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.

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))).

We propose a new resource efficient transmission scheme to achieve cooperative diversity of a distributed smart antenna system consisting of an information source, destination, and two alternating supportive relay stations. Each station is equipped with one antenna element. Furthermore we assume perfect synchronization and channel knowledge at the receivers. The proposed linear encoding functions with interference cancellation at one relay station result in an equivalent space-time delay coded system, but with additionally colored noise from the relay stations. Based on the maximum likelihood sequence estimator we derive criteria to evaluate the performance compared to the direct S1SO transmission. Finally the numerical simulation results of an suboptimal sequence estimator show significant performance gains if the power gain from the hopping is gainful exploited. If not, the additional noise from the relay stations will degrade the performance at reasonable transmit powers.

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.

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 $U$ .

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.