Learn More
We investigate a class of reinforcement learning dynamics in which players adjust their strategies based on their actions' cumulative payoffs over time – specifically, by playing mixed strategies that maximize their expected cumulative payoff minus a strongly convex, regularizing penalty term. In contrast to the class of penalty functions used to define(More)
—We investigate the behavior of a large number of selfish users that are able to switch dynamically between multiple wireless access-points (possibly belonging to different standards) by introducing an iterated non-cooperative game. Users start out completely uneducated and na¨ıve but, by using a fixed set of strategies to process a broadcasted training(More)
We analyze the problem of distributed power allocation for orthogonal multiple access channels by considering a non-cooperative game whose strategy space corresponds to the users' distribution of transmission power over the network's channels. When the channels are static, we find that this game admits an exact potential function and this allows us to show(More)
This work proposes a distributed power allocation scheme for maximizing energy efficiency in the uplink of orthogonal frequency-division multiple access (OFDMA)-based heterogeneous networks (HetNets) where a macro-tier is augmented with a mix of small cell access points – broadly varying in capabilities. The user equipment (UE) in the network are modeled as(More)
—In this paper, we examine cognitive radio systems that evolve dynamically over time due to changing user and environmental conditions. To combine the advantages of orthogonal frequency division multiplexing (OFDM) and multiple-input, multiple-output (MIMO) technologies, we consider a MIMO–OFDM cognitive radio network where wireless users with multiple(More)
We study repeated games where players employ an exponential learning scheme in order to adapt to an ever-changing environment. If the game's payoffs are subject to random perturbations, this scheme leads to a new stochastic version of the replicator dynamics that is quite different from the " aggregate shocks " approach of evolutionary game theory.(More)
We analyze the problem of finding the optimal signal covariance matrix for multiple-input multiple-output (MIMO) multiple access channels by using an approach based on ”ex-ponential learning”, a novel optimization method which applies more generally to (quasi-)convex problems defined over sets of positive-definite matrices (with or without(More)
We investigate the emergence of rationality in repeated games where, at each iteration, the players' payoffs are randombly perturbed (to account e.g. for the effects of fading or errors in the reading of one's throughput). We see that even if players start out completely uneducated about the game, there is a simple learning scheme that enables them to(More)
We analyze the distributed power allocation problem in parallel multiple access channels (MAC) by studying an associated non-cooperative game which admits an exact potential function. Even though games of this type have been the subject of considerable study in the literature [1–4], we find that the sufficient conditions which ensure uniqueness of Nash(More)