathcal{O}(1/K^{1/2})$, where $K$ is the number of users. It has also been shown that user sampling coupled with orthogonal transmission can enhance the central DP privacy leakage with the same scaling behavior. In this work, we show that, by join incorporating both wireless aggregation and user sampling, one can obtain even stronger privacy guarantees. We propose a private wireless gradient aggregation scheme, which relies on independently randomized participation decisions by each user. The central DP leakage of our proposed scheme scales as $\mathcal{O}(1/K^{3/4})$. In addition, we show that LDP is also boosted by user sampling. We also present analysis for the convergence rate of the proposed scheme and study the tradeoffs between wireless resources, convergence, and privacy theoretically and empirically for two scenarios when the number of sampled participants are $(a)$ known, or $(b)$ unknown at the parameter server.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Adversarial Filters for Secure Modulation Classification

Modulation Classification (MC) refers to the problem of classifying the modulation class of a wireless signal. In the wireless communications pipeline, MC is the first operation performed on the received signal and is critical for reliable decoding. This paper considers the problem of secure modulation classification, where a transmitter (Alice) wants to maximize MC accuracy at a legitimate receiver (Bob) while minimizing MC accuracy at an eavesdropper (Eve). The contribution of this work is to design novel adversarial learning techniques for secure MC. In particular, we present adversarial filtering based algorithms for secure MC, in which Alice uses a carefully designed adversarial filter to mask the transmitted signal, that can maximize MC accuracy at Bob while minimizing MC accuracy at Eve. We present two filtering based algorithms, namely gradient ascent filter (GAF), and a fast gradient filter method (FGFM), with varying levels of complexity. Our proposed adversarial filtering based approaches significantly outperform additive adversarial perturbations (used in the traditional ML community and other prior works on secure MC) and also have several other desirable properties. In particular, GAF and FGFM algorithms are a) computational efficient (allow fast decoding at Bob), b) power-efficient (do not require excessive transmit power at Alice); and c) SNR efficient (i.e., perform well even at low SNR values at Bob).

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Asymmetric Leaky Private Information Retrieval

Information-theoretic formulations of the private information retrieval (PIR) problem have been investigated under a variety of scenarios. Symmetric private information retrieval (SPIR) is a variant where a user is able to privately retrieve one out of $K$ messages from $N$ non-colluding replicated databases without learning anything about the remaining $K-1$ messages. However, the goal of perfect privacy can be too taxing for certain applications. In this paper, we investigate if the information-theoretic capacity of SPIR (equivalently, the inverse of the minimum download cost) can be increased by relaxing both user and DB privacy definitions. Such relaxation is relevant in applications where privacy can be traded for communication efficiency. We introduce and investigate the Asymmetric Leaky PIR (AL-PIR) model with different privacy leakage budgets in each direction. For user privacy leakage, we bound the probability ratios between all possible realizations of DB queries by a function of a non-negative constant $ε$. For DB privacy, we bound the mutual information between the undesired messages, the queries, and the answers, by a function of a non-negative constant $δ$. We propose a general AL-PIR scheme that achieves an upper bound on the optimal download cost for arbitrary $ε$ and $δ$. We show that the optimal download cost of AL-PIR is upper-bounded as $D^{*}(ε,δ)\leq 1+\frac{1}{N-1}-\frac{δe^ε}{N^{K-1}-1}$. Second, we obtain an information-theoretic lower bound on the download cost as $D^{*}(ε,δ)\geq 1+\frac{1}{Ne^ε-1}-\fracδ{(Ne^ε)^{K-1}-1}$. The gap analysis between the two bounds shows that our AL-PIR scheme is optimal when $ε=0$, i.e., under perfect user privacy and it is optimal within a maximum multiplicative gap of $\frac{N-e^{-ε}}{N-1}$ for any $(ε,δ)$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Communication Efficient Federated Learning over Multiple Access Channels

In this work, we study the problem of federated learning (FL), where distributed users aim to jointly train a machine learning model with the help of a parameter server (PS). In each iteration of FL, users compute local gradients, followed by transmission of the quantized gradients for subsequent aggregation and model updates at PS. One of the challenges of FL is that of communication overhead due to FL's iterative nature and large model sizes. One recent direction to alleviate communication bottleneck in FL is to let users communicate simultaneously over a multiple access channel (MAC), possibly making better use of the communication resources. In this paper, we consider the problem of FL learning over a MAC. In particular, we focus on the design of digital gradient transmission schemes over a MAC, where gradients at each user are first quantized, and then transmitted over a MAC to be decoded individually at the PS. When designing digital FL schemes over MACs, there are new opportunities to assign different amount of resources (such as rate or bandwidth) to different users based on a) the informativeness of the gradients at each user, and b) the underlying channel conditions. We propose a stochastic gradient quantization scheme, where the quantization parameters are optimized based on the capacity region of the MAC. We show that such channel aware quantization for FL outperforms uniform quantization, particularly when users experience different channel conditions, and when have gradients with varying levels of informativeness.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Latent-variable Private Information Retrieval

In many applications, content accessed by users (movies, videos, news articles, etc.) can leak sensitive latent attributes, such as religious and political views, sexual orientation, ethnicity, gender, and others. To prevent such information leakage, the goal of classical PIR is to hide the identity of the content/message being accessed, which subsequently also hides the latent attributes. This solution, while private, can be too costly, particularly, when perfect (information-theoretic) privacy constraints are imposed. For instance, for a single database holding $K$ messages, privately retrieving one message is possible if and only if the user downloads the entire database of $K$ messages. Retrieving content privately, however, may not be necessary to perfectly hide the latent attributes. Motivated by the above, we formulate and study the problem of latent-variable private information retrieval (LV-PIR), which aims at allowing the user efficiently retrieve one out of $K$ messages (indexed by $θ$) without revealing any information about the latent variable (modeled by $S$). We focus on the practically relevant setting of a single database and show that one can significantly reduce the download cost of LV-PIR (compared to the classical PIR) based on the correlation between $θ$ and $S$. We present a general scheme for LV-PIR as a function of the statistical relationship between $θ$ and $S$, and also provide new results on the capacity/download cost of LV-PIR. Several open problems and new directions are also discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Wireless Federated Learning with Local Differential Privacy

In this paper, we study the problem of federated learning (FL) over a wireless channel, modeled by a Gaussian multiple access channel (MAC), subject to local differential privacy (LDP) constraints. We show that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong LDP guarantees for the users. We propose a private wireless gradient aggregation scheme, which shows that when aggregating gradients from $K$ users, the privacy leakage per user scales as $\mathcal{O}\big(\frac{1}{\sqrt{K}} \big)$ compared to orthogonal transmission in which the privacy leakage scales as a constant. We also present analysis for the convergence rate of the proposed private FL aggregation algorithm and study the tradeoffs between wireless resources, convergence, and privacy.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Secure Retrospective Interference Alignment

In this paper, the $K$-user interference channel with secrecy constraints is considered with delayed channel state information at transmitters (CSIT). We propose a novel secure retrospective interference alignment scheme in which the transmitters carefully mix information symbols with artificial noises to ensure confidentiality. Achieving positive secure degrees of freedom (SDoF) is challenging due to the delayed nature of CSIT, and the distributed nature of the transmitters. Our scheme works over two phases: phase one in which each transmitter sends information symbols mixed with artificial noises, and repeats such transmission over multiple rounds. In the next phase, each transmitter uses delayed CSIT of the previous phase and sends a function of the net interference and artificial noises (generated in previous phase), which is simultaneously useful for all receivers. These phases are designed to ensure the decodability of the desired messages while satisfying the secrecy constraints. We present our achievable scheme for three models, namely: 1) $K$-user interference channel with confidential messages (IC-CM), and we show that $\frac{1}{2} (\sqrt{K} -6) $ SDoF is achievable, 2) $K$-user interference channel with an external eavesdropper (IC-EE), and 3) $K$-user IC with confidential messages and an external eavesdropper (IC-CM-EE). We show that for the $K$-user IC-EE, $\frac{1}{2} (\sqrt{K} -3) $ SDoF is achievable, and for the $K$-user IC-CM-EE, $\frac{1}{2} (\sqrt{K} -6) $ is achievable. To the best of our knowledge, this is the first result on the $K$-user interference channel with secrecy constrained models and delayed CSIT that achieves a SDoF which scales with $K$, the number of users.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Flow of Information in Feed-Forward Deep Neural Networks

Feed-forward deep neural networks have been used extensively in various machine learning applications. Developing a precise understanding of the underling behavior of neural networks is crucial for their efficient deployment. In this paper, we use an information theoretic approach to study the flow of information in a neural network and to determine how entropy of information changes between consecutive layers. Moreover, using the Information Bottleneck principle, we develop a constrained optimization problem that can be used in the training process of a deep neural network. Furthermore, we determine a lower bound for the level of data representation that can be achieved in a deep neural network with an acceptable level of distortion.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Hierarchical Quickest Change Detection via Surrogates

Change detection (CD) in time series data is a critical problem as it reveal changes in the underlying generative processes driving the time series. Despite having received significant attention, one important unexplored aspect is how to efficiently utilize additional correlated information to improve the detection and the understanding of changepoints. We propose hierarchical quickest change detection (HQCD), a framework that formalizes the process of incorporating additional correlated sources for early changepoint detection. The core ideas behind HQCD are rooted in the theory of quickest detection and HQCD can be regarded as its novel generalization to a hierarchical setting. The sources are classified into targets and surrogates, and HQCD leverages this structure to systematically assimilate observed data to update changepoint statistics across layers. The decision on actual changepoints are provided by minimizing the delay while still maintaining reliability bounds. In addition, HQCD also uncovers interesting relations between changes at targets from changes across surrogates. We validate HQCD for reliability and performance against several state-of-the-art methods for both synthetic dataset (known changepoints) and several real-life examples (unknown changepoints). Our experiments indicate that we gain significant robustness without loss of detection delay through HQCD. Our real-life experiments also showcase the usefulness of the hierarchical setting by connecting the surrogate sources (such as Twitter chatter) to target sources (such as Employment related protests that ultimately lead to major uprisings).

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Improved Approximation of Storage-Rate Tradeoff for Caching with Multiple Demands

Caching at the network edge has emerged as a viable solution for alleviating the severe capacity crunch in modern content centric wireless networks by leveraging network load-balancing in the form of localized content storage and delivery. In this work, we consider a cache-aided network where the cache storage phase is assisted by a central server and users can demand multiple files at each transmission interval. To service these demands, we consider two delivery models - $(1)$ centralized content delivery where user demands at each transmission interval are serviced by the central server via multicast transmissions; and $(2)$ device-to-device (D2D) assisted distributed delivery where users multicast to each other in order to service file demands. For such cache-aided networks, we present new results on the fundamental cache storage vs. transmission rate tradeoff. Specifically, we develop a new technique for characterizing information theoretic lower bounds on the storage-rate tradeoff and show that the new lower bounds are strictly tighter than cut-set bounds from literature. Furthermore, using the new lower bounds, we establish the optimal storage-rate tradeoff to within a constant multiplicative gap. We show that, for multiple demands per user, achievable schemes based on repetition of schemes for single demands are order-optimal under both delivery models.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Information Theoretic Limits of Data Shuffling for Distributed Learning

Data shuffling is one of the fundamental building blocks for distributed learning algorithms, that increases the statistical gain for each step of the learning process. In each iteration, different shuffled data points are assigned by a central node to a distributed set of workers to perform local computations, which leads to communication bottlenecks. The focus of this paper is on formalizing and understanding the fundamental information-theoretic trade-off between storage (per worker) and the worst-case communication overhead for the data shuffling problem. We completely characterize the information theoretic trade-off for $K=2$, and $K=3$ workers, for any value of storage capacity, and show that increasing the storage across workers can reduce the communication overhead by leveraging coding. We propose a novel and systematic data delivery and storage update strategy for each data shuffle iteration, which preserves the structural properties of the storage across the workers, and aids in minimizing the communication overhead in subsequent data shuffling iterations.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

On the Worst-case Communication Overhead for Distributed Data Shuffling

Distributed learning platforms for processing large scale data-sets are becoming increasingly prevalent. In typical distributed implementations, a centralized master node breaks the data-set into smaller batches for parallel processing across distributed workers to achieve speed-up and efficiency. Several computational tasks are of sequential nature, and involve multiple passes over the data. At each iteration over the data, it is common practice to randomly re-shuffle the data at the master node, assigning different batches for each worker to process. This random re-shuffling operation comes at the cost of extra communication overhead, since at each shuffle, new data points need to be delivered to the distributed workers. In this paper, we focus on characterizing the information theoretically optimal communication overhead for the distributed data shuffling problem. We propose a novel coded data delivery scheme for the case of no excess storage, where every worker can only store the assigned data batches under processing. Our scheme exploits a new type of coding opportunity and is applicable to any arbitrary shuffle, and for any number of workers. We also present an information theoretic lower bound on the minimum communication overhead for data shuffling, and show that the proposed scheme matches this lower bound for the worst-case communication overhead.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Cache Aided Wireless Networks: Tradeoffs between Storage and Latency

We investigate the fundamental information theoretic limits of cache-aided wireless networks, in which edge nodes (or transmitters) are endowed with caches that can store popular content, such as multimedia files. This architecture aims to localize popular multimedia content by proactively pushing it closer to the edge of the wireless network, thereby alleviating backhaul load. An information theoretic model of such networks is presented, that includes the introduction of a new metric, namely normalized delivery time (NDT), which captures the worst case time to deliver any requested content to the users. We present new results on the trade-off between latency, measured via the NDT, and the cache storage capacity of the edge nodes. In particular, a novel information theoretic lower bound on NDT is presented for cache aided networks. The optimality of this bound is shown for several system parameters.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

MISO Broadcast Channel with Hybrid CSIT: Beyond Two Users

We study the impact of heterogeneity of channel-state-information available at the transmitters (CSIT) on the capacity of broadcast channels with a multiple-antenna transmitter and $k$ single-antenna receivers (MISO BC). In particular, we consider the $k$-user MISO BC, where the CSIT with respect to each receiver can be either instantaneous/perfect, delayed, or not available; and we study the impact of this heterogeneity of CSIT on the degrees-of-freedom (DoF) of such network. We first focus on the $3$-user MISO BC; and we completely characterize the DoF region for all possible heterogeneous CSIT configurations, assuming linear encoding strategies at the transmitters. The result shows that the state-of-the-art achievable schemes in the literature are indeed sum-DoF optimal, when restricted to linear encoding schemes. To prove the result, we develop a novel bound, called Interference Decomposition Bound, which provides a lower bound on the interference dimension at a receiver which supplies delayed CSIT based on the average dimension of constituents of that interference, thereby decomposing the interference into its individual components. Furthermore, we extend our outer bound on the DoF region to the general $k$-user MISO BC, and demonstrate that it leads to an approximate characterization of linear sum-DoF to within an additive gap of $0.5$ for a broad range of CSIT configurations. Moreover, for the special case where only one receiver supplies delayed CSIT, we completely characterize the linear sum-DoF.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

On the Latency and Energy Efficiency of Erasure-Coded Cloud Storage Systems

The increase in data storage and power consumption at data-centers has made it imperative to design energy efficient Distributed Storage Systems (DSS). The energy efficiency of DSS is strongly influenced not only by the volume of data, frequency of data access and redundancy in data storage, but also by the heterogeneity exhibited by the DSS in these dimensions. To this end, we propose and analyze the energy efficiency of a heterogeneous distributed storage system in which $n$ storage servers (disks) store the data of $R$ distinct classes. Data of class $i$ is encoded using a $(n,k_{i})$ erasure code and the (random) data retrieval requests can also vary across classes. We show that the energy efficiency of such systems is closely related to the average latency and hence motivates us to study the energy efficiency via the lens of average latency. Through this connection, we show that erasure coding serves the dual purpose of reducing latency and increasing energy efficiency. We present a queuing theoretic analysis of the proposed model and establish upper and lower bounds on the average latency for each data class under various scheduling policies. Through extensive simulations, we present qualitative insights which reveal the impact of coding rate, number of servers, service distribution and number of redundant requests on the average latency and energy efficiency of the DSS.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Perfect Output Feedback in the Two-User Decentralized Interference Channel

In this paper, the $η$-Nash equilibrium ($η$-NE) region of the two-user Gaussian interference channel (IC) with perfect output feedback is approximated to within $1$ bit/s/Hz and $η$ arbitrarily close to $1$ bit/s/Hz. The relevance of the $η$-NE region is that it provides the set of rate-pairs that are achievable and stable in the IC when both transmitter-receiver pairs autonomously tune their own transmit-receive configurations seeking an $η$-optimal individual transmission rate. Therefore, any rate tuple outside the $η$-NE region is not stable as there always exists one link able to increase by at least $η$ bits/s/Hz its own transmission rate by updating its own transmit-receive configuration. The main insights that arise from this work are: $(i)$ The $η$-NE region achieved with feedback is larger than or equal to the $η$-NE region without feedback. More importantly, for each rate pair achievable at an $η$-NE without feedback, there exists at least one rate pair achievable at an $η$-NE with feedback that is weakly Pareto superior. $(ii)$ There always exists an $η$-NE transmit-receive configuration that achieves a rate pair that is at most $1$ bit/s/Hz per user away from the outer bound of the capacity region.

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preprint2015arXiv

Secure Degrees of Freedom Region of the Two-User MISO Broadcast Channel with Alternating CSIT

The two user multiple-input single-output (MISO) broadcast channel with confidential messages (BCCM) is studied in which the nature of channel state information at the transmitter (CSIT) from each user can be of the form $I_{i}$, $i=1,2$ where $I_{1}, I_{2}\in \{\mathsf{P}, \mathsf{D}, \mathsf{N}\}$, and the forms $\mathsf{P}$, $\mathsf{D}$ and $\mathsf{N}$ correspond to perfect and instantaneous, completely delayed, and no CSIT, respectively. Thus, the overall CSIT can alternate between $9$ possible states corresponding to all possible values of $I_{1}I_{2}$, with each state occurring for $λ_{I_{1}I_{2}}$ fraction of the total duration. The main contribution of this paper is to establish the secure degrees of freedom (s.d.o.f.) region of the MISO BCCM with alternating CSIT with the symmetry assumption, where $λ_{I_{1} I_{2}}=λ_{I_{2}I_{1}}$. The main technical contributions include developing a) novel achievable schemes for MISO BCCM with alternating CSIT with security constraints which also highlight the synergistic benefits of inter-state coding for secrecy, b) new converse proofs via local statistical equivalence and channel enhancement; and c) showing the interplay between various aspects of channel knowledge and their impact on s.d.o.f.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Distributed Space-Time Interference Alignment with Moderately-Delayed CSIT

This paper proposes an interference alignment method with distributed and delayed channel state information at the transmitter (CSIT) for a class of interference networks. The core idea of the proposed method is to align interference signals over time at the unintended receivers in a distributed manner. With the proposed method, achievable trade-offs between the sum of degrees of freedom (sum-DoF) and feedback delay of CSI are characterized in both the X-channel and three-user interference channel to reveal the impact on how the CSI feedback delay affects the sum-DoF of the interference networks. A major implication of derived results is that distributed and moderately- delayed CSIT is useful to strictly improve the sum-DoF over the case of no CSI at the transmitter in a certain class of interference networks. For a class of X-channels, the results show how to optimally use distributed and moderately-delayed CSIT to yield the same sum-DoF as instantaneous and global CSIT. Further, leveraging the proposed transmission method and the known outer bound results, the sum-capacity of the two-user X-channel with a particular set of channel coefficients is characterized within a constant number of bits.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Fundamental Limits of Caching with Secure Delivery

Caching is emerging as a vital tool for alleviating the severe capacity crunch in modern content-centric wireless networks. The main idea behind caching is to store parts of popular content in end-users' memory and leverage the locally stored content to reduce peak data rates. By jointly designing content placement and delivery mechanisms, recent works have shown order-wise reduction in transmission rates in contrast to traditional methods. In this work, we consider the secure caching problem with the additional goal of minimizing information leakage to an external wiretapper. The fundamental cache memory vs. transmission rate trade-off for the secure caching problem is characterized. Rather surprisingly, these results show that security can be introduced at a negligible cost, particularly for large number of files and users. It is also shown that the rate achieved by the proposed caching scheme with secure delivery is within a constant multiplicative factor from the information-theoretic optimal rate for almost all parameter values of practical interest.

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preprint2014arXiv

On the Degrees-of-freedom of the 3-user MISO Broadcast Channel with Hybrid CSIT

The 3-user multiple-input single-output (MISO) broadcast channel (BC) with hybrid channel state information at the transmitter (CSIT) is considered. In this framework, there is perfect and instantaneous CSIT from a subset of users and delayed CSIT from the remaining users. We present new results on the degrees of freedom (DoF) of the 3-user MISO BC with hybrid CSIT. In particular, for the case of 2 transmit antennas, we show that with perfect CSIT from one user and delayed CSIT from the remaining two users, the optimal DoF is 5/3. For the case of 3 transmit antennas and the same hybrid CSIT setting, it is shown that a higher DoF of 9/5 is achievable and this result improves upon the best known bound. Furthermore, with 3 transmit antennas, and the hybrid CSIT setting in which there is perfect CSIT from two users and delayed CSIT from the third one, a novel scheme is presented which achieves 9/4 DoF. Our results also reveal new insights on how to utilize hybrid channel knowledge for multi-user scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Towards Optimal Secure Distributed Storage Systems with Exact Repair

Distributed storage systems in the presence of a wiretapper are considered. A distributed storage system (DSS) is parameterized by three parameters (n, k,d), in which a file stored across n distributed nodes, can be recovered from any k out of n nodes. If a node fails, any d out of (n-1) nodes help in the repair of the failed node. For such a (n,k,d)-DSS, two types of wiretapping scenarios are investigated: (a) Type-I (node) adversary which can wiretap the data stored on any l<k nodes; and a more severe (b) Type-II (repair data) adversary which can wiretap the contents of the repair data that is used to repair a set of l failed nodes over time. The focus of this work is on the practically relevant setting of exact repair regeneration in which the repair process must replace a failed node by its exact replica. We make new progress on several non-trivial instances of this problem which prior to this work have been open. The main contribution of this paper is the optimal characterization of the secure storage-vs-exact-repair-bandwidth tradeoff region of a (n,k,d)-DSS, with n<=4 and any l<k in the presence of both Type-I and Type-II adversaries. While the problem remains open for a general (n,k,d)-DSS with n>4, we present extensions of these results to a (n, n-1,n-1)-DSS, in presence of a Type-II adversary that can observe the repair data of any l=(n-2) nodes. The key technical contribution of this work is in developing novel information theoretic converse proofs for the Type-II adversarial scenario. From our results, we show that in the presence of Type-II attacks, the only efficient point in the storage-vs-exact-repair-bandwidth tradeoff is the MBR (minimum bandwidth regenerating) point. This is in sharp contrast to the case of a Type-I attack in which the storage-vs-exact-repair-bandwidth tradeoff allows a spectrum of operating points beyond the MBR point.

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preprint2013arXiv

Retroactive Anti-Jamming for MISO Broadcast Channels

Jamming attacks can significantly impact the performance of wireless communication systems. In addition to reducing the capacity, such attacks may lead to insurmountable overhead in terms of re-transmissions and increased power consumption. In this paper, we consider the multiple-input single-output (MISO) broadcast channel (BC) in the presence of a jamming attack in which a subset of the receivers can be jammed at any given time. Further, countermeasures for mitigating the effects of such jamming attacks are presented. The effectiveness of these anti-jamming countermeasures is quantified in terms of the degrees-of-freedom (DoF) of the MISO BC under various assumptions regarding the availability of the channel state information (CSIT) and the jammer state information at the transmitter (JSIT). The main contribution of this paper is the characterization of the DoF region of the two user MISO BC under various assumptions on the availability of CSIT and JSIT. Partial extensions to the multi-user broadcast channels are also presented.

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preprint2012arXiv

Degrees of Freedom Region of the MIMO Interference Channel with Output Feedback and Delayed CSIT

The two-user multiple-input multiple-output (MIMO) interference channel (IC) with arbitrary number of antennas at each terminal is considered and the degrees of freedom (DoF) region is characterized in the presence of noiseless channel output feedback from each receiver to its respective transmitter and availability of delayed channel state information at the transmitters (CSIT). It is shown that having output feedback and delayed CSIT can strictly enlarge the DoF region of the MIMO IC when compared to the case in which only delayed CSIT is present. The proposed coding schemes that achieve the corresponding DoF region with feedback and delayed CSIT utilize both resources, i.e., feedback and delayed CSIT in a non-trivial manner. It is also shown that the DoF region with local feedback and delayed CSIT is equal to the DoF region with global feedback and delayed CSIT, i.e., local feedback and delayed CSIT is equivalent to global feedback and delayed CSIT from the perspective of the degrees of freedom region. The converse is proved for a stronger setting in which the channels to the two receivers need not be statistically equivalent.

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preprint2012arXiv

Determination Of Optimal Number Of Clusters In Wireless Sensor Networks

Prolonged network lifetime, scalability and efficient load balancing are essential for optimal performance of a wireless sensor network. Clustering provides an effective way of extending the lifetime of a sensor network. Clustering is the process that divides sensor networks into smaller localized group (called clusters) of members with a cluster head. Clustering protocols need to elect optimal number of clusters in hierarchically structured wireless sensor networks. Any clustering scheme that elects clusters uniformly (irrespective of the distance from Base Station) incurs excessive energy usage on clusters proximal and distant to Base Station. In single hop networks a gradual increment in the energy depletion rate is observed as the distance from the cluster head increases. This work focuses on the analysis of wasteful energy consumption within a uniform cluster head election model (EPEM) and provides an analytic solution to reduce the overall consumption of energy usage amongst the clusters elected in a wireless sensor network. A circular model of sensor network is considered, where the sensor nodes are deployed around a centrally located Base Station. The sensor network is divided into several concentric rings centred at the Base Station. A model, Unequal Probability Election Model (UEPEM), which elects cluster heads non-uniformly is proposed. The probability of cluster head election depends on the distance from the Base Station. UEPEM reduces the overall energy usage by about 21% over EPEM. The performance of UEPEM improves as the number of rings is increased.

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preprint2012arXiv

Heegard-Berger and Cascade Source Coding Problems with Common Reconstruction Constraints

For the HB problem with the CR constraint, the rate-distortion function is derived under the assumption that the side information sequences are (stochastically) degraded. The rate-distortion function is also calculated explicitly for three examples, namely Gaussian source and side information with quadratic distortion metric, and binary source and side information with erasure and Hamming distortion metrics. The rate-distortion function is then characterized for the HB problem with cooperating decoders and (physically) degraded side information. For the cascade problem with the CR constraint, the rate-distortion region is obtained under the assumption that side information at the final node is physically degraded with respect to that at the intermediate node. For the latter two cases, it is worth emphasizing that the corresponding problem without the CR constraint is still open. Outer and inner bounds on the rate-distortion region are also obtained for the cascade problem under the assumption that the side information at the intermediate node is physically degraded with respect to that at the final node. For the three examples mentioned above, the bounds are shown to coincide. Finally, for the HB problem, the rate-distortion function is obtained under the more general requirement of constrained reconstruction, whereby the decoder's estimate must be recovered at the encoder only within some distortion.

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preprint2012arXiv

Minimum CSIT to achieve Maximum Degrees of Freedom for the MISO BC

Channel state information at the transmitter (CSIT) is a key ingredient in realizing the multiplexing gain provided by distributed MIMO systems. For a downlink multiple-input single output (MISO) broadcast channel, with M antennas at the transmitters and K single antenna receivers, the maximum multiplexing gain or the maximum degrees of freedom (DoF) is min(M,K). The optimal DoF of min(M,K) is achievable if the transmitter has access to perfect, instantaneous CSIT from all receivers. In this paper, we pose the question that what is minimum amount of CSIT required per user in order to achieve the maximum DoF of min(M,K). By minimum amount of CSIT per user, we refer to the minimum fraction of time that the transmitter has access to perfect and instantaneous CSIT from a user. Through a novel converse proof and an achievable scheme, it is shown that the minimum fraction of time, perfect CSIT is required per user in order to achieve the DoF of min(M,K) is given by min(M,K)/K.

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preprint2012arXiv

On the Feedback Capacity of the Fully Connected $K$-User Interference Channel

The symmetric K user interference channel with fully connected topology is considered, in which (a) each receiver suffers interference from all other (K-1) transmitters, and (b) each transmitter has causal and noiseless feedback from its respective receiver. The number of generalized degrees of freedom (GDoF) is characterized in terms of α, where the interference-to-noise ratio (INR) is given by INR=SNR^α. It is shown that the per-user GDoF of this network is the same as that of the 2-user interference channel with feedback, except for α=1, for which existence of feedback does not help in terms of GDoF. The coding scheme proposed for this network, termed cooperative interference alignment, is based on two key ingredients, namely, interference alignment and interference decoding. Moreover, an approximate characterization is provided for the symmetric feedback capacity of the network, when the SNR and INR are far apart from each other.

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preprint2012arXiv

On the Symmetric Feedback Capacity of the K-user Cyclic Z-Interference Channel

The K-user cyclic Z-interference channel models a situation in which the kth transmitter causes interference only to the (k-1)th receiver in a cyclic manner, e.g., the first transmitter causes interference only to the Kth receiver. The impact of noiseless feedback on the capacity of this channel is studied by focusing on the Gaussian cyclic Z-interference channel. To this end, the symmetric feedback capacity of the linear shift deterministic cyclic Z-interference channel (LD-CZIC) is completely characterized for all interference regimes. Using insights from the linear deterministic channel model, the symmetric feedback capacity of the Gaussian cyclic Z-interference channel is characterized up to within a constant number of bits. As a byproduct of the constant gap result, the symmetric generalized degrees of freedom with feedback for the Gaussian cyclic Z-interference channel are also characterized. These results highlight that the symmetric feedback capacities for both linear and Gaussian channel models are in general functions of K, the number of users. Furthermore, the capacity gain obtained due to feedback decreases as K increases.

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preprint2012arXiv

On the Synergistic Benefits of Alternating CSIT for the MISO BC

The degrees of freedom (DoF) of the two-user multiple-input single-output (MISO) broadcast channel (BC) are studied under the assumption that the form, I_i, i=1,2, of the channel state information at the transmitter (CSIT) for each user's channel can be either perfect (P), delayed (D) or not available (N), i.e., I_1 and I_2 can take values of either P, D or N, and therefore the overall CSIT can alternate between the 9 resulting states, each state denoted as I_1I_2. The fraction of time associated with CSIT state I_1I_2 is denoted by the parameter λ_{I_1I_2} and it is assumed throughout that λ_{I_1I_2}=λ_{I_2I_1}, i.e., λ_{PN}=λ_{NP}, λ_{PD}=λ_{DP}, λ_{DN}=λ_{ND}. Under this assumption of symmetry, the main contribution of this paper is a complete characterization of the DoF region of the two user MISO BC with alternating CSIT. Surprisingly, the DoF region is found to depend only on the marginal probabilities (λ_P, λ_D,λ_N)=(\sum_{I_2}λ_{PI_2},\sum_{I_2}λ_{DI_2}, \sum_{I_2}λ_{NI_2}), I_2\in {P,D,N}, which represent the fraction of time that any given user (e.g., user 1) is associated with perfect, delayed, or no CSIT, respectively. As a consequence, the DoF region with all 9 CSIT states, \mathcal{D}(λ_{I_1I_2}:I_1,I_2\in{P,D,N}), is the same as the DoF region with only 3 CSIT states \mathcal{D}(λ_{PP}, λ_{DD}, λ_{NN}), under the same marginal distribution of CSIT states, i.e., (λ_{PP}, λ_{DD},λ_{NN})=(λ_P,λ_D,λ_N). The results highlight the synergistic benefits of alternating CSIT and the tradeoffs between various forms of CSIT for any given DoF value.

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preprint2012arXiv

On X-Channels with Feedback and Delayed CSI

The sum degrees of freedom (DoF) of the two-user MIMO X-channel is characterized in the presence of output feedback and delayed channel state information (CSI). The number of antennas at each transmitters is assumed to be M and the number of antennas at each of the receivers is assumed to be N. It is shown that the sum DoF of the two-user MIMO X-channel is the same as the sum DoF of a two-user MIMO broadcast channel with 2M transmit antennas, and N antennas at each receiver. Hence, for this symmetric antenna configuration, there is no performance loss in the sum degrees of freedom due to the distributed nature of the transmitters. This result highlights the usefulness of feedback and delayed CSI for the MIMO X-channel. The K-user X-channel with single antenna at each transmitter and each receiver is also studied. In this network, each transmitter has a message intended for each receiver. For this network, it is shown that the sum DoF with partial output feedback alone is at least 2K/(K+1). This lower bound is strictly better than the best lower bound known for the case of delayed CSI assumption for all values of K.

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preprint2012arXiv

Secure Source Coding with a Helper

We consider a secure source coding problem with a rate-limited helper. In particular, Alice observes an independent and identically distributed (i.i.d.) source X and wishes to transmit this source losslessly to Bob over a rate-limited link. A helper (Helen), observes an i.i.d. correlated source Y and can transmit information to Bob over a separate rate-limited link. A passive eavesdropper (Eve) can observe the coded output of Alice, i.e., the link from Alice to Bob is public. The uncertainty about the source X at Eve, is measured by the conditional entropy of the source given the coded output of Alice. We completely characterize the rate-equivocation region for this secure source coding model, where we show that Slepian-Wolf binning of X with respect to the coded side information received at Bob is optimal. We next consider a modification of this model in which Alice also has access to the coded output of Helen. For the two-sided helper model, we characterize the rate-equivocation region. While the availability of side information at Alice does not reduce the rate of transmission from Alice, it significantly enhances the resulting equivocation at Eve. In particular, the resulting equivocation for the two-sided helper case is shown to be min(H(X),R_y), i.e., one bit from the two-sided helper provides one bit of uncertainty at Eve. From this result, we infer that Slepian-Wolf binning of X is suboptimal and one can further decrease the information leakage to the eavesdropper by utilizing the side information at Alice. We finally generalize these results to the case in which there is additional un-coded side information W available at Bob and characterize the rate-equivocation regions under the assumption that Y-X-W forms a Markov chain.

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preprint2011arXiv

Competitive Privacy in the Smart Grid: An Information-theoretic Approach

Advances in sensing and communication capabilities as well as power industry deregulation are driving the need for distributed state estimation in the smart grid at the level of the regional transmission organizations (RTOs). This leads to a new competitive privacy problem amongst the RTOs since there is a tension between sharing data to ensure network reliability (utility/benefit to all RTOs) and withholding data for profitability and privacy reasons. The resulting tradeoff between utility, quantified via fidelity of its state estimate at each RTO, and privacy, quantified via the leakage of the state of one RTO at other RTOs, is captured precisely using a lossy source coding problem formulation for a two RTO network. For a two-RTO model, it is shown that the set of all feasible utility-privacy pairs can be achieved via a single round of communication when each RTO communicates taking into account the correlation between the measured data at both RTOs. The lossy source coding problem and solution developed here is also of independent interest.

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preprint2011arXiv

Discriminatory Lossy Source Coding: Side Information Privacy

A lossy source coding problem is studied in which a source encoder communicates with two decoders, one with and one without correlated side information with an additional constraint on the privacy of the side information at the uninformed decoder. Two cases of this problem arise depending on the availability of the side information at the encoder. The set of all feasible rate-distortion-equivocation tuples are characterized for both cases. The difference between the informed and uninformed cases and the advantages of encoder side information for enhancing privacy are highlighted for a binary symmetric source with erasure side information and Hamming distortion.

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preprint2011arXiv

Multi-User Privacy: The Gray-Wyner System and Generalized Common Information

The problem of preserving privacy when a multivariate source is required to be revealed partially to multiple users is modeled as a Gray-Wyner source coding problem with K correlated sources at the encoder and K decoders in which the kth decoder, k = 1, 2, ...,K, losslessly reconstructs the kth source via a common link and a private link. The privacy requirement of keeping each decoder oblivious of all sources other than the one intended for it is introduced via an equivocation constraint at each decoder such that the total equivocation summed over all decoders is E. The set of achievable rates-equivocation tuples is completely characterized. Using this characterization, two different definitions of common information are presented and are shown to be equivalent.

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preprint2008arXiv

Dependence Balance Based Outer Bounds for Gaussian Networks with Cooperation and Feedback

We obtain new outer bounds on the capacity regions of the two-user multiple access channel with generalized feedback (MAC-GF) and the two-user interference channel with generalized feedback (IC-GF). These outer bounds are based on the idea of dependence balance which was proposed by Hekstra and Willems [1]. To illustrate the usefulness of our outer bounds, we investigate three different channel models. We first consider a Gaussian MAC with noisy feedback (MAC-NF), where transmitter $k$, $k=1,2$, receives a feedback $Y_{F_{k}}$, which is the channel output $Y$ corrupted with additive white Gaussian noise $Z_{k}$. As the feedback noise variances become large, one would expect the feedback to become useless, which is not reflected by the cut-set bound. We demonstrate that our outer bound improves upon the cut-set bound for all non-zero values of the feedback noise variances. Moreover, in the limit as $σ_{Z_{k}}^{2}\to \infty$, $k=1,2$, our outer bound collapses to the capacity region of the Gaussian MAC without feedback. Secondly, we investigate a Gaussian MAC with user-cooperation (MAC-UC), where each transmitter receives an additive white Gaussian noise corrupted version of the channel input of the other transmitter [2]. For this channel model, the cut-set bound is sensitive to the cooperation noises, but not sensitive enough. For all non-zero values of cooperation noise variances, our outer bound strictly improves upon the cut-set outer bound. Thirdly, we investigate a Gaussian IC with user-cooperation (IC-UC). For this channel model, the cut-set bound is again sensitive to cooperation noise variances but not sensitive enough. We demonstrate that our outer bound strictly improves upon the cut-set bound for all non-zero values of cooperation noise variances.

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preprint2008arXiv

Outer Bounds for Multiple Access Channels with Feedback using Dependence Balance

We use the idea of dependence balance to obtain a new outer bound for the capacity region of the discrete memoryless multiple access channel with noiseless feedback (MAC-FB). We consider a binary additive noisy MAC-FB whose feedback capacity is not known. The binary additive noisy MAC considered in this paper can be viewed as the discrete counterpart of the Gaussian MAC-FB. Ozarow established that the capacity region of the two-user Gaussian MAC-FB is given by the cut-set bound. Our result shows that for the discrete version of the channel considered by Ozarow, this is not the case. Direct evaluation of our outer bound is intractable due to an involved auxiliary random variable whose large cardinality prohibits an exhaustive search. We overcome this difficulty by using functional analysis to explicitly evaluate our outer bound. Our outer bound is strictly less than the cut-set bound at all points on the capacity region where feedback increases capacity. In addition, we explicitly evaluate the Cover-Leung achievable rate region for the binary additive noisy MAC-FB in consideration. Furthermore, using the tools developed for the evaluation of our outer bound, we also explicitly characterize the boundary of the feedback capacity region of the binary erasure MAC, for which the Cover-Leung achievable rate region is known to be tight. This last result confirms that the feedback strategies developed by Kramer for the binary erasure MAC are capacity achieving.

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