Interactive statistical decision making (ISDM) features algorithm-dependent data generated through interaction. Existing information-theoretic lower bounds in ISDM largely target expected risk, while tail-sensitive objectives are less developed. We generalize the interactive Fano framework of Chen et al. by replacing the hard success event with a randomized one-bit statistic representing an arbitrary bounded transform of the loss. This yields a Bernoulli f-divergence inequality, which we invert to obtain a two-sided interval for the transform, recovering the previous result as a special case. Instantiating the transform with a bounded hinge and using the Rockafellar-Uryasev representation, we derive lower bounds on the prior-predictive (Bayesian) CVaR of bounded losses. For KL divergence with the mixture reference distribution, the bound becomes explicit in terms of mutual information via Pinsker's inequality.

We investigate Dobrushin coefficients of discrete Markov kernels that have bounded pointwise maximal leakage (PML) with respect to all distributions with a minimum probability mass bounded away from zero by a constant $c>0$. This definition recovers local differential privacy (LDP) for $c\to 0$. We derive achievable bounds on contraction in terms of a kernels PML guarantees, and provide mechanism constructions that achieve the presented bounds. Further, we extend the results to general $f$-divergences by an application of Binette's inequality. Our analysis yields tighter bounds for mechanisms satisfying LDP and extends beyond the LDP regime to any discrete kernel.

Linear queries, as the basis of broad analysis tasks, are often released through privacy mechanisms based on differential privacy (DP), the most popular framework for privacy protection. However, DP adopts a context-free definition that operates independently of the data-generating distribution. In this paper, we revisit the privacy analysis of the Laplace mechanism through the lens of pointwise maximal leakage (PML). We demonstrate that the distribution-agnostic definition of the DP framework often mandates excessive noise. To address this, we incorporate an assumption about the prior distribution by lower-bounding the probability of any single record belonging to any specific class. With this assumption, we derive a tight, context-aware leakage bound for general linear queries, and prove that our derived bound is strictly tighter than the standard DP guarantee and converges to the DP guarantee as this probability lower bound approaches zero. Numerical evaluations demonstrate that by exploiting this prior knowledge, the required noise scale can be reduced while maintaining privacy guarantees.

This paper explores the implications of guaranteeing privacy by imposing a lower bound on the information density between the private and the public data. We introduce a novel and operationally meaningful privacy measure calledpointwise maximal cost(PMC) and demonstrate that imposing an upper bound on PMC is equivalent to enforcing a lower bound on the information density. PMC quantifies the information leakage about a secret to adversaries who aim to minimize non-negative cost functions after observing the outcome of a privacy mechanism. When restricted to finite alphabets, PMC can equivalently be defined as the information leakage to adversaries aiming to minimize the probability of incorrectly guessing randomized functions of the secret. We study the properties of PMC and apply it to standard privacy mechanisms to demonstrate its practical relevance. Through a detailed examination, we connect PMC with other privacy measures that impose upper or lower bounds on the information density. These are pointwise maximal leakage (PML), local differential privacy (LDP), and (asymmetric) local information privacy. In particular, we show that a mechanism satisfies LDP if and only if it has both bounded PMC and bounded PML. Overall, our work fills a conceptual and operational gap in the taxonomy of privacy measures, bridges existing disconnects between different frameworks, and offers insights for selecting a suitable notion of privacy in a given application.

We study agents acting in an unknown environment where the agent’s goal is to find a robust policy. We consider robust policies as policies that achieve high cumulative rewards for all possible environments. To this end, we consider agents minimizing the maximum regret over different environment parameters, leading to the study of minimax regret. This research focuses on deriving information-theoretic bounds for minimax regret in Markov Decision Processes (MDPs) with a finite time horizon. Building on concepts from supervised learning, such as minimum excess risk (MER) and minimax excess risk, we use recent bounds on the Bayesian regret to derive minimax regret bounds. Specifically, we establish minimax theorems and use bounds on the Bayesian regret to perform minimax regret analysis using these minimax theorems. Our contributions include defining a suitable minimax regret in the context of MDPs, finding information-theoretic bounds for it, and applying these bounds in various scenarios.

This paper studies the Bayesian regret of the Thompson Sampling algorithm for bandit problems, building on the information-theoretic framework introduced by Russo and Van Roy [1]. Specifically, it extends the rate-distortion analysis of Dong and Van Roy [2], which provides near-optimal bounds for linear bandits. A key limitation of these results is the assumption of a finite action space. We address this by extending the analysis to settings with infinite and continuous action spaces. Additionally, we specialize our results to bandit problems with expected rewards that are Lipschitz continuous with respect to the action space, deriving a regret bound that explicitly accounts for the complexity of the action space.

IoT-based low-cost gas sensors networks are important for environmental monitoring, but their regular calibrations are needed to achieve acceptable sensing performance. A critical step in network calibration is identifying when sensors within the network are sensing the same phenomenon, which is essential for accurate calibration. In this paper, we propose an adaptive similarity-search-based method for detecting these periods of similarity under the assumption of linear sensor drift. Our method leverages the relationships between neighboring sensors’ measurements to enhance calibration accuracy, outperforming the commonly used Pearson correlation approach. We validate the effectiveness of our method through experiments with both synthetic data and real-world CO2sensor networks, demonstrating improved calibration accuracy and reliability.

This paper studies integrated sensing and communication (ISAC) systems with two rate-limited helpers who observe the channel state sequence and the feedback sequence, respectively. Depending on the timing to compress and use the state information, our proposed coding scheme gives an inner bound of the capacity-compression-distortion tradeoff region. The tradeoff is realized by sending part of the state information at the beginning of the transmission to facilitate the communication and compressing the remaining part together with the feedback signal. A special case with a tight bound is also provided.

We propose a zero estimation cost (ZEC) scheme for causal-encoding noncausal-decoding vector-valued Witsenhausen counterexample based on the coordination coding result. In contrast to source coding, our goal is to communicate a controlled system state. The introduced ZEC scheme is a joint controlcommunication approach that transforms the system state into a sequence that can be efficiently communicated using block coding. The noncausal decoder receives sufficient information for reconstructing the system state perfectly, enabling the achievable estimation cost to be zero. Numerical results show that our approach significantly reduces the power budget required for achieving zero-estimation-cost state reconstruction at the decoder. In the second part, we introduce a more general non-zero estimation cost (Non-ZEC) scheme. We observe numerically that the Non-ZEC scheme operates as a time-sharing mechanism between Witsenhausen's original two-point strategy and the ZEC scheme. Overall, by leveraging block-coding gain, our proposed methods substantially improve the power-estimation trade-off for Witsenhausen counterexample.

We study different classes of reinforcement learning problems using the minimax regret framework. We formalize a finite-horizon reinforcement learning problem setting that is suitable for the information-theoretic analysis of minimax regret which encompasses linear bandits, Markov decision processes, linear Markov decision processes, and other reinforcement learning problems. We derive a minimax theorem applicable to this setting that does not require any finiteness or deterministic policy constraints. Using this theorem, we show that any Bayesian regret bound can be used to bound the minimax regret within our framework. We then apply the minimax theorem to obtain an information-theoretic upper bound for the minimax regret, leveraging a general Bayesian regret bound. The derived minimax regret bound inherits key properties of the Bayesian regret bound, including its ability to isolate factors such as the information ratio, the mutual information between the learning target and the environment, and the Bayesian regret of the target policy. Finally, we demonstrate the applicability of our bounds in various settings, including linear bandits, episodic reinforcement learning, and linear Markov decision processes, recovering known results for the minimax regret.

A multi-user private data compression problem is studied. A server has access to a database of$N$files,$(Y_{1},\ldots,\ Y_{N})$, each of size$F$bits and is connected to an encoder. The encoder is connected through an unsecured link to a user. We assume that each file$Y_{i}$is arbitrarily correlated with a private attribute$X$, which is assumed to be accessible by the encoder. Moreover, an adversary is assumed to have access to the link. The users and the encoder have access to a shared secret key$W$. We assume that at each time the user asks for a file$Y_{d_{i}}$, where$(d_{1},\ \ldots,\ d_{K})$corresponds to the demand vector. The goal is to design the delivered message$\mathcal{C}=(\mathcal{C}_{1},\ \ldots,\mathcal{C}_{K})$after the user send his demands to the encoder such that the average length of$\mathcal{C}$is minimized, while satisfying:$\mathbf{i}$. The message$\mathcal{C}$does not reveal any information about$X$, i.e.,$X$and$\mathcal{C}$are independent, which corresponds to the perfect privacy constraint; ii. The user is able to decode its demands,$Y_{d_{i}}$, by using$\mathcal{C}$, and the shared key$W$. Here, the encoder sequentially encode each demand$Y_{d_{i}}$at time$i$, using the shared key and previous encoded messages. We propose a variable-length coding scheme that uses privacy-aware compression techniques. We study proposed upper and lower bounds on the average length of$\mathcal{C}$in an example. Finally, we study an application considering cache-aided networks.

We analyze the privacy guarantees of the Laplace mechanism releasing the histogram of a dataset through the lens of pointwise maximal leakage (PML). While differential privacy is commonly used to quantify the privacy loss, it is a context free definition that does not depend on the data distribution. In contrast, PML enables a more refined analysis by incorporating assumptions about the data distribution. We show that when the probability of each histogram bin is bounded away from zero, stronger privacy protection can be achieved for a fixed level of noise. Our results demonstrate the advantage of context-aware privacy measures and show that incorporating assumptions about the data can improve privacy-utility tradeoffs.

Distribution-preserving integrated sensing and communication is investigated in this paper. In addition to the distortion constraint, we impose another constraint on the distance between the reconstructed sequence distribution and the original state distribution to force the system to preserve the statistical property of the channel states. An inner bound of the distribution-preserving capacity-distortion region is provided with some capacity region results under special cases. Furthermore, we consider the case where the system aims to keep the reconstructed sequence secret from an eavesdropper who also observes the channel output and receives rate-limited side information about the estimator. An inner bound of the tradeoff region and a capacity-achieving special case are presented. In addition, we provide some numerical examples to illustrate the tradeoff between the communication rate, distortion, and the preservation of the distribution.

Strong secrecy communication over a discrete memoryless state-dependent multiple access channel (SD-MAC) with an external eavesdropper is investigated. The channel is governed by discrete memoryless and i.i.d. channel states, and the channel state information (CSI) is revealed to the encoders in a causal manner. The main results of this paper are inner and outer bounds of the capacity region, for which we investigate coding schemes incorporating wiretap coding and secret key agreements between the sender and the legitimate receiver. Two kinds of block Markov coding schemes are proposed. The first is a new coding scheme that uses backward decoding and the Wyner-Ziv coding, and the secret key is constructed from a lossy description of the CSI. The other is an extended version of an existing coding scheme for point-to-point wiretap channels with causal CSI. A numerical example shows that the achievable region given by the first coding scheme can be strictly larger than the second one. However, these two schemes do not outperform each other in general, and there exist some numerical examples in which each coding scheme achieves some rate pairs that cannot be achieved by another scheme. Our established inner bound reduces to some best-known results in the literature as special cases. We further investigate some capacity-achieving cases for state-dependent multiple access wiretap channels (SD-MAWCs) with degraded message sets. It turns out that the two coding schemes are both optimal in these cases.

Distribution-preserving integrated sensing and communication with secure reconstruction is investigated in this paper. In addition to the distortion constraint, we impose another constraint on the distance between the reconstructed sequence distribution and the original state distribution to force the system to preserve the statistical property of the channel states. An inner bound of the distribution-preserving capacity-distortion region is provided with some capacity region results under special cases. A numerical example demonstrates the tradeoff between the communication rate, reconstruction distortion and distribution preservation. Furthermore, we consider the case that the reconstructed sequence should be kept secret from an eavesdropper who also observes the channel output. An inner bound of the tradeoff region and a capacity-achieving special case are presented.

We examine the relationship between privacy metrics that utilize information density to measure information leakage between a private and a disclosed random variable. Firstly, we prove that bounding the information density from above or below in turn implies a lower or upper bound on the information density, respectively. Using this result, we establish new relationships between local information privacy, asymmetric local information privacy, pointwise maximal leakage and local differential privacy. We further provide applications of these relations to privacy mechanism design. Secondly, we provide equivalence statements of lower bounds on information density and risk-averse adversaries. More specifically, we prove an equivalence between a guessing framework and a cost-function framework that both result in the same lower bound on the information density.

We investigate the problem of secure multi-terminal strong coordination aided by a multiple-access wiretap channel (MAC-WT). In this setup, independent and identically distributed (i.i.d.) copies of correlated sources are observed by two transmitters who encode the channel inputs to the MAC-WT. The legitimate receiver on observing the channel output must produce approximately i.i.d. copies of an output random variable jointly distributed with the two sources. Furthermore, we demand that an external eavesdropper learns essentially nothing about the sources and the simulated output sequence by observing its corresponding MAC-WT output. This is aided by the presence of independent pairwise shared randomness between each encoder and the legitimate decoder. The shared randomness rate tuples which permit such channel simulation with strong secrecy are of interest. We derive an achievable rate region based on a combination of coordination coding and wiretap coding, along with an outer bound. The inner bound is shown to be tight and a complete characterization is derived for the special case when the sources are independent and the legitimate receiver's channel is composed of deterministic links.

We study a multi-task private semantic communication problem, in which an encoder has access to an information source arbitrarily correlated with some latent private data. A user has$L$tasks with priorities. The encoder designs a message to be revealed which is called the semantic of the information source. Due to the privacy constraints the semantic can not be disclosed directly and the encoder adds noise to produce disclosed data. The goal is to design the disclosed data that maximizes the weighted sum of the utilities achieved by the user while satisfying a privacy constraint on the private data. In this work, we first consider a single-task scenario and design the added noise utilizing various methods including the extended versions of the Functional Representation Lemma, Strong Functional Representation Lemma, and separation technique. We then study the multi-task scenario and derive a simple design of the source semantics. We show that in the multi-task scenario the main problem can be divided into multiple parallel single-task problems.

We investigate the Witsenhausen counterexample in a continuous vector-valued context with a causal encoder and noncausal decoder. Our main result is the optimal single-letter condition that characterizes the set of achievable Witsenhausen power costs and estimation costs, leveraging a modified weak typicality approach. In particular, we accommodate our power analysis to the causal encoder constraint, and provide an improved distortion error analysis for the challenging estimation of the interim state. Interestingly, the idea of dual role of control is explicitly captured by the two auxiliary random variables.

We investigate the problem of multi-terminal strong coordination over a network of noiseless links with degraded source observations. In this setup, independent and identically distributed (i.i.d.) copies of correlated sources are observed by two transmitters, with one of the source observations being common while the other one is private. The transmitters communicate their source descriptions over noiseless links to the receiver, which must produce approximately i.i.d. copies of an output random variable jointly distributed with the two sources. This is aided by the presence of common randomness shared between all three parties. The communication and common randomness rate tuples which permit such channel simulation are of interest. We derive a complete characterization for this multi-terminal strong coordination problem. It is observed that the optimal scheme is based on a superposition structure, where the common source description forms the base layer and the private source description forms the top layer.

We study the continuous vector-valued Witsen-hausen counterexample with Gaussian states through the lens of empirical coordination coding. We characterize the region of achievable pairs of costs in three scenarios: (i) causal encoding and causal decoding, (ii) causal encoding and causal decoding with channel feedback, and (iii) causal encoding and noncausal decoding with channel feedback. In these vector-valued versions of the problem, the optimal coding schemes must rely on a time-sharing strategy, since the region of achievable pairs of costs might not be convex in the scalar version of the problem. We examine the role of the channel feedback when the encoder is causal and the decoder is either causal or non-causal, and we show that feedback improves the performance, only when the decoder is non-causal.

Data publishing under privacy constraints can be achieved with mechanisms that add randomness to data points when released to an untrusted party, thereby decreasing the data’s utility. In this paper, we analyze this privacy-utility tradeoff for the pointwise maximal leakage (PML) privacy measure and provide optimal privacy mechanisms for a general class of convex utility functions. PML was recently proposed as an operationally meaningful privacy measure based on two equivalent threat models: An adversary guessing a randomized function and an adversary aiming to maximize a general gain function. We prove a cardinality bound, showing that output alphabets of optimal mechanisms in this context need not to be larger than the size of their inputs. Then, we characterize the optimization region as a (convex) polytope. We derive closed-form optimal privacy mechanisms for arbitrary priors in the high privacy regime (when the privacy parameter is sufficiently small) and uniform priors for all ranges of the privacy parameter using tools from convex analysis. Furthermore, we present a linear program that can compute optimal mechanisms for PML in a general setting. We conclude by demonstrating the performance of the closed-form mechanisms through numerical simulations.

The vector-valued extension of the famous Witsenhausen counterexample setup is studied where the encoder, i.e. the first decision maker, non-causally knows and encodes the i.i.d. state sequence and the decoder, i.e. the second decision maker, causally estimates the interim state. The coding scheme is transferred from the finite alphabet coordination problem, for which it is proved to be optimal. The extension to the Gaussian setup is based on a non-standard weak typicality approach and requires a careful average estimation error analysis since the interim state is estimated by the decoder. We provide a single-letter expression that characterizes the optimal trade-off between the Witsenhausen power cost and estimation cost. The two auxiliary random variables improve the communication with the decoder, while performing the dual role of the channel input, which also controls the state of the system. Interestingly, we show that a pair of discrete and continuous auxiliary random variables, outperforms both Witsenhausen two-point strategy and the best affine policies. The optimal choice of random variables remains unknown.

We study an information theoretic privacy mechanism design problem for two scenarios where the private data is either observable or hidden. In the hidden private data scenario, an agent observes useful dataYthat is correlated with private dataX, and generate disclosed dataUwhich maximizes the revealed information aboutYwhile satisfying a bounded privacy leakage constraint. Considering the other scenario, the agent has additional access toX. To design the privacy mechanism, we first extend the Functional Representation Lemma and Strong Functional Representation Lemma by relaxing the independence condition and thereby allowing a certain leakage. We then find lower and upper bounds on the privacy-utility trade-offs in both scenarios. In particular, for the case where no leakage is allowed andXis observable, our upper and lower bounds improve previous bounds. Considering bounded mutual information as privacy constraint and the observable private data scenario we show that if the common information and mutual information betweenXandYare equal, then the attained upper bound is tight. Finally, the privacy-utility trade-off with prioritized private data is studied where part ofXis more private than the remaining part.

Strong secrecy communication over a discrete memoryless state-dependent multiple access channel (SD-MAC) with an external eavesdropper is investigated. The channel is governed by discrete memoryless and i.i.d. channel states and the channel state information (CSI) is revealed to the encoders in a causal manner. An inner bound of the capacity is provided. To establish the inner bound, we investigate coding schemes incorporating wiretap coding and secret key agreement between the sender and the legitimate receiver. Two kinds of block Markov coding schemes are studied. The first one uses backward decoding and Wyner-Ziv coding and the secret key is constructed from a lossy reproduction of the CSI. The other one is an extended version of the existing coding scheme for point-to-point wiretap channels with causal CSI. We further investigate some capacity-achieving cases for state-dependent multiple access wiretap channels (SD-MAWCs) with degraded message sets. It turns out that the two coding schemes are both optimal in these cases.

We extend the results of Ghourchian et al. [1] to joint source-channel coding with eavesdropping. Our work characterizes the sequential encoding process using the cumulative rate distribution functions (CRDF) and includes a security constraint using the cumulative leakage distribution functions (CLF). The information leakage is defined based on the mutual information between the source and the output of the wiretap channel to the eavesdropper. We derive inner and outer bounds on the achievable CRDF for a given source and CLF, and show that the bounds are tight when the distribution achieving the capacity of the wiretap channel is the same as the one achieving the capacity of the channel.

In this work, we study the performance of the Thompson Sampling algorithm for Contextual Bandit problems based on the framework introduced by [1] and their concept of lifted information ratio. First, we prove a comprehensive bound on the Thompson Sampling expected cumulative regret that depends on the mutual information of the environment parameters and the history. Then, we introduce new bounds on the lifted information ratio that hold for sub-Gaussian rewards, thus generalizing the results from [1] which analysis requires binary rewards. Finally, we provide explicit regret bounds for the special cases of unstructured bounded contextual bandits, structured bounded contextual bandits with Laplace likelihood, structured Bernoulli bandits, and bounded linear contextual bandits.

Pointwise maximal leakage (PML) is an operationally meaningful privacy measure that quantifies the amount of information leaking about a secret X to a single outcome of a related random variable Y. In this paper, we extend the notion of PML to random variables on arbitrary probability spaces. We develop two new definitions: First, we extend PML to countably infinite random variables by considering adversaries who aim to guess the value of discrete (finite or countably infinite) functions of X. Then, we consider adversaries who construct estimates of X that maximize the expected value of their corresponding gain functions. We use this latter setup to introduce a highly versatile form of PML that captures many scenarios of practical interest whose definition requires no assumptions about the underlying probability spaces.

A privacy mechanism design problem is studied through the lens of information theory. In this work, an agent observes useful data Y = (Y1,…,YN) that is correlated with private data X = (X1,…,XN) which is assumed to be also accessible by the agent. Here, we consider K users where user i demands a sub-vector of Y, denoted by Ci. The agent wishes to disclose Cito user i. A privacy mechanism is designed to generate disclosed data U which maximizes a linear combinations of the users utilities while satisfying a bounded privacy constraint in terms of mutual information. In a similar work it has been assumed that Xiis a deterministic function of Yi, however in this work we let Xiand Yibe arbitrarily correlated.First, an upper bound on the privacy-utility trade-off is obtained by using a specific transformation, Functional Representation Lemma and Strong Functional Representation Lemma, then we show that the upper bound can be decomposed into N parallel problems. Next, lower bounds on privacy-utility tradeoff are derived using Functional Representation Lemma and Strong Functional Representation Lemma. The upper bound is tight within a constant and the lower bounds assert that the disclosed data is independent of all $\left\{ {{X_j}} \right\}_{i = 1}^N$ except one which we allocate the maximum allowed leakage to it. Finally, the obtained bounds are studied in special cases.

A private cache-aided compression problem is studied, where a server has access to a database of$N$files,$(Y_{1},\ldots,Y_{N})$, each of size$F$bits and is connected through a shared link to$K$users, each equipped with a local cache of size$MF$bits. In the placement phase, the server fills the users' caches without knowing their demands, while the delivery phase takes place after the users send their demands to the server. We assume that each file$Y_{i}$is arbitrarily correlated with a private attribute$X$, and an adversary is assumed to have access to the shared link. The users and the server have access to a shared key$W$. The goal is to design the cache contents and the delivered message$\mathcal{C}$such that the average length of$\mathcal{C}$is minimized, while satisfying:$\mathbf{i}$. The response$\mathcal{C}$does not reveal any information about$X$, i.e.,$X$and$\mathcal{C}$are independent, which corresponds to the perfect privacy constraint;$\mathbf{ii}$. User$i$is able to decode its demand,$Y_{d_{i}}$, by using$\mathcal{C}$, its local cache$Z_{i}$, and the shared key$W$. Since the database is correlated with$X$, existing codes for cache-aided delivery do not satisfy the perfect privacy condition. Indeed, we propose a variable-length coding scheme that combines privacy-aware compression with coded caching techniques. In particular, we use two-part code construction and Functional Representation Lemma. Finally, we extend the results to the case, where$X$and$\mathcal{C}$can be correlated, i.e., non-zero leakage is allowed.

In this paper, we design privacy-preserving and cost-efficient energy management strategies for smart grid users that are equipped with renewable energy sources. The adversary is assumed to employ a factorial hidden Markov model based inference for load disaggregation, and the corresponding joint log-likelihood of the model is utilized as the privacy measure. The studied dynamic pricing model is applicable to a commodity-limited market, where the price of unit amount of energy is determined by the users’ aggregated power request. The users’ energy management strategies are designed under a non-cooperative game framework, where each user aims to optimize a weighted sum objective of both privacy measure and energy cost saving. The users’ non-cooperative game is shown to admit a unique pure strategy Nash equilibrium. As an extension, a computational-efficient distributed Nash equilibrium energy management strategy seeking method is proposed, which also avoids the privacy leakage due to the sharing of payoff functions between users. The performance of practical designs of the energy management strategies in the equilibrium is finally illustrated by numerical experiments.

In this article, data-driven self-calibration algorithms for the low-cost gas sensors are designed. The sensor measurement errors happen due to the imperfect compensation for the variation of sensor component behavior that is caused by changing of environmental factors. To calibrate the sensors, the hidden Markov model (HMM) is utilized to characterize the statistical dependency between the environmental factors and the variation of sensor component behavior. Considering the time-varying property of this dependency, a time-adaptive learning framework is further designed to update the HMM so that the time-varying drift process can be better tracked over a long term. More specifically, a time-adaptive expectation maximization learning approach is proposed to efficiently update the HMM parameters. A closed form of the convergence rate of this time-adaptive learning approach is derived, which provides a theoretical guarantee on the time efficiency as well as the computational efficiency. The performance of the scheme is illustrated in numerical experiments utilizing real data, which shows that long-term stable calibration performance can be achieved.

We introduce a privacy measure called pointwise maximal leakage, generalizing the pre-existing notion of maximal leakage, which quantifies the amount of information leaking about a secret$X$by disclosing a single outcome of a (randomized) function calculated on$X$. Pointwise maximal leakage is a robust and operationally meaningful privacy measure that captures the largest amount of information leaking about$X$to adversaries seeking to guess arbitrary (possibly randomized) functions of$X$, or equivalently, aiming to maximize arbitrary gain functions. We study several properties of pointwise maximal leakage, e.g., how it composes over multiple outcomes, how it is affected by pre- and post-processing, etc. Furthermore, we propose to view information leakage as a random variable which, in turn, allows us to regard privacy guarantees as requirements imposed on different statistical properties of the information leakage random variable. We define several privacy guarantees and study how they behave under pre-processing, post-processing and composition. Finally, we examine the relationship between pointwise maximal leakage and other privacy notions such as local differential privacy, local information privacy,$f$-information, and so on. Overall, our paper constructs a robust and flexible framework for privacy risk assessment whose central notion has a strong operational meaning which can be adapted to a variety of applications and practical scenarios.

Non-intrusive load monitoring (NILM) is the process of disaggregating total electricity consumption measured by a smart meter into individual appliances’ contributions. In this paper, we present a privacy control strategy that selectively filters appliances’ consumption from the smart meter measurements to hinder NILM disaggregation performance. The privacy controller uses charging and discharging operations of an energy storage to achieve desired smart meter measurements. We model the household consumption using both additive and difference factorial hidden Markov models and design a control strategy to minimize privacy leakage measured in terms of Bayesian risk due to maximum a posteriori detection. Due to the high computational complexity of the optimal control strategy, we propose a computationally efficient sub-optimal strategy. We evaluate the proposed approaches using the ECO data set and show their privacy improvements against the Viterbi disaggregation algorithm.

Preventing unintentional leakage of information about the training set has high relevance for many machine learning tasks, such as medical image segmentation. While differential privacy (DP) offers mathematically rigorous protection, the high output dimensionality of segmentation tasks prevents the direct application of state-of-the-art algorithms such as Private Aggregation of Teacher Ensembles (PATE). In order to alleviate this problem, we propose to learn dimensionality-reducing transformations to map the prediction target into a bounded lower-dimensional space to reduce the required noise level during the aggregation stage. To this end, we assess the suitability of principal component analysis (PCA) and autoencoders. We conclude that autoencoders are an effective means to reduce the noise in the target variables.

Pointwise maximal leakage (PML) is a robust and operationally meaningful privacy measure that quantifies the amount of information leaking about a secret X by disclosing a single outcome of a (randomized) function calculated on X. In this paper, we define a new privacy measure called event maximal leakage (EML), which generalizes PML by quantifying the amount of information leaking about X to arbitrary events. Then, we use our new privacy measure to define a new probabilistic privacy guarantee called (ϵ, δ)-EML. We study the data-processing and composition properties of (ϵ, δ)-EML and other privacy guarantees, where our goal is to understand whether or not they are closed under pre- and post-processing, and how they change as a result of adaptively composing privacy mechanisms.

The design of privacy mechanisms for two scenarios is studied where the private data is hidden or observable. In the first scenario, an agent observes useful data Y , which is correlated with private data X, and wants to disclose the useful information to a user. A privacy mechanism is employed to generate data U that maximizes the revealed information about Y while satisfying a privacy criterion. In the second scenario, the agent has additionally access to the private data. To this end, the Functional Representation Lemma and Strong Functional Representation Lemma are extended relaxing the independence condition and thereby allowing a certain leakage. Lower bounds on privacy-utility trade-off are derived for the second scenario as well as upper bounds for both scenarios. In particular, for the case where no leakage is allowed, our upper and lower bounds improve previous bounds.

An information theoretic privacy mechanism design problem for two scenarios is studied where the private data is either hidden or observable. In each scenario, privacy leakage constraints are considered using two different measures. In these scenarios the private data is hidden or observable. In the first scenario, an agent observes useful data Y that is correlated with private data X, and wishes to disclose the useful information to a user. A privacy mechanism is designed to generate disclosed data U which maximizes the revealed information about Y while satisfying a per-letter privacy constraint. In the second scenario, the agent has additionally access to the private data. First, the Functional Representation Lemma and Strong Functional Representation Lemma are extended by relaxing the independence condition to find a lower bound considering the second scenario. Next, lower bounds as well as upper bounds on privacy-utility trade-off are derived for both scenarios. In particular, for the case where X is deterministic function of Y, we show that our upper and lower bounds are asymptotically optimal considering the first scenario.

In this article, data-driven self-calibration algorithms for the Internet of Things-based gas concentration monitoring systems embedded with low-cost gas sensors are designed. The measurement errors are assumed to be caused by imperfect compensation for the variation of sensor component behavior. Specifically, the calibration procedure for the nondispersive infrared$CO_{2}$sensors is developed, for which the temperature dependency is the most dominant drift source. For a single sensor, the hidden Markov model is used to characterize the statistical relationship between different quantities introduced by the physical model that builds on the Beer–Lambert law. For the calibration in the Internet of Things-based system, sensors first transmit their belief functions of the true gas concentration level to the cloud. Then, the cloud fusion center computes a fused belief function according to certain rules. This belief function is then used as reference for calibrating the sensors. To deal with the case where belief functions highly conflict with each other, a Wasserstein distance-based weighted average belief function fusion approach is first proposed as a networked calibration algorithm. To achieve more long-term stable calibration results, the networked calibration problem is further formulated as a partially observed Markov decision process (MDP) problem, and the calibration strategies are derived in a sequential manner. Correspondingly, the deep$Q$-network approach is applied as a computationally efficient method to solve the proposed MDP problem. The performance of practical designs of the proposed self-calibration algorithms is finally illustrated in numerical experiments utilizing real data.

We study a statistical signal processing privacy problem, where an agent observes useful data$Y$and wants to reveal the information to a user. Since the useful data is correlated with the private data$X$, the agent employs a privacy mechanism to generate data$U$that can be released. We study the privacy mechanism design that maximizes the revealed information about$Y$while satisfying a strong$\ell _{1}$-privacy criterion. When a sufficiently small leakage is allowed, we show that the optimizer distributions of the privacy mechanism design problem have a specific geometry, i.e., they are perturbations of fixed vector distributions. This geometrical structure allows us to use a local approximation of the conditional entropy. By using this approximation the original optimization problem can be reduced to a linear program so that an approximate solution for the optimal privacy mechanism can be easily obtained. The main contribution of this work is to consider a non-invertible leakage matrix with non-zero leakage. In our first example, inspired by a watermark application, we first demonstrate the accuracy of the approximation. Then, we employ different measures for utility and privacy leakage to compare the privacy-utility trade-off using our approach with other methods. In particular, we show that by allowing small leakage, significant utility can be achieved using our method compared to the case where no leakage is allowed. In the second and third examples which are based on the MNIST data set and medical applications, we illustrate the suggested design for disclosed data$U$. It has been shown that the letters of$Y$which are disclosing more information about$X$are combined (randomized) to produce a new letter of$U$.

In this work, we present polar code designs that offer a provably optimal solution for biometric identification and authentication systems under noisy enrollment for certain sources and observation channels. We consider a discrete memoryless biometric source and discrete symmetric memoryless observation channels. It is shown that the proposed polar code designs achieve the fundamental limits with privacy and secrecy constraints. Depending on how the secret keys are extracted and whether the privacy leakage rate should be close to zero, we consider four related setups, which are (i) the generated secret key system, (ii) the chosen secret key system, (iii) the generated secret key system with zero leakage, and (iv) the chosen secret key system with zero leakage. For the first two setups, (i) and (ii), the privacy level is characterized by the privacy leakage rate. For the last two setups (iii) and (iv), private keys are additionally employed to achieve close to zero privacy leakage rate. In setups (i) and (iii), it is assumed that the secret keys are generated, i.e., extracted from biometric information. While in setups (ii) and (iv), secret keys provided to the system are chosen uniformly at random from some trustful source. This work provides the first examples of fundamental limits-achieving code designs for identification and authentication. Moreover, since the code designs are based on polar codes and many existing works study low-complexity and short block-length polar coding, the proposed code designs in this work provide the code design structure and a framework for the application of biometric identification and authentication.

Optimal design of distributed decision policies can be a difficult task, illustrated by the famous Witsenhausen counterexample. In this paper we characterize the optimal control designs for the vector-valued setting assuming that it results in an interim state, i.e. the result of the first decision maker action, that can be described by a continuous random variable which has a probability density function. More specifically, we provide a genie-aided outer bound that relies on our previous results for empirical coordination problems. This solution turns out to be not optimal in general, since it consists of a time-sharing strategy between two linear schemes of specific power. It follows that the optimal decision strategy for the original scalar Witsenhausen problem must lead to an interim state that cannot be described by a continuous random variable which has a probability density function.

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We study the problem of rate-distortion equivocation with side-information only available at the decoder when an independent private random key is shared between the sender and the receiver. The sender compresses the sequence, and the receiver reconstructs it such that the average distortion between the source and the output is limited. The equivocation is measured at an eavesdropper that intercepts the source encoded message, utilizing side-information correlated with the source and the side-information at the decoder. We have derived the entire achievable rate-distortion-equivocation region for this problem.

Most methods for publishing data with privacy guarantees introduce randomness into datasets which reduces the utility of the published data. In this paper, we study the privacy-utility tradeoff by taking maximal leakage as the privacy measure and the expected Hamming distortion as the utility measure. We study three different but related problems. First, we assume that the data-generating distribution (i.e., the prior) is known, and we find the optimal privacy mechanism that achieves the smallest distortion subject to a constraint on maximal leakage. Then, we assume that the prior belongs to some set of distributions, and we formulate a min-max problem for finding the smallest distortion achievable for the worst-case prior in the set, subject to a maximal leakage constraint. Lastly, we define a partial order on privacy mechanisms based on the largest distortion they generate. Our results show that when the prior distribution is known, the optimal privacy mechanism fully discloses symbols with the largest prior probabilities, and suppresses symbols with the smallest prior probabilities. Furthermore, we show that sets of priors that contain more uniform distributions lead to larger distortion, while privacy mechanisms that distribute the privacy budget more uniformly over the symbols create smaller worst-case distortion. A full version of this paper is accessible at: https://arxiv.org/pdf/2105.01033.pdf

In this work, we present a polar code design that offers a provably optimal solution for biometric identification systems allowing authentication under noisy enrollment with secrecy and privacy constraints. Binary symmetric memoryless source and channels are considered. It is shown that the proposed polar code design achieves the fundamental limits and satisfies more stringent secrecy constraints than previously in the literature. The proposed polar code design provides the first example of a code design that achieves the fundamental limits involving both identification and authentication.

There is a growing interest in analysing freshness of data in networked systems. Age of Information (AoI) has emerged as a relevant metric to quantify this freshness at a receiver, and minimizing this metric for different system models has received significant research attention. However, a fundamental question remains: what is the minimum achievable AoI in any single-server-single-source queuing system for a given service-time distribution? We address this question for the average peak AoI (PAoI) statistic by considering generate-at-will source model, service preemptions, and request delays. Our main result is on the characterization of the minimum achievable average PAoI, and we show that it is achieved by a fixed-threshold policy among the set of all causal policies. We use the characterization to provide necessary and sufficient condition for preemptions to be beneficial for a given service-time distribution. Our numerical results, obtained using well-known distributions, demonstrate that the heavier the tail of a distribution the higher the performance gains of using preemptions.

Machine learning models are known to memorize the unique properties of individual data points in a training set. This memorization capability can be exploited by several types of attacks to infer information about the training data, most notably, membership inference attacks. In this paper, we propose an approach based on information leakage for guaranteeing membership privacy. Specifically, we propose to use a conditional form of the notion of maximal leakage to quantify the information leaking about individual data entries in a dataset, i.e., the entrywise information leakage. We apply our privacy analysis to the Private Aggregation of Teacher Ensembles (PATE) framework for privacy-preserving classification of sensitive data and prove that the entrywise information leakage of its aggregation mechanism is Schur-concave when the injected noise has a log-concave probability density. The Schur-concavity of this leakage implies that increased consensus among teachers in labeling a query reduces its associated privacy cost. Finally, we derive upper bounds on the entrywise information leakage when the aggregation mechanism uses Laplace distributed noise.

We design optimal privacy-enhancing and cost-efficient energy management strategies for consumers that are equipped with a rechargeable energy storage. The Kullback-Leibler divergence rate is used as privacy measure and the expected cost-saving rate is used as utility measure. The corresponding energy management strategy is designed by optimizing a weighted sum of both privacy and cost measures over a finite time horizon, which is achieved by formulating our problem into a belief-state Markov decision process problem. A computationally efficient approximated Q-learning method is proposed as a generalization to high-dimensional problems over an infinite time horizon. At last, we explicitly characterize a stationary policy that achieves the steady belief state over an infinite time horizon, which greatly simplifies the design of the privacy-preserving energy management strategy. The performance of the practical design approaches are finally illustrated in numerical experiments.

In this paper, we study a stochastic disclosure control problem using information-theoretic methods. The useful data to be disclosed depend on private data that should be protected. Thus, we design a privacy mechanism to produce new data which maximizes the disclosed information about the useful data under a strong χ2-privacy criterion. For sufficiently small leakage, the privacy mechanism design problem can be geometrically studied in the space of probability distributions by a local approximation of the mutual information. By using methods from Euclidean information geometry, the original highly challenging optimization problem can be reduced to a problem of finding the principal right-singular vector of a matrix, which characterizes the optimal privacy mechanism. In two extensions we first consider a scenario where an adversary receives a noisy version of the user's message and then we look for a mechanism which finds U based on observing X, maximizing the mutual information between U and Y while satisfying the privacy criterion on U and Z under the Markov chain (Z, Y)-X-U.

In this paper, we study fundamental trade-offs in privacy-preserving biometric identification systems with noisy enrollment. The proposed identification systems include helper data, secret keys, and private keys. Helper data are stored in a public database and used for identification. Secret keys are either stored in a secure database or provided to the user, and can be used in a next step, e.g. for authentication. Private keys are provided by users, and are also used for identification. In this paper, we impose a noisy enrollment channel and an arbitrarily small privacy and secrecy leakage rate. We characterize the optimal trade-off among the identification, secret key, private key, and helper data rates. Depending on how secret keys are produced, we study two cases of the proposed privacy-preserving identification systems, where the secret keys are generated and chosen respectively. By introducing private keys, it is shown that the identification system achieves close to zero privacy leakage rate in both generated and chosen secret key settings. The results also show that the identification rate and the secret key rate can be enlarged by increasing the private key rate. This work provides a framework for analyzing privacy-preserving identification systems and an insight on the design of optimal systems.

We study hypothesis testing problems with fixed compression mappings and with user-dependent compression mappings to decide whether or not an observation sequence is related to one of the users in a database, which contains compressed versions of previously enrolled users' data. We first provide the optimal characterization of the exponent of the probability of the second type of error for the fixed compression mappings scenario when the number of users in the database grows exponentially. We then establish operational equivalence relations between the Wyner-Ahlswede-Körner network, the single-user hypothesis testing problem, the multi-user hypothesis testing problem with user-dependent compression mappings and the identification systems with user-dependent compression mappings. These equivalence relations imply the strong converse and exponentially strong converse for the multi-user hypothesis testing and the identification systems both with user-dependent compression mappings. Finally they also show how an identification scheme can be turned into a multi-user hypothesis testing scheme with an explicit transfer of rate and error probability conditions and vice versa.

High-dimensional identification systems consisting of two groups of users in the presence of statistical uncertainties are considered in this work. The task is to design enrollment mappings to compress users' information and an identification mapping that combines the stored information in the database and an observation to estimate the underlying user index. The compression-identification trade-off regions are established for the compound, extended compound, general and mixture settings. It is shown that several settings admit the same compression-identification trade-offs. We then study a connection between the Wyner-Ahlswede-Körner network and the identification setting. It indicates that a strong converse for the WAK network is equivalent to a strong converse for the identification setting. Finally, we present strong converse arguments for the discrete identification setting that are extensible to the Gaussian scenario.

We study the optimal control problem of the maximum a posteriori (MAP) state sequence detection of an adversary using smart meter data. The privacy leakage is measured using the Bayesian risk and the privacy-enhancing control is achieved in real-time using an energy storage system. The control strategy is designed to minimize the expected performance of a non-causal adversary at each time instant. With a discrete-state Markov model, we study two detection problems: when the adversary is unaware or aware of the control. We show that the adversary in the former case can be controlled optimally. In the latter case, where the optimal control problem is shown to be non-convex, we propose an adaptive-grid approximation algorithm to obtain a sub-optimal strategy with reduced complexity. Although this work focuses on privacy in smart meters, it can be generalized to other sensor networks.

There is a growing interest in analysing the freshness of data in networked systems. Age of Information (AoI) has emerged as a popular metric to quantify this freshness at a given destination. There has been a significant research effort in optimizing this metric in communication and networking systems under different settings. In contrast to previous works, we are interested in a fundamental question, what is the minimum achievable AoI in any single-server-single-source queuing system for a given service-time distribution? To address this question, we study a problem of optimizing AoI under service preemptions. Our main result is on the characterization of the minimum achievable average peak AoI (PAoI). We obtain this result by showing that a fixed-threshold policy is optimal in the set of all randomized-threshold causal policies. We use the characterization to provide necessary and sufficient conditions for the service-time distributions under which preemptions are beneficial.

We consider a three-node network, in which two agents wish to communicate over a noisy channel, while controlling the distribution observed by a third external agent. We use strong coordination to constrain the distribution, and we provide a complete characterization of the "remote strong coordination and reliable communication" region.

We study an information-theoretic privacy problem, where an agent observes useful data Y and wants to reveal the information to a user. Since the useful data is correlated with sensitive data X, the agent employs a privacy mechanism to produce data U that can be disclosed. Thus, we study the privacy mechanism design that maximizes the revealed information about Y while satisfying an ℓ1-privacy criterion under the Markov chain X-Y -U. When a sufficiently small leakage is allowed, we show that the optimizer of the design problem has a specific structure which allows us to use a local approximation of mutual information. More specifically, we show that the optimizer vectors are perturbations of fixed distributions. By using this approximation the original optimization problem can be reduced to a linear programming problem and an approximate solution for privacy mechanism design can be obtained.

This paper studies the optimal tradeoff between secrecy and non-secrecy rates of the MISO wiretap channels for different power constraint settings: sum power constraint only, per-antenna power constraints only, and joint sum and per-antenna power constraints. The problem is motivated by the fact that channel capacity and secrecy capacity are generally achieved by different transmit strategies. First, a necessary and sufficient condition to ensure a positive secrecy capacity is shown. The optimal tradeoff between secrecy rate and transmission rate is characterized by a weighted rate sum maximization problem. Since this problem is not necessarily convex, equivalent problem formulations are introduced to derive the optimal transmit strategies. Under sum power constraint only, a closed-form solution is provided. Under per-antenna power constraints, necessary conditions to find the optimal power allocation are derived. Sufficient conditions are provided for the special case of two transmit antennas. For the special case of aligned channels, the optimal transmit strategies can deduced from an equivalent point-to-point channel problem. Last, the theoretical results are illustrated by numerical simulations.

We study a two-stage identification problem with pre-processing to enable efficient data retrieval and reconstruction. In the enrollment phase, users' data are stored into the database in two layers. In the identification phase an observer obtains an observation, which originates from an unknown user in the enrolled database through a memoryless channel. The observation is sent for processing in two stages. In the first stage, the observation is pre-processed, and the result is then used in combination with the stored first layer information in the database to output a list of compatible users to the second stage. Then the second step uses the information of users contained in the list from both layers and the original observation sequence to return the exact user identity and a corresponding reconstruction sequence. The rate-distortion regions are characterized for both discrete and Gaussian scenarios. Specifically, for a fixed list size and distortion level, the compression-identification trade-off in the Gaussian scenario results in three different operating cases characterized by three auxiliary functions. While the choice of the auxiliary random variable for the first layer information is essentially unchanged when the identification rate is varied, the second one is selected based on the dominant function within those three. Due to the presence of a mixture of discrete and continuous random variables, the proof for the Gaussian case is highly non-trivial, which makes a careful measure theoretic analysis necessary. In addition, we study a connection of the previous setting to a two observer identification and a related problem with a lower bound for the list size, where the latter is motivated from privacy concerns.

In this paper we revisit the connections of the distributed hypothesis testing against independence (HT) problem with the Wyner-Ahlswede-Korner (WAK) problem and thë identification systems (ID). We show that the strong converse for the WAK problem is equivalent to the strong converse for the HT problem via constructive and nonconstructive transformations of codes. As another consequence of the transformation we provide a new exponentially strong converse equivalence statement. Applying the same idea, we prove a new result that the -identification capacity of the ID problem is equal to the maximum ε-exponent of type II of error in the HT problem when both side compression is allowed.

We consider the problem of synthesizing joint distributions of signals and actions over noisy channels in the finite length regime. For a fixed blocklength n and an upper bound on the distance ε, a coding scheme is proposed such that the induced joint distribution is ε-close in L1distance to a target i.i.d. distribution. The set of achievable target distributions and rate for asymptotic strong coordination can be recovered from the main result of this paper by having n that tends to infinity.

We introduce the concept of achievable cumulative rate distribution functions (CRDF) to characterize sequentially encoding processes that ensure a lossless or lossy reconstruction subject to an average distortion using a non-causal decoder. Utilizing tools from majorization theory, we derive necessary and sufficient conditions on the CRDF for a given IID source. It turns out that the optimal achievable distortion level can be adequately characterized by the concave-hull of the CRDF.

The vector-valued extension of the famous Witsenhausen counter-example setup is studied where the first decision maker (DM1) non-causally knows and encodes the iid state sequence and the second decision maker (DM2) causally estimates the interim state. The coding scheme is transferred from the finite alphabet coordination problem for which it is proved to be optimal. The extension to the Gaussian setup is based on a non-standard weak typicality approach and requires a careful average estimation error analysis since the interim state is estimated by the decoder. Next, we provide a choice of auxiliary random variables that outperforms any linear scheme. The optimal scheme remains unknown.

Privacy against an adversary (AD) that tries to detect the underlying privacy-sensitive data distribution is studied. The original data sequence is assumed to come from one of the two known distributions, and the privacy leakage is measured by the probability of error of the binary hypothesis test carried out by the AD. A management unit (MU) is allowed to manipulate the original data sequence in an online fashion while satisfying an average distortion constraint. The goal of the MU is to maximize the minimal type II probability of error subject to a constraint on the type I probability of error assuming an adversarial Neyman-Pearson test, or to maximize the minimal error probability assuming an adversarial Bayesian test. The asymptotic exponents of the maximum minimal type II probability of error and the maximum minimal error probability are shown to be characterized by a Kullback-Leibler divergence rate and a Chernoff information rate, respectively. Privacy performances of particular management policies, the memoryless hypothesis-aware policy and the hypothesis-unaware policy with memory, are compared. The proposed formulation can also model adversarial example generation with minimal data manipulation to fool classifiers. At last, the results are applied to a smart meter privacy problem, where the user's energy consumption is manipulated by adaptively using a renewable energy source in order to hide user's activity from the energy provider.

In this work we consider a two-stage identification problem with pre-processing where the users' data and observation are Gaussian distributed. In the first stage the processing unit returns a list of compatible users using the information from the first storage layer and the pre-processed observation. Then, the refined search is performed in the second stage where the processing unit returns the exact user's identity and a corresponding reconstruction sequence. We provide a complete rate-distortion trade-off for the Gaussian setting.

We study the high-dimensional identification systems under the presence of statistical uncertainties. The task is to design mappings for enrollment and identification purposes. The identification mapping compresses users' information then stores the index in the corresponding position in a database. The identification mapping combines the information in the database and the observation which originates randomly from an enrolled user to produce an estimate of the underlying user index. We study two scenarios. Users' data are generated from the same unknown distribution while the observation channel is also subjected to uncertainty. Each user's data are generated iid from the distribution corresponding to its own state, while the observation channel is known. We provide an achievable compression-identification trade-off for the first and second settings considering both discrete and continuous cases. In the discrete scenario, the described regions are also the correspondingly complete characterizations.

The trade-off of hypothesis tests on the correlated privacy hypothesis and utility hypothesis is studied. The error exponent of the Bayesian composite hypothesis test on the privacy or utility hypothesis can be characterized by the corresponding minimal Chernoff information rate. An optimal management protects the privacy by minimizing the error exponent of the privacy hypothesis test and meanwhile guarantees the utility hypothesis testing performance by satisfying a lower bound on the corresponding minimal Chernoff information rate. The asymptotic minimum error exponent of the privacy hypothesis test is shown to be characterized by the infimum of corresponding minimal Chernoff information rates subject to the utility guarantees.

We study a hypothesis testing problem to decide whether or not an observ!ation sequence is related to one of users in a database which contains compressed versions of users' data. Our main interest lies on the characterization of the exponent of the probability of the second kind of error when the number of users in the database grows exponentially. We show a lower bound on the error exponent and identify special cases where the bound is tight. Next, we study the ε-achievable error exponent and show a sub-region where the lower bound is tight.

Consider a hidden Markov model describing a system with two types of states: a monitored state and a private state. The two types of states are dependent and evolve jointly according to a Markov process with a stationary transition probability. It is desired to reveal the monitored states to a receiver but hide the private states. For this purpose, a privacy filter is necessary which suitably perturbs the monitored states before communication with the receiver. Our objective is to design the privacy filter to optimize the tradeoff between the monitoring accuracy and privacy, measured through a time-invariant distortion measure and Shannon's equivocation, respectively. As the optimal privacy filter is difficult to compute using the dynamic programming, we adopt a suboptimal greedy approach through which the privacy filter can be computed efficiently. Here, the greedy approach has the additional advantage of not being restricted to the finite time horizon setups. Simulations show the superiority of the approach compared to a privacy filter which only adds independent noise to the observations.

This paper studies optimal transmit strategies for multiple-input multiple-output (MIMO) Gaussian channels with joint sum and per-antenna power constraints. It is shown that if an unconstraint optimal allocation for an antenna exceeds a per-antenna power constraint, then the maximal power for this antenna is used in the constraint optimal transmit strategy. This observation is then used in an iterative algorithm to compute the optimal transmit strategy in closed-form. Finally, a numerical example is provided to illustrate the theoretical results.

Frequencies above 6 GHz are being considered by mobile communication industry for the deployment of future 5G networks. However in the higher carrier frequencies, especially the millimeter-wave frequencies (above 30 GHz), there can be severe degradations in the transmitted and received signals due to Phase Noise (PN) introduced by the local oscillators. In this paper, the effect of PN has been investigated for Orthogonal Frequency Division Multiplexing (OFDM), Offset QAM Filter-Bank Multi-Carrier (OQAM-FBMC) and QAM Filter-Bank Multi-Carrier (QAM-FBMC). The sources of degradation in these waveforms are quantified and closed-form expressions are derived for Signal-to-Interference Ratio (SIR). Evaluations are performed in terms of SIR and Symbol Error Rate (SER) for mm-wave frequencies using mmMAGIC PN model. The results reveal that OFDM outperforms OQAM-FBMC and QAM-FBMC and is a promising candidate for mm-wave communication.

Privacy-preserving energy management is studied in the presence of a renewable energy source. It is assumed that the energy demand/supply from the energy provider is tracked by a smart meter. The resulting privacy leakage is measured through the probabilities of error in a binary hypothesis test, which tries to detect the consumer behavior based on the meter readings. An optimal privacy-preserving energy management policy maximizes the minimal Type II probability of error subject to a constraint on the Type I probability of error. When the privacy-preserving energy management policy is based on all the available information of energy demands, energy supplies, and hypothesis, the asymptotic exponential decay rate of the maximum minimal Type II probability of error is characterized by a divergence rate expression. Two special privacy-preserving energy management policies, the memoryless hypothesis-aware policy and the hypothesis-unaware policy with memory, are then considered and their performances are compared. Further, it is shown that the energy supply alphabet can be constrained to the energy demand alphabet without loss of optimality for the evaluation of a single-letter-divergence privacy-preserving guarantee.

We study a two-stage identification problem with pre-processing to enable efficient data retrieval and reconstruction. The first stage outputs a list of compatible users to the second stage which uses it to return the exact user identity with a corresponding reconstruction sequence. The rate-distortion region is characterized. A connection to a two observer identification problem is also studied.

This paper investigates the capacity limit of an uplink waveform channel assuming imperfect channel state information at the receiver (CSIR). Various realistic assumptions are incorporated into the problem, which make the study valuable for performance assessment of real cellular networks to identify potentials for performance improvements in practical receiver designs. We assume that the continuous-time received signal is first discretized by mismatched filtering based on the imperfect CSIR. The resulting discrete-time signals are then decoded considering two different decoding strategies, i.e., an optimal decoding strategy based on specific statistics of channel estimation errors and a sub-optimal decoding strategy treating the estimation error signal as additive Gaussian noise. Motivated by the proposed decoding strategies, we study the performance of the decision feedback equalizer for finite constellation inputs, in which inter-stream interferences are treated either using their true statistics or as Gaussian noise. Numerical results are provided to exemplify the benefit of exploiting the knowledge on the statistics of the channel estimation errors and inter-stream interferences. Simulations also assess the effect of the CSI imperfectness on the achievable rate, which reveal that finite constellation inputs are less sensitive to the estimation accuracy than Gaussian input, especially in the high SNR regime.

In this paper, an energy flow control strategy to reduce the smart meter privacy leakage is studied. The considered smart grid is equipped with an energy storage device. The privacy leakage is modeled as optimal Bayesian detections on the behaviors of the consumer made by an authorized adversary. To evaluate the privacy risk, a Bayesian detection-operational privacy leakage metric is proposed. The design of an optimal privacy-preserving energy control strategy can be formulated as a belief state MDP problem. Therefore, standard methods and algorithms can be utilized to obtain or to approximate the optimal control strategy. A simplified problem to design an instantaneous optimal privacy-preserving control strategy is also considered. It is shown that the problem of the instantaneous optimal control strategy design can be formulated as a set of linear programmings.

In this paper, we consider a discrete time linear quadratic Gaussian (LQG) control problem in which state information of the plant is encoded in a variable-length binary codeword at every time step, and a control input is determined based on the codewords generated in the past. We derive a lower bound of the rate achievable by the class of prefix-free codes attaining the required LQG control performance. This lower bound coincides with the infimum of a certain directed information expression, and is computable by semidefinite programming (SDP). Based on a technique by Silva et al., we also provide an upper bound of the best achievable rate by constructing a controller equipped with a uniform quantizer with subtractive dither and Shannon-Fano coding. The gap between the obtained lower and upper bounds is less than 0:754r + 1 bits per time step regardless of the required LQG control performance, where r is the rank of a signal-to-noise ratio matrix obtained by SDP, which is no greater than the dimension of the state.

The zero-error secrecy capacity of uncertain wiretap channels is defined. If the sensor-estimator channel is perfect, it is also calculated. Further properties are discussed. The problem of estimating a dynamical system with nonstochastic disturbances is studied where the sensor is connected to the estimator and an eavesdropper via an uncertain wiretap channel. The estimator should obtain a uniformly bounded estimation error whereas the eavesdropper's error should tend to infinity. It is proved that the system can be estimated securely if the zero-error capacity of the sensor-estimator channel is strictly larger than the logarithm of the system's unstable pole and the zero-error secrecy capacity of the uncertain wiretap channel is positive.

We consider secure multi-terminal source coding problems in the presence of a public helper. Two main scenarios are studied: 1) source coding with a helper where the coded side information from the helper is eavesdropped by an external eavesdropper, 2) triangular source coding with a helper where the helper is considered as a public terminal. We are interested in how the helper can support the source transmission subject to a constraint on the amount of information leaked due to its public nature. We characterize the tradeoff between transmission rate, incurred distortion, and information leakage rate at the helper/eavesdropper in the form of a rate-distortion-leakage region for various classes of problems.

We consider multiple-input single-output (MISO) Gaussian channels with joint sum and per-antenna power constraints. A closed-form solution of the optimal beamforming vector is derived which achieves the maximal transmission rate. The result shows that if the sum power constraint only optimal power allocation violates a per-antenna power constraint then the joint power constraint optimal power allocation is at the intersection of the sum power constraint and the per-antenna power constraints.

This paper considers the capacity limit of an uplink wideband CDMA (WCDMA) system assuming imperfect channel state information at the receiver (CSIR). In order to make the studied results useful for the performance assessment of real cellular networks, various realistic assumptions are included in the problem. A discrete-time channel model is derived based on the mismatched filtering at the receiver. Capacity inner bounds are then characterized based on the discrete-time channel model considering different assumptions on decoding strategy. Numerical results are also provided to show the effect of imperfect CSIR on the capacity.

Two classes of source/channel coding problems, namely, coding with action-dependent side information and coding with additional signal reconstruction are considered in a unified fashion. In the source coding setting, a decoder wishes to reconstruct the source subject to a distortion constraint, while an encoder is required to estimate the decoder's reconstruction reliably. Side information is action-dependent in the sense that its quality and/or availability at the encoder or decoder can be influenced by a cost-constrained action sequence. In the channel coding dual, the decoder wishes to decode both the message and the channel input sequence reliably, and the channel state information available at the encoder or decoder is assumed to depend on the action sequence. We consider discrete memoryless systems and characterize single letter expressions for the rate-distortion-cost function and channel capacity for the respective source and channel coding problems.

We consider the problems of secure lossy source coding with side information in the presence of a passive eavesdropper who has access to the source description. The encoder wishes to compress the source sequence in order to satisfy a distortion criterion at the decoder, while revealing only limited knowledge about the source to the eavesdropper. The side information available to the encoder, the legitimate decoder, or the eavesdropper can be influenced by a cost-constrained action sequence. Three different settings are studied. In the first two settings, we are interested in understanding the influence of the action sequence on the rate-distortion-leakage tradeoff where the action is taken either by the decoder or by the encoder to influence side information at the decoder and eavesdropper. Next, we consider a setting where common action-dependent side information is available securely to both encoder and decoder, and thus can be used for secret key generation. We characterize the optimal rate-distortion-cost-leakage region or the corresponding inner bounds for a discrete memoryless source for above settings. The results are useful in characterizing fundamental limits for example in secure sensor networking and future cyber physical systems.

In this paper, the privacy problem of a tandem distributed detection system vulnerable to an eavesdropper is proposed and studied in the Bayesian formulation. The privacy risk is evaluated by the detection cost of the eavesdropper which is assumed to be informed and greedy. For the sensors whose operations are constrained to suppress the privacy risk, it is shown that the optimal detection strategies are likelihood-ratio tests. This fundamental insight allows for the optimization to reuse known algorithms extended to incorporate the privacy constraint. The trade-off between the detection performance and privacy risk is illustrated in an example.

In this paper, the privacy problem of a parallel distributed detection system vulnerable to an eavesdropper is proposed and studied in the Bayesian formulation. The privacy risk is evaluated by the detection cost of the eavesdropper which is assumed to be informed and greedy. It is shown that the optimal detection strategy of the sensor whose decision is eavesdropped on is a likelihood-ratio test. This fundamental insight allows for the optimization to reuse known algorithms extended to incorporate the privacy constraint. The trade-off between the detection performance and privacy risk is illustrated in a numerical example. The incorporation of physical layer privacy in the system design will lead to trustworthy sensor networks in future.

We consider the problem of lossy source coding with side information under a privacy constraint that the reconstruction sequence at a decoder should be kept secret to a certain extent from another terminal such as an eavesdropper, a sender, or a helper. We are interested in how the reconstruction privacy constraint at a particular terminal affects the rate-distortion tradeoff. In this work, we allow the decoder to use a random mapping, and give inner and outer bounds to the rate-distortion-equivocation region for different cases. In the case where each reconstruction symbol depends only on the source description and current side information symbol, the complete rate-distortion-equivocation region is provided. A binary example illustrating a new tradeoff due to the new privacy constraint, and a gain from the use of randomized decoder is given.

In this paper, we investigate the problem of successive refinement with side information (SI) under secrecy constraint. In particular, under classical successive refinement coding scheme, there are degraded SI sequences Ynand Znat two decoders and Enat the eavesdropper. Based on the status of two switches, three different cases are investigated. In case 1 and 3, the eavesdropper only observes output of encoder 1 and 2, respectively, while in case 2, the eavesdropper observes outputs of both encoder 1 and 2. The Markov chain X - Y - (Z, E) holds in all cases. The equivocation is measured by the normalized entropy of source sequence conditioned on the observation of eavesdropper. We completely characterize the rate-distortion-equivocation regions for all three cases, and show that layered coding is optimal. Finally, a binary source example is given.

This paper considers the capacity limit of an uplink wideband CDMA system. In order to make the studied results useful for the performance assessment of real cellular networks, various realistic assumptions are included in the problem. An equivalent discrete-time channel model is derived based on sufficient statistic for optimal decoding of transmit messages. The capacity regions are then characterized considering finite constellation and Gaussian input assumptions. For further insight, an analysis on the asymptotic capacity is considered, in which the conditions to simultaneously achieve the individual capacities are derived.

This paper studies and derives new results for the interference channel with a relay (ICR). Three inner bounds for the discrete memoryless ICR are proposed, based on three coding strategies that employ layered code at the relay. The first scheme is inspired by layered noisy network coding, proposed by Lim et al. for the two-way relay channel, the second and the third schemes rely on simpler encoding and decoding processes, dubbed layered quantize-forward. Performance of the proposed schemes is investigated for two classes of channels with Gaussian noise: the interference channel with in-band relay reception/out-of-band relay transmission and the interference with in-band relay reception/in-band relay transmission. For the former class of channels, it is shown that the first proposed scheme achieves the same inner bound as the generalized hash-forward scheme with incremental binning. In addition, the inner bound is within 0.5 bit of the capacity region under certain conditions on the channel parameters. For the latter class of channels, new upper bounds on sum-rate are established by extending known upper bounds for symmetric channels. The first inner bound is shown to be within 0.5 bit of the capacity region if the relay's power exceeds a certain threshold, which depends on channel parameters. Numerical examples show that the proposed schemes can achieve significantly higher sum-rates when compared with other compress-forward schemes. Analysis also reveals a tradeoff between achievable rates, coding delay, and complexity of the proposed schemes. Results in this paper provide a better understanding of coding for the ICR, in particular, they show that layered coding is a beneficial element in multiuser networks with relays.

A computable expression for Heegard and Berger's rate-distortion function has eluded information theory for nearly three decades. Heegard and Berger's single-letter achievability bound is well known to be optimal for physically degraded side information; however, it is not known whether the bound is optimal for arbitrarily correlated side information (general discrete memoryless sources). In this paper, we consider a new setup where the side information at one receiver is conditionally less noisy than that at the other. The new setup includes degraded side information as a special case, and it is motivated by the literature on degraded and less noisy broadcast channels. Our key contribution is a converse proving the optimality of Heegard and Berger's achievability bound in a new setting, where the side information is conditionally less noisy and one distortion function is deterministic. The less noisy setup is also generalized to two different successive-refinement problems.

We consider secure multi-terminal source coding problems in the presence of a public helper. Two main scenarios are studied: 1) source coding with a helper where the coded side information from the helper is eavesdropped by an external eavesdropper, 2) triangular source coding with a helper where the helper is considered as a public terminal. We are interested in how the helper can support the source transmission subject to a constraint on the amount of information leaked due to its public nature. We characterize the tradeoff between transmission rate, incurred distortion, and information leakage rate at the helper/eavesdropper in the form of a rate-distortion-leakage region for various classes of problems.

We consider the successive refinement of information problem with decoder side information. The rate-distortion region is unknown in general; Steinberg & Merhav and Tian & Diggavi solved it in the special case of degraded side information. We extend this special case to a new setup, conditionally less noisy side information, and we give a single-letter solution when one distortion function is deterministic.

This paper studies a new model for cooperative communication, the interfering relay channels. We show that the hash-forward scheme introduced by Kim for the primitive relay channel is capacity achieving for a class of semideterministic interfering relay channels. The obtained capacity result generalizes and unifies earlier capacity results for a class of primitive relay channels and a class of deterministic interference channels.

The integration of multiple services such as the transmission of private, common, and confidential messages at the physical layer is becoming important for future wireless networks in order to increase spectral efficiency. In this paper, bidirectional relay networks are considered, in which a relay node establishes bidirectional communication between two other nodes using a decode-and-forward protocol. In the broadcast phase, the relay transmits additional common and confidential messages, which then requires the study of the bidirectional broadcast channel (BBC) with common and confidential messages. This channel generalizes the broadcast channel with receiver side information considered by Kramer and Shamai. Low complexity polar codes are constructed that achieve the capacity region of both the degraded symmetric BBC, and the BBC with common and confidential messages. The use of polar codes allows an intuitive interpretation of how to incorporate receiver side information and secrecy constraints as different sets of frozen bits at the different receivers for an optimal code design. In order to show that the constructed codes achieve capacity, a tighter bound on the cardinality of an auxiliary random variable used in the converse is found using a method by Salehi.

This paper studies a model for communications in wireless networks supported by designated cooperation links. In particular, a 2-user Gaussian one-sided interference channel with two rate-limited and orthogonal communication links between the receivers is considered. A communication protocol for the channel is proposed, which combines rate-splitting and superposition encoding techniques with the conventional decode-forward and compress-forward strategies. It is shown that a careful design of codebooks and coding scheme, which is obtained from intuition based on superposition coding, can greatly reduce the complexity of the strategy. Analytical and numerical results show that the proposed scheme, although not universally optimal, can achieve the capacity region or sum capacity exactly or asymptotically in certain scenarios. Various limits of sum capacity gain due to cooperation are also discussed.

In this work, coding for a discrete memoryless broadcast channel with random states and two receivers is studied. Each receiver knows one of the two information messages at the sender and wants to know the other one. Assuming the channel state sequence is noncausally known at the sender, an achievable rate region based on the Gel'fand–Pinsker coding strategy is derived and an outer bound to the capacity region is presented. Further, the capacity region for the special case where in addition one receiver knows the channel state is established. An equivalent characterization of an achievable rate region characterizing convex set is derived using Shannon's concept of transmit strategies. This characterization is used to derive an Arimoto–Blahut-like algorithm including a stopping criterion to compute the weighted rate-sum maxima, which can be used to characterize the whole achievable rate region. The tradeoff between the input distribution and the impact of the channel state, the necessity of the time-sharing operation, and the additive Gaussian channel case assuming Costa's choice of auxiliary random variables are discussed by examples.

This paper proposes two new coding schemes for the discrete memoryless two-way relay channel. The main target is to show the benefits of compress-forward without Wyner-Ziv binning and of layered relaying in networks wherein a relay is to help multiple destinations, that may have unequal channel quality and/or have access to different side information. Numerical results for a Gaussian channel show that the new coding schemes outperform variants of compress-forward relaying and offer a good trade-off between achievable rates and complexity and decoding delay. The idea can also be applied to other relay networks.

We consider a problem of constrained multi-stage coding for channels with a rewrite option. It is a natural extension of Weissman's channels with action-dependent states to the multistage coding case where an encoder in each stage observes its own message as well as all previous-stage messages, inputs, and outputs. In addition to decoding all messages at the final stage, the new reconstruction constraint introduced in Sumszyk and Steinberg's information embedding with reversible stegotext is imposed on the problem such that the decoder is required to be able to reconstruct all channel input sequences reliably. The complete characterization of the channel capacity region is given for the two-stage case, while the inner and outer bounds to the capacity regions for the cases of three or more stages are provided. For the two-stage case, a discussion regarding the rate constraint of the message in the second stage is also given in which we can draw a connection to the two-stage coding condition which appears in our previous study on channel with action-dependent state and reversible input.

We consider the capacity region of a three receiver broadcast channel with some message cognition at two receivers. The problem generalizes the bi-directional broadcast channel to include a third receiver, a common message, and (partial) message cognition. We characterize the capacity region for several classes of less noisy, more capable, and deterministic broadcast channels.

We consider a lossless multi-terminal source coding problem with one transmitter, two receivers and side information. The achievable rate region of the problem is not well understood. In this paper, we characterise the rate region when the side information at one receiver is conditionally less noisy than the side information at the other, given this other receiver's desired source. The conditionally less noisy definition includes degraded side information and a common message as special cases, and it is motivated by the concept of less noisy broadcast channels. The key contribution of the paper is a new converse theorem employing a telescoping identity and the Csiszár sum identity.

We consider a problem of coding for channels with action-dependent states available noncausally to the encoder where the decoder is additionally required to be able to decode the channel input reliably. Lower and upper bounds on the channel capacity are derived. It is shown that the capacity is determined if there exists a maximizing joint probability distribution in the upper bound which satisfies the two-stage coding condition, and it, in turn, reveals the formula duality between this problem and that of source coding with common reconstruction and action-dependent side information. We also state two simple coding schemes and the corresponding achievable rates for the cases where the two-stage coding condition is not fulfilled.

We consider a secure lossy source coding problem with the presence of an eavesdropper who has access to the source description. An encoder wants to compress the source in such a way that the intended decoder can reconstruct the source sequence and satisfy a distortion criterion, while revealing only limited knowledge about the source to the eavesdropper. In our system an action sequence is generated based on the source description with some costs to influence the side information available to the legitimate decoder and the eavesdropper. We provide a complete characterization of the rate-distortion-cost-equivocation region for a discrete source with correlated action-dependent side information at the decoders. The result serves as a fundamental limit for example in secure sensor networking.

We present a new achievable rate region for the discrete memoryless Z channel (DM-ZC) using Marton coding with rate splitting. The region is shown to include previously known achievable rate regions. Secondly we study a class of degraded Z channels, the bijective degraded Z channel (BDZC). An outer bound for the BDZC is proved, which is shown to meet the inner bound for the deterministic settings. For the Gaussian Z channel with weak crossover link, we show that if Gaussian inputs are optimal then a coding scheme based on Marton coding without rate splitting achieves to within half a bit per real dimension from the boundary of the capacity region.

In this work, the MIMO Gaussian bidirectional broadcast channel (BBC) with common messages is studied. The problem is motivated by the concept of bidirectional relaying in a three-node network, where a half-duplex relay node establishes a bidirectional communication between two other nodes using a decode-and-forward protocol and thereby adds an own multicast message to the communication. The capacity region of the broadcast phase is derived and the corresponding transmit covariance matrix optimization problem is analyzed in detail. Thereby, it is shown that the transmit covariance optimization problem is strongly connected to the corresponding one of the MIMO Gaussian BBC without common messages. In particular, this knowledge can be exploited to transfer results such as optimal transmit strategies from one scenario to the other one.

In this work, we study the MIMO Gaussian bidirectional broadcast channel (BBC) with common message and characterize the capacity region. Moreover, we show that the transmit covariance matrix optimization problem has the same structure as the corresponding optimization problem of the BBC without common message which leads to the comfortable position to transfer results from one scenario to the other. This problem is motivated by the concept of bidirectional relaying in a three-node network, where a half-duplex relay node establishes a bidirectional communication between two other nodes and thereby adds an own multicast message to the communication.

We derive achievable rate regions for a 2-user Gaussian Z-interference channel with conferencing decoders. We identify different cases where the rate-limitedness of the conference link from the interference-free receiver to the interfered receiver affects the conferencing strategy as well as the achievable rate region. Furthermore, an outer bound to the capacity region based on cut-set and genie-aided bounds is presented.

In this work we provide an achievable rate region for the cellular downlink with three users where two users want to communicate with each other. Due to the side information from the prior uplink, gains are achievable by combining bidirectional and classical broadcast channel coding strategies. A coding theorem for the bidirectional broadcast channel with random state non-causally known at the encoder is generalized to continuous alphabets and applied to Gaussian channels with an average power constraint. The single-user capacities are achievable if one decoder additionally knows the channel state. Accordingly, we see that a more comprehensive view on the information flow in a multi-user network can lead to a larger achievable rate region.

We consider a source coding problem where the encoder can take actions that influence the availability and/or quality of the side information which is available partially and noncausally at the encoder and the decoder. We then characterize the associated achievable tradeoffs between rate, distortion, and cost. In addition, we state and discuss a capacity result for the channel coding dual problem where the formula duality of special cases is recognized.

This paper considers a discrete memoryless four-node network where two nodes want to send three independent messages to the other two nodes. The two receiving nodes are allowed to cooperate by means of a unidirectional noiseless link with finite capacity. A coding scheme is proposed which combines rate splitting, block Markov multi-level superposition coding with binning and joint decoding. The general achievable rates are then specialized to degraded channel and Gaussian channel, where it is shown that the sum capacity for the Gaussian channel is achieved under certain conditions. Results in this paper recover and unify previously known results for the discrete memoryless Z channel without cooperation, and results for the Gaussian Z-interference channel with a digital relay link.

We determine the rate region of a source coding problem with common reconstruction and action-dependent side information where an action sequence is taken by an encoder over a rate-limited link. We show that the rate region depends only on the sum-rate and the sum-rate distortion and cost function is characterized. The result serves as a fundamental limit in transmission scenarios where the encoder wants to control and monitor the quality of the decoder's reconstruction via the respective uses of action sequences and a common reconstruction constraint.

Bidirectional relaying is a promising approach to improve the performance in wireless networks such as sensor, ad-hoc, and even cellular systems. Bidirectional relaying applies to three-node networks, where a relay establishes a bidirectional communication between two other nodes using a decode-and-forward protocol. First, the two nodes transmit their messages to the relay which decodes them. Then, the relay broadcasts a reencoded message in such a way that both nodes can decode their intended message using their own message as side information. We consider uncertainty in the channel state information (CSI) and assume that all nodes only know that the channel over which the transmission takes place is from a pre-specified set of channels. In this work, we concentrate on the second phase, which is called the compound bidirectional broadcast channel. We present a robust coding strategy which enables reliable communication under channel uncertainty and show that this strategy actually achieves the compound capacity. Further, we analyze scenarios where either the receivers or the transmitter have perfect CSI. We show that CSI at the receivers does not affect the maximal achievable rates, while CSI at the transmitter improves the capacity region. A numerical example and a game-theoretic interpretation complete this work.

In this work the transmit covariance matrix optimization problem for the MIMO Gaussian bidirectional broadcast channel is studied. A half-duplex relay node establishes bidirectional communication between two nodes using a decode-and-forward protocol. In the initial multiple access phase both nodes transmit their messages to the relay node. In the succeeding phase the relay broadcasts an optimal re-encoded message so that both nodes can decode the other's message using their own message as side information. We show that if the channels are orthogonal then there exist equivalent transmit strategies with different ranks. The study of special cases reveals some of the difficult structure of the optimal solution. In particular a closed form procedure for the case of full rank transmission for invertible channels is derived. Moreover, for parallel channels the optimal solution is completely characterized and discussed.

We consider the broadcast phase of a spectrally efficient two-phase decode-and-forward protocol which is used by a relay node to establish a bidirectional communication between two nodes. In the first phase the two nodes transmit their message to the relay node which decodes the messages. In the succeeding phase the relay node broadcasts a re-encoded composition of them. We consider imperfect channel knowledge and assume that all nodes merely know that the channel used for the transmission belongs to a set of channels. This is called compound bidirectional broadcast channel. We derive a universal strategy which achieves capacity and show that perfect channel state information (CSI) at the receivers does not lead to an increased capacity region. Otherwise, perfect CSI at the transmitter can advantageously be used to enlarge the capacity region. Finally, we give a game-theoretic interpretation as a game against nature.

In this work, coding for a discrete memoryless broadcast channel with random states and two receivers is studied. Each receiver knows one of the two information sources at the sender and wants to know the other one. Since it is assumed that the sender knows the channel state sequence non-causally, an achievable rate region using Gel'fand-Pinsker-coding is derived. Further, a simple outer bound to the capacity region as well as convexity and cardinality properties regarding the input probability distributions are discussed. The problem is motivated by the application of bidirectional communication between two terminals in a cellular system.

In this work the transmit covariance matrix optimization problem for the discrete memoryless MIMO Gaussian bidirectional broadcast channel is studied. A half-duplex relay node establishes bidirectional communication between two nodes using a decode-and-forward protocol. In the initial multiple access phase both nodes transmit their messages to the relay node. In the succeeding phase the relay broadcasts an optimal re-encoded message so that both nodes can decode the other's message using their own message as side information. The capacity region of the bidirectional broadcast channel is completely characterized by a weighted rate sum maximization problem, which can be solved by a simple iterative fixed point algorithm. If an efficient transmit covariance matrix is invariant with respect to the joint subspace spanned by the channels, then different combinations of the part transmitted on the orthogonal subspaces result in equivalent transmit strategies with different ranks. A closed-form procedure to obtain the optimal transmit covariance is derived for the case where the rank of the channels is equal to the number of antennas at the relay node and a full-rank transmission is optimal. It shows the complicated structure of the optimal eigenspace, which depends on the weights and the mean transmit power constraint. For parallel channels the optimal solution is completely characterized and discussed, which also solves the optimal power allocation problem for a single-antenna OFDM system.

In this paper, we prove a new achievable rate region for the two-phase two-way relay channel under half-duplex constraints. The points in the region are achieved by a combination of a compress-and-forward strategy at the relay node (potentially including partial decoding) with a kind of joint decoding at the receivers. For some channels, the achievable rate region presented in this paper is shown to be a proper superset of a rate region proven in some recent papers where a separate decoding was assumed.

We consider the broadcast phase of a three-node network, where a relay node establishes a bidirectional communication between two nodes using a spectrally efficient two-phase decode-and-forward protocol. In the first phase the two nodes transmit their messages to the relay node. Then the relay node decodes the messages and broadcasts a re-encoded composition of them in the second phase. We consider Gaussian MIMO channels and determine the capacity region for the second phase which we call the Gaussian MIMO bidirectional broadcast channel.

In this paper, we state a new achievable rate region for the two-phase two-way relay channel with a half-duplex relay node. The new region is obtained using a partial-decode-and-forward protocol, which is a superposition of both, decode-and-forward and compress-and-forward. It contains the achievable rate regions of [1] and [2] as special cases.

We consider a three-node network, where a relay node establishes a bidirectional communication between two nodes using a two-phase decode-and-forward protocol. In the first phase the two nodes transmit their messages to the relay node which decodes them. Then the relay broadcasts the information to both nodes in the succeeding phase which we call the bidirectional broadcast channel. In this work we first consider both phases separately and compare existing strategies especially for the second phase. We determine the capacity region of the bidirectional broadcast channel and discuss the impact of multiple antennas and the correlation between the channels on the capacity region. We show how the available spectral resources can be shared between the two phases and compare achievable rate regions for the whole bidirectional communication. Finally, we indicate how the bidirectional relay communication and the knowledge about the corresponding rate region can be advantageously used in cellular systems or cross-layer designs.

In this work, we study a cross-layer design of a spectrally efficient bidirectional relay communication in a three- node network using superposition encoding at the relay node. On the physical layer, a half-duplex relay node decodes-and- forwards the messages of two nodes in a two-phase protocol with optimal time-division. On the data link layer, we assume ergodic arrival processes at node 1 and 2 which have queues with infinite buffer length. At the beginning of each time-slot a centralized controller chooses the service rate pair which achieves the weighted rate sum maximum of the instantaneous achievable rate region for the block-fading channel state of the next time-slot with weights equal to the current buffer levels. To this end, the controller adjusts the time-division and relay power distribution. The policy is throughput optimal since the stability region is equal to the bidirectional ergodic rate region. This is because whenever the mean queue length is large, a negative drift of a quadratic Lyapunov function on the buffer levels can be proved.

In a three-node network bidirectional communication between two nodes can be enabled by a half-duplex relay node with a decode-and-forward protocol. In the first phase, the messages of two nodes are transmitted to the relay node. In the second phase a re-encoded composition is broadcasted by the relay node. In this work the capacity region of the broadcast phase in terms of the maximal probability of error is determined. It is characterized by the mutual informations of the separate channels coupled by the common input.

Recently, a quasi-orthogonal space-time block code (QSTBC) capable of achieving a significant fraction of the outage mutual information of a multiple-input-multiple-output (MIMO) wireless communication system for the case of nT= 4 transmit and nR = 1 receive antennas was proposed. We generalize these results to nT= 2ntransmit and an arbitrary number of receive antennas. Furthermore, we completely characterize the structure of the equivalent channel for the general case and show that for all nT= 2nand nRthe eigenvectors of the equivalent channel are fixed and independent from the channel realization. Furthermore, the eigenvalues of the equivalent channel are independent identically distributed random variables each following a noncentral chi-square distribution with 4nRdegrees of freedom. Based on these important insights into the structure of the QSTBC, we derive tight lower and upper bounds for the outage probability achieved with QSTBC. Finally, by utilizing the special structure of the QSTBC, we propose a new transmit strategy, which decouples the signals transmitted from different antennas in order to detect the symbols separately with a linear ML-detector rather than joint detection, an up to now only known advantage of orthogonal space-time block codes (OSTBC).

We consider the problem of relay selection in a network with N relay nodes. A half-duplex relay node enables bidirectional communication between two nodes with a spectrally efficient two-phase protocol. In the first phase both nodes transmit their messages to a relay node, which decodes the messages and broadcasts a composition using superposition encoding in the succeeding phase. The probability that the achievable rate region of one relay node contains all other rate regions decreases with the number of relay nodes N. Therefore, we propose a relay selection criterion that decides according to the weighted rate sum for any bidirectional rate pair on the boundary of the achievable rate region individually. If we allow time-sharing between the usage of different relay nodes, we can enlarge the achievable rate region. In an iid Rayleigh fading scenario relay selection realizes multi-user diversity so that the sum-rate of any rate pair on the boundary of the ergodic rate region asymptotically grows with Theta(log(log(/V))).

In this work we consider the achievable rates of a joint resource allocation for a three-node network where a half-duplex relay node enables bidirectional communication between nodes 1 and 2 and thereby adds an own multicast message to the communication. In the multiple access phase nodes 1 and 2 transmit their message to the relay node, which decodes the messages and forwards them in the succeeding broadcast phase. Therefore, the relay node encodes the multicast and bidirectional messages using the superposition encoding strategy. We do not allow cooperation between the encoders of nodes 1 and 2, but since both nodes know a priori its own bidirectional message, both nodes can cancel the interference caused by their own message before decoding the unknown messages. It shows that for both nodes it is always optimal to decode the relay message first. Furthermore, the total sum-rate maximum is determined by the sum-rate optimum of the bidirectional broadcast phase. From the closed form solutions of the combinatorial problems we can characterize the bidirectional rate pairs where the total sum-rate remains constant. In the end the obtained results are discussed and illustrated by means of some working examples. The joint resource allocation improves the overall spectral efficiency and enables new trade-offs between the routing tasks.

We study the transmit strategies in a MIMO bidirectional relaying scenario with individual power constraints. In two phases a half-duplex relay node decodes-and-forwards the signals of two nodes. Each node has multiple antennas. We deduce the transmit strategy in the first phase from the general Gaussian MIMO-MAC. Since each node knows a priori the interference of its own message in the second phase interference-free reception is achieved. Therefore, the optimal relay transmit strategy is given by two point-to-point water-filling solutions which are coupled by the relay power distribution. In the large SNR, the sum of any bidirectional rate tuple on the boundary of the rate region is asymptotically proportional with the minimum spatial degree of both MIMO channels.

In our previous work we determined the weak capacity region for the broadcast phase of two-phase bidirectional relay channel. It turned out that the set of achievable rates obtained by optimizing over the two communication phases exceeds that obtained by using the network coding principle, i.e. by applying XOR to the decoded messages. In this paper we supplement our result by a proof of the strong converse with respect to the maximum error probability to the coding theorem for the broadcast phase. This result implies that the capacity region of that phase remains constant for a certain range of values of average error parameters [epsiv1,epsiv2].

In this work, the concept of linear relay network code [1] for an arbitrary number of relay nodes is extended to scalar frequency selective channels. Furthermore, we present performance criteria derived from the pairwise error probability (PEP) of a maximum likelihood sequence estimator (MLSE). By neglecting the transient processes of finite frames, deeper insights into the performance of FIR linear relay networks can be gained. Finally, the performance of a linear relay network using an MLSE sphere decoder at the destination is illustrated by some Monte Carlo simulations.

Diversity combining schemes are successfully applied in communication systems. In many recent publications the performance of different diversity combining schemes in different fading scenarios and with different modulation schemes was analyzed and analytical expressions for the performance derived. It turned out that the performance depends on the statistics of the fading, especially on the correlation of the diversity branches. In this work, we study the impact of correlation on the performance of the maximum ratio, equal gain, and selection combining schemes without computing directly the error rate. The performance depends only on the eigenvalues, i.e. powers, of the correlation matrix and not on the eigenvectors. Therefore, majorization theory can be used as a measure of the correlation. We show that the performance of maximum ratio combining and equal gain combining are Schur-convex functions with respect to the correlation eigenvalues, i.e. correlation increases the error rate. Surprisingly, the behavior of selection combining depends on the SNR: for small SNR, correlation decreases the error rate, whereas for high SNR, correlation increases the error rate. Finally, we illustrate the theoretical results by numerical simulations.

Recently, a quasi-orthogonal space-time block code (QSTBC) capable of achieving a significant fraction of the outage mutual information of a multiple-input-multiple output (MIMO) wireless communication system for the case of four transmit and one receive antennas was proposed. We generalize these results to 2/sup n/ transmit and an arbitrary number of receive antennas. Furthermore, we derive an analytical lower bound for the fraction of outage probability achieved with QSTBC and show that this bound is tight for low signal-to-noise-ratios (SNR) values and also for increasing number of receive antennas. We present also an upper bound, which is tight for high SNR values and derive analytical expressions for the case of four transmit antennas. Furthermore, by utilizing the special structure of the QSTBC we propose a new transmit strategy, which decouples the signals transmitted from different antennas in order to detect the symbols separately with a linear ML-detector rather than joint detection, an up to now only known advantage of orthogonal space-time codes (OSTBC).

In our work we study a cooperative system using multiplexing. The key idea of a cooperative system is, that spatially separated stations assist each other to build up a distributed smart antenna and thereby to improve the efficiency of their transmissions. Here, the multiple antenna link is exploited by multiplexing. In order to evaluate the multiple antenna link, we introduce a system model considering one cooperative station. From this, we present the capacity and some properties of a system which differs from ordinary MIMO systems. Furthermore we derive the optimal amplification factors, which specify the optimal power allocation. Finally we compare the ergodic capacity between simple and cooperative transmission.