The 18th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt 2020)

The 15th Workshop on Resource Allocation, Cooperation and Competition in Wireless Networks (RAWNET)

Session RAWNET-Papers

RAWNET Session

Conference
12:01 AM — 11:59 PM EEST
Local
Jun 18 Thu, 5:01 PM — 4:59 PM EDT

Invariant Nash Equilibrium for Large Player Games in Multiple Access Channels

Prashant Narayanan and Lakshmi Narasimhan Theagarajan Department of EE, Indian Institute of Technology Palakkad, Kerala 678557

0
We consider a wireless multiple access channel (MAC) with N users. Associated with each user is their time-varying channel state and a finite-length queue which varies with time. In MAC, a receiver decodes the signals of each user by treating the other users’ signals as noise. Each user decides their transmit power and the queue-admission control variable dynamically to maximize their expected throughput without any knowledge of the states and actions of other users. This decision problem is formulated as a Markov game for which we show the existence of equilibrium and an algorithm to compute the equilibrium policies. We show that, when the number of users exceeds a given threshold, the expected throughput of all users at all the equilibria points are the same. Furthermore, we show that the equilibrium policies of the users are invariant as long as the number of users remain above the latter threshold, which is referred to as the infinitely invariant Nash equilibrium (IINE). For the considered system, we prove that IINE exists and show that each user can compute these policies using a sequence of linear programs which does not depend upon the parameters of the other users. We also provide the necessary and sufficient conditions for the existence of IINE. Finally, we validate our analysis using numerical simulations.

You Snooze, You Lose: Minimizing Channel-Aware Age of Information

Bhishma Dedhia and Sharayu Moharir Department of Electrical Engineering Indian Institute of Technology Bombay

0
We propose a variant of the Age of Information (AoI) metric called Channel-Aware Age of Information (CA-AoI). Unlike AoI, CA-AoI takes into account the channel conditions between the source and the intended destination to compute the “age” of the recent most update received by the destination. We design scheduling policies for multi-sensor systems in which sensors report their measurements to a central monitoring station via a shared unreliable communication channel with the goal of minimizing the time-average of the weighted sum of CA- AoIs. We show that the scheduling problem is indexable and derive low complexity Whittle index based scheduling policies. We also design randomized scheduling algorithms and give optimization procedures to find the optimal parameters of the policy. Via simulations, we show that our proposed policies surpass the greedy policy in several settings. Moreover the Whittle Index based scheduling policies outperform other policies in all the settings considered.

Joint Downlink Power Control and Channel Allocation based on a Partial View of Future Channel Conditions

T. T. Nga Nguyen (Continental Digital Services France, LAAS-CNRS, Universite de Toulouse, CNRS, UPS, Toulouse, France), Olivier Brun (LAAS-CNRS, Universite de Toulouse, CNRS, UPS, Toulouse, France) and Balakrishna J. Prabhu (LAAS-CNRS, Universite de Toulouse, CNRS, UPS, Toulouse, France)

0
We propose two downlink scheduling algorithms that take advantage of partial information on future channel conditions for improving the sum utility. The scheduling model allows for both power control and channel allocation. The objective of the scheduler is the long-term utility under an average power constraint. The two algorithms incorporate the channel predictions in their decisions.The STO1 algorithm computes the decision in each slot based on the means of future channel gains. Depending on the horizon considered, this can require solving a large-dimensional problem in each slot. The STO2 algorithm reduces the dimensionality by operating on two time-scales. On the slower scale it computes an estimation over a larger horizon, and in the faster scale of a slot, it computes the decision based on a shorter horizon. Numerical experiments with both fixed number of users as well as a dynamic number of users show that the two algorithms provide gains in utility compared to agnostic ones.

Mobile Data Offloading with Flexible Pricing

M. Sushma and K. P. Naveen Department of Electrical Engineering Indian Institute of Technology Tirupati, India.

0
We consider a data offloading scenario where the small-cell service providers (SSPs) are allowed to implement a flexible-pricing scheme. In the aforementioned scheme, the price that an SSP charges the mobile-network operator (MNO) depends on the amount of MNO’s traffic that is offloaded onto the respective SSP. Formulating the SSPs’ problem of determining their traffic-dependent prices as a Bayesian game, we first show that there exists no Nash equilibriums in pure strategies. We then proceed to derive the structure of a mixed-strategy symmetric Bayesian Nash equilibrium (BNE). We also compare the flexible-pricing scheme with the traditional flat-pricing scheme (where the SSPs are restricted to announce a single price, irrespective of the traffic that is offloaded onto them) in terms of the payoffs achieved by the SSPs as well as the MNO. We show that the SSPs benefit under the flexible-pricing scheme while the MNO incurs a loss in payoff; however, the net-payoff of the system remains balanced. Formally, in terms of mechanism design, the flexible- pricing scheme is incentive compatible as all entities (including the neutral MNO) achieve a non-negative payoff. Finally, focussing on the SSPs’ payoff, we conduct a numerical study to demonstrate the efficacy of the flexible-pricing scheme over the flat-pricing scheme (using price of anarchy as the metric).

Optimal Blind and Adaptive Fog Orchestration under Local Processor Sharing

Francesco De Pellegrini, Francescomaria Faticanti, Mandar Datar, Eitan Altman and Domenico Siracusa

0
This paper studies the tradeoff between running cost and processing delay in order to optimally orchestrate multiple fog applications. Fog applications process batches of objects’ data along chains of containerised microservice modules, which can run either for free on a local fog server or run in cloud at a cost. Processor sharing techniques, in turn, affect the applications’ processing delay on a local edge server depending on the number of application modules running on the same server. The fog orchestrator copes with local server congestion by offloading part of computation to the cloud trading off processing delay for a finite budget. Such problem can be described in a convex optimisation framework valid for a large class of processor sharing techniques. The optimal solution is in threshold form and depends solely on the order induced by the marginal delays of N fog applications. This reduces the original multidimensional problem to an unidimensional one which can be solved in O(N 2) by a parallelised search algorithm under complete system information. Finally, an online learning procedure based on a primal-dual stochastic approximation algorithm is designed in order to drive optimal reconfiguration decisions in the dark, by requiring only the unbiased estimation of the marginal delays. Extensive numerical results characterise the structure of the optimal solution, the system performance and the advantage attained with respect to baseline algorithmic solutions.

Distributed Hypothesis Testing with Variable-Length Coding

Sadaf Salehkalaibar and Michele Wigger

0
This paper characterizes the optimal type-II error exponent for a distributed hypothesis testing-against-independence problem when the expected rate of the sensor- detector link is constrained. Unlike for the well-known Ahlswede-Csiszar result that holds under a maximum rate constraint and where a strong converse holds, here the optimal exponent depends on the allowed type-I error exponent. Specifically, if the type-I error probability is limited by , then the optimal type-II error exponent under an expected rate constraint R coincides with the optimal type-II error exponent under a maximum rate constraint of (1 − )R.

Session Chair

Not Needed — Asynchronous Q&A throughout the conference

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Session RAWNET-Keynote

Keynote

Conference
3:00 PM — 4:15 PM EEST
Local
Jun 19 Fri, 8:00 AM — 9:15 AM EDT

Semantic Communications: a Paradigm Shift in Networked Intelligent Systems

Marios Kountouris (Dept. of Communications Systems EURECOM Sophia Antipolis, France)

0
Wireless communication systems have been traditionally viewed as an opaque data pipe carrying content and messages, whose meaning, impact upon receipt, and usefulness for achieving a goal, have been deliberately set aside. As we are entering the era of connected intelligence, overlooking the relevance and the significance of the exchanged information is inefficient, comes short of meaningfully scaling and is inadequate for emerging time-sensitive and data-intensive communication scenarios. In this talk, Semantic Communication is introduced, a radically new paradigm that accounts for the semantics of information bits being processed and transported in the network.

Session Chair

Alex Dytso - Princeton University, Samson Lasaulce – CNRS and Samir M. Perlaza - Inria

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