On Ultra-Reliable and Low Latency Simultaneous Information and Energy Transmission Systems

In this INRIA Research Report, the fundamental limits of simultaneous information and energy transmission (SIET) are studied in the non-asymptotic block-length regime. The focus is on the case of a transmitter simultaneously sending information to a receiver and energy to an energy harvester through the binary symmetric channel. Given a finite number of channel uses (latency constraint) as well as tolerable average decoding error probability and energy shortage probability (reliability constraints), two sets of information and energy transmission rates are presented. One consists in rate pairs for which the existence of at least one code achieving such rates under the latency and reliability constraints is proved (achievable region). The second one consists in a set whose complement contains the rate pairs for which there does not exist a code capable of achieving such rates (converse region). These two sets approximate the informationenergy capacity region, which allows analyzing the trade-offs among performance, latency, and reliability in SIET systems

On the Asymptotic Behavior of Selfish Transmitters Sharing a Common Channel

This paper analyzes the asymptotic behavior of a multiple-access network comprising a large number of selfish transmitters competing for access to a common wireless communication channel, and having different utility functions for determining their strategies. A necessary and sufficient condition is given for the total number of packet arrivals from selfish transmitters to converge in distribution. The asymptotic packet arrival distribution at Nash equilibrium is shown to be a mixture of a Poisson distribution and finitely many Bernoulli distributions.Comment: Proceedings of the 2008 IEEE International Symposium on Information Theory, Toronto, ON, Canada, July 6 - 11, 200

Pricing mechanisms for cooperative state estimation

978-1-4673-0274-6International audienceThe conflict between cooperation in distributed state estimation and the resulting leakage of private state information (competitive privacy) is studied for an interconnected two regional transmission organizations (RTOs) model of the grid. Using an information theoretic rate-distortion-leakage (RDL) tradeoff model, each RTO communicates at a rate chosen to optimize an objective function that is dependent on two opposing quantities: a rate-distortion based pricing function that encourages cooperation, and a leakage function that impedes it. It is shown that strictly non-zero pricing incentives are required to achieve non-trivial target distortions

The role of Signal Processing in Meeting Privacy Challenges [an overview]

International audienceWith the increasing growth and sophistication of information technology, personal information is easily accessible electronically. This flood of released personal data raises important privacy concerns. However, electronic data sources exist to be used and have tremendous value (utility) to their users and collectors, leading to a tension between privacy and utility. This article aims to quantify that tension by means of an information-theoretic framework and motivate signal processing approaches to privacy problems. The framework is applied to a number of case studies to illustrate concretely how signal processing can be harnessed to provide data privacy

Simultaneous Information and Energy Transmission: A Finite Block-Length Analysis

International audienceIn this paper, a non-asymptotic analysis of the fundamental limits of simultaneous energy and information transmission (SEIT) is presented. The notion of information-capacity region, i.e., the largest set of simultaneously achievable information and energy rates, is revisited in a context in which transmissions occur within a finite number of channel uses and strictly positive error decoding probability and energy shortage probability are tolerated. The focus is on the case of one transmitter, one information receiver and one energy harvester communicating through binary symmetric memoryless channels. In this case, the information-capacity region is approximated and the trade-off between information rate and energy rate is thoroughly studied

On Ultra-Reliable and Low Latency Simultaneous Information and Energy Transmission Systems

International audienc

On Ultra-Reliable and Low Latency Simultaneous Information and Energy Transmission Systems

In this INRIA Research Report, the fundamental limits of simultaneous information and energy transmission (SIET) are studied in the non-asymptotic block-length regime. The focus is on the case of a transmitter simultaneously sending information to a receiver and energy to an energy harvester through the binary symmetric channel. Given a finite number of channel uses (latency constraint) as well as tolerable average decoding error probability and energy shortage probability (reliability constraints), two sets of information and energy transmission rates are presented. One consists in rate pairs for which the existence of at least one code achieving such rates under the latency and reliability constraints is proved (achievable region). The second one consists in a set whose complement contains the rate pairs for which there does not exist a code capable of achieving such rates (converse region). These two sets approximate the informationenergy capacity region, which allows analyzing the trade-offs among performance, latency, and reliability in SIET systems

Approximate Nash Region of the Gaussian Interference Channel with Noisy Output Feedback

International audienceIn this paper, an achievable $\eta$-Nash equilibrium ($\eta$-NE) region for the two-user Gaussian interference channel with noisy channel-output feedback is presented for all $\eta \geqslant 1$. This result is obtained in the scenario in which each transmitter-receiver pair chooses its own transmit-receive configuration in order to maximize its own individual information transmission rate. At an $\eta$-NE, any unilateral deviation by either of the pairs does not increase the corresponding individual rate by more than $\eta$ bits per channel use

Fundamental Limits of a Dense IoT Cell in the Uplink

International audienceThe envisioned Internet of Things (IoT) will involve a massive deployment of objects connected through wireless cells. While commercial solutions are already available, the fundamental limits of such networks in terms of node density, achievable rates or reliability are not known. To address this question, this paper uses a large scale Multiple Access Channel (MAC) to model IoT nodes randomly distributed over the coverage area of a unique base station. The traffic is represented by an information rate spatial density ρ(x). This model, referred to as the Spatial Continuum Multiple Access Channel, is defined as the asymptotic limit of a sequence of discrete MACs. The access capacity region of this channel is defined as the set of achievable information rate spatial densities achievable with vanishing transmission errors and under a sum-power constraint. Simulation results validate the model and show that this fundamental limit theoretically achievable when all nodes transmit simultaneously over an infinite time, may be reached even with a relatively small number of simultaneous transmitters (typically around 20 nodes) which gives credibility to the model. The results also highlight the potential interest of non-orthogonal transmissions for IoT uplink transmissions when compared to an ideal time sharing strategy

A college admissions game for uplink user association in wireless small cell networks

International audienceIn this paper, the problem of uplink user association in small cell networks, which involves interactions between users, small cell base stations, and macro-cell stations, having often conflicting objectives, is considered. The problem is formulated as a college admissions game with transfers in which a number of colleges, i.e., small cell and macro-cell stations seek to recruit a number of students, i.e., users. In this game, the users and access points (small cells and macro-cells) rank one another based on preference functions that capture the users' need to optimize their utilities which are functions of packet success rate (PSR) and delay as well as the small cells' incentive to extend the macro-cell coverage (e.g., via cell biasing/range expansion) while maintaining the users' quality-of-service. A distributed algorithm that combines notions from matching theory and coalitional games is proposed to solve the game. The convergence of the algorithm is shown and the properties of the resulting assignments are discussed. Simulation results show that the proposed approach yields a performance improvement, in terms of the average utility per user, reaching up to 23% relative to a conventional, best-PSR algorithm