Searcharxiv⌕ Search

arXiv · 0709.3880

A New Perspective on Multi-user Power Control Games in Interference Channels

Abstract

This paper considers the problem of how to allocate power among competing users sharing a frequency-selective interference channel. We model the interaction between selfish users as a non-cooperative game. As opposed to the existing iterative water-filling algorithm that studies the myopic users, this paper studies how a foresighted user, who knows the channel state information and response strategies of its competing users, should optimize its transmission strategy. To characterize this multi-user interaction, the Stackelberg equilibrium is introduced, and the existence of this equilibrium for the investigated non-cooperative game is shown. We analyze this interaction in more detail using a simple two-user example, where the foresighted user determines its transmission strategy by solving as a bi-level program which allows him to account for the myopic user's response. It is analytically shown that a foresighted user can improve its performance, if it has the necessary information about its competitors. Since the optimal solution of Stackelberg equilibrium is computationally prohibitive, we propose a practical low-complexity approach based on Lagrangian duality theory. Numerical simulations verify the performance improvements. Possible ways to acquire the required information and to extend the formulation to more than two users are also discussed.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yi Su, Mihaela van der Schaar. 2008-12-16. A New Perspective on Multi-user Power Control Games in Interference Channels. https://arxiv.org/abs/0709.3880

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Switchback Price Experiments with Forward-Looking Demand

Switchback experiments are widely used in pricing applications. We consider a retailer running such an experiment for the price of a single product, with infinite supply. In each period, the seller chooses a price from a set of predefined prices consisting of an original price and a few discounted price levels. The goal is to estimate the demand gradient at the original price to adjust the original price and improve revenue after the experiment. In our model, in each period, a unit mass of buyers arrives on the market, with values distributed based on a time-varying process. Crucially, buyers are forward-looking with a discounted utility and will choose not to purchase now if they expect to face a discounted price in the near future. We show that such forward-looking behavior introduces bias in naive estimators of the demand gradient, due to intertemporal interference. Furthermore, although experiments with only two price points are common in practice, we prove that no estimator using data from such experiments can recover the correct demand gradient, even in the limit of an infinitely long experiment with an infinitesimal price discount. Moreover, we characterize the form of the bias of naive estimators. Finally, we show that with a simple three-price-level experiment, the seller can remove the bias due to strategic forward-looking behavior and construct an estimator for the demand gradient that asymptotically recovers the truth. Our results provide practical guidance for platforms to obtain unbiased demand gradients from price experiments in the presence of forward-looking demand.

cs.GT↗

Learning in Structured Stackelberg Games

We initiate the study of structured Stackelberg games, a novel form of strategic interaction between a leader and a follower where contextual information can be predictive of the follower's (unknown) type. Motivated by applications such as security games and AI safety, we show how this additional structure can help the leader learn a utility-maximizing policy in both the online and distributional settings. In the online setting, we first prove that standard learning-theoretic measures of complexity do not characterize the difficulty of the leader's learning task. Notably, we find that there exists a learning-theoretic measure of complexity, analogous to the Littlestone dimension in online classification, that tightly characterizes the leader's instance-optimal regret. We term this the Stackelberg-Littlestone dimension, and leverage it to provide a provably optimal online learning algorithm. In the distributional setting, we provide analogous results by showing that two new dimensions control the sample complexity upper- and lower-bound.

cs.GT↗

Infinite lexicographic products of positional objectives

This paper contributes to the study of positional determinacy of infinite duration games played on potentially infinite graphs with neutral transitions. Recently, [Ohlmann, TheoretiCS 2023] established that positionality of prefix-independent objectives is preserved by finite lexicographic products. We propose two different notions of infinite lexicographic products indexed by arbitrary ordinals, and extend Ohlmann's result by proving that they also preserve positionality. In the context of one-player positionality, this extends positional determinacy results of [Grädel and Walukiewicz, Logical Methods in Computer Science 2006] to edge-labelled games and arbitrarily many priorities for both Max-Parity and Min-Parity. Moreover, we show that the Max-Parity objectives over countable ordinals are complete for the infinite levels of the difference hierarchy over $Σ^0_2$ and that Min-Parity is complete for the class $Σ^0_3$. We obtain therefore positional languages that are complete for all those levels, as well as new insights about closure under unions and neutral letters.

cs.GT↗