2 research outputs found
Resource Allocation in a MAC with and without security via Game Theoretic Learning
In this paper a -user fading multiple access channel with and without
security constraints is studied. First we consider a F-MAC without the security
constraints. Under the assumption of individual CSI of users, we propose the
problem of power allocation as a stochastic game when the receiver sends an ACK
or a NACK depending on whether it was able to decode the message or not. We
have used Multiplicative weight no-regret algorithm to obtain a Coarse
Correlated Equilibrium (CCE). Then we consider the case when the users can
decode ACK/NACK of each other. In this scenario we provide an algorithm to
maximize the weighted sum-utility of all the users and obtain a Pareto optimal
point. PP is socially optimal but may be unfair to individual users. Next we
consider the case where the users can cooperate with each other so as to
disagree with the policy which will be unfair to individual user. We then
obtain a Nash bargaining solution, which in addition to being Pareto optimal,
is also fair to each user.
Next we study a -user fading multiple access wiretap Channel with CSI of
Eve available to the users. We use the previous algorithms to obtain a CCE, PP
and a NBS.
Next we consider the case where each user does not know the CSI of Eve but
only its distribution. In that case we use secrecy outage as the criterion for
the receiver to send an ACK or a NACK. Here also we use the previous algorithms
to obtain a CCE, PP or a NBS. Finally we show that our algorithms can be
extended to the case where a user can transmit at different rates. At the end
we provide a few examples to compute different solutions and compare them under
different CSI scenarios.Comment: 27 pages, 12 figures. Part of the paper was presented in 2016 IEEE
Information theory and applicaitons (ITA) Workshop, San Diego, USA in Feb.
2016. Submitted to journa
Distributed Learning of Equilibria for a Stochastic Game on Interference Channels
We consider a wireless communication system in which N transmitter-receiver pairs want to communicate with each other. Each transmitter transmits data at a certain rate using a power that depends on the channel gain to its receiver. If a receiver can successfully receive the message, it sends an acknowledgement (ACK), else it sends a negative ACK (NACK). Each user aims to maximize its probability of successful transmission. We formulate this problem as a stochastic game and propose a fully distributed learning algorithm to find a correlated equilibrium (CE). We also propose a fully distributed learning algorithm to find a Pareto optimal solution, and we compare the utilities of each user at the CE and the Pareto point and also with some other well known recent algorithms