Kartik Ahuja

Efficient Interference Management Policies for Femtocell Networks

Managing interference in a network of macrocells underlaid with femtocells presents an important, yet challenging problem. A majority of spatial (frequency/time) reuse based approaches partition the users based on coloring the interference graph, which is shown to be suboptimal. Some spatial time reuse based approaches schedule the maximal independent sets (MISs) in a cyclic, (weighted) round-robin fashion, which is inefficient for delay-sensitive applications. Our proposed policies schedule the MISs in a non-cyclic fashion, which aim to optimize any given network performance criterion for delay-sensitive applications while fulfilling minimum throughput requirements of the users. Importantly, we do not take the interference graph as given as in existing works; we propose an optimal construction of the interference graph. We prove that under certain conditions, the proposed policy achieves the optimal network performance. For large networks, we propose a low-complexity algorithm for computing the proposed policy. We show that the policy computed achieves a constant competitive ratio (with respect to the optimal network performance), which is independent of the network size, under wide range of deployment scenarios. The policy can be implemented in a decentralized manner by the users. Compared to the existing policies, our proposed policies can achieve improvement of up to 130% in large-scale deployments.

Self-Organizing Networks of Information Gathering Cognitive Agents

In many scenarios, networks emerge endogenously as cognitive agents establish links in order to exchange information. Network formation has been widely studied in economics, but only on the basis of simplistic models that assume that the value of each additional piece of information is constant. In this paper, we present a first model and associated analysis for network formation under the much more realistic assumption that the value of each additional piece of information depends on the type of that piece of information and on the information already possessed: information may be complementary or redundant. We model the formation of a network as a noncooperative game in which the actions are the formation of links and the benefit of forming a link is the value of the information exchanged minus the cost of forming the link. We characterize the topologies of the networks emerging at a Nash equilibrium (NE) of this game and compare the efficiency of equilibrium networks with the efficiency of centrally designed networks. To quantify the impact of information redundancy and linking cost on social information loss we provide estimates for the price of anarchy (PoA), and to quantify the impact on individual information loss we introduce and provide estimates for a measure we call maximum information loss (MIL). Finally, we consider the setting in which agents are not endowed with information, but must produce it. We show that the validity of the well-known “law of the few” depends on how information aggregates, in particular, the “law of the few” fails when information displays complementarities.

The Population Dynamics of Websites: [Extended Abstract]

Websites derive revenue by advertising or charging fees for services and so their profit depends on their user base -- the number of users visiting the website. But how should websites control their user base? This paper is the first to address and answer this question. It builds a model in which, starting from an initial user base, the website controls the growth of the population by choosing the intensity of referrals and targeted ads to potential users. A larger population provides more profit to the website, but building a larger population through referrals and targeted ads is costly; the optimal policy must therefore balance the marginal benefit of adding users against the marginal cost of referrals and targeted ads. The nature of the optimal policy depends on a number of factors. Most obvious is the initial user base; websites starting with a small initial population should offer many referrals and targeted ads at the beginning, but then decrease referrals and targeted ads over time. Less obvious factors are the type of website and the typical length of time users remain on the site: the optimal policy for a website that generates most of its revenue from a core group of users who remain on the site for a long time -- e.g., mobile and online gaming sites -- should be more aggressive and protective of its user base than that of a website whose revenue is more uniformly distributed across users who remain on the site only briefly. When arrivals and exits are stochastic, the optimal policy is more aggressive -- offering more referrals and targeted ads.

To participate or not in spectrum auctions with entry fee: Bayesian game theoretic approach

In this paper, competition among multiple secondary users (SUs) for spectrum access is modeled as a simultaneous repeated auction. Upon participation in an auction, a SU is charged with an entry fee. However, participation does not ensure an access to the channel. This tradeoff leads it to decide either for or against entering the auction. We consider no cooperation among the SUs, and model this situation as a Bayesian game. A modification of the standard regret testing procedure is proposed to fit our system model. Our proposed procedure converges to Nash equilibrium (NE) of the game. Since this procedure is computationally expensive, we propose a less expensive learning based procedure for the decision taking. We present computer simulation results to compare the average profits and bidding efficiencies over time for the proposed procedure. We also compare their bidding efficiencies to another procedure in the literature, based on second highest bid prediction.

Towards a theory of societal co-evolution: Individualism versus collectivism

Substantial empirical research has shown that the level of individualism vs. collectivism is one of the most critical and important determinants of societal traits, such as economic growth, economic institutions and health conditions. But the exact nature of this impact has thus far not been well understood in an analytical setting. In this work, we develop one of the first theoretical models that analytically studies the impact of individualism-collectivism on the society. We model the growth of an individuals welfare (wealth, resources and health) as depending not only on himself, but also on the level of collectivism, i.e. the level of dependence on the rest of the individuals in the society, which leads to a co-evolutionary setting. Based on our model, we are able to predict the impact of individualism-collectivism on various societal metrics, such as average welfare, average lifetime, total population, cumulative welfare and average inequality. We analytically show that individualism has a positive impact on average welfare and cumulative welfare, but comes with the drawbacks of lower average life-time, lower total population and higher average inequality.