Resource Allocation in a MAC with and without security via Game Theoretic Learning

In this paper a $K$-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 $K$-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

Noise removal methods on ambulatory EEG: A Survey

Over many decades, research is being attempted for the removal of noise in the ambulatory EEG. In this respect, an enormous number of research papers is published for identification of noise removal, It is difficult to present a detailed review of all these literature. Therefore, in this paper, an attempt has been made to review the detection and removal of an noise. More than 100 research papers have been discussed to discern the techniques for detecting and removal the ambulatory EEG. Further, the literature survey shows that the pattern recognition required to detect ambulatory method, eye open and close, varies with different conditions of EEG datasets. This is mainly due to the fact that EEG detected under different conditions has different characteristics. This is, in turn, necessitates the identification of pattern recognition technique to effectively distinguish EEG noise data from a various condition of EEG data