arXiv subjects

Vishnu Raj

Publications and source records attributed to Vishnu Raj.

23 records · Page 2Linked to original sources

arXiv2020

Leveraging online learning for CSS in frugal IoT network

We present a novel method for centralized collaborative spectrum sensing for IoT network leveraging cognitive radio network. Based on an online learning framework, we propose an algorithm to efficiently combine the individual sensing results based on the past performance of each detector. Additionally, we show how to utilize the learned normalized weights as a proxy metric of detection accuracy and selectively enable the sensing at detectors. Our results show improved performance in terms of inter-user collision and misdetection. Further, by selectively enabling some of the devices in the network, we propose a strategy to extend the field life of devices without compromising on detection accuracy.

cs.IT↗

arXiv2019

Blind decoding in $α$-Stable noise: An online learning approach

A novel method for performing error control coding in Symmetric $α-$Stable noise environments without any prior knowledge about the value of $α$ is introduced. We use an online learning framework which employs multiple distributions to decode the received block and then combines these results based on the past performance of each individual distributions. The proposed method is also able to handle a mixture of Symmetric $α-$Stable distributed noises. Performance results in turbo coded system highlight the utility of the work.

cs.IT↗

arXiv2017

An aggregating strategy for shifting experts in discrete sequence prediction

We study how we can adapt a predictor to a non-stationary environment with advises from multiple experts. We study the problem under complete feedback when the best expert changes over time from a decision theoretic point of view. Proposed algorithm is based on popular exponential weighing method with exponential discounting. We provide theoretical results bounding regret under the exponential discounting setting. Upper bound on regret is derived for finite time horizon problem. Numerical verification of different real life datasets are provided to show the utility of proposed algorithm.

cs.LG↗

arXiv2017

Taming Non-stationary Bandits: A Bayesian Approach

We consider the multi armed bandit problem in non-stationary environments. Based on the Bayesian method, we propose a variant of Thompson Sampling which can be used in both rested and restless bandit scenarios. Applying discounting to the parameters of prior distribution, we describe a way to systematically reduce the effect of past observations. Further, we derive the exact expression for the probability of picking sub-optimal arms. By increasing the exploitative value of Bayes' samples, we also provide an optimistic version of the algorithm. Extensive empirical analysis is conducted under various scenarios to validate the utility of proposed algorithms. A comparison study with various state-of-the-arm algorithms is also included.

stat.ML↗

arXiv2017

Spectrum Access In Cognitive Radio Using A Two Stage Reinforcement Learning Approach

With the advent of the 5th generation of wireless standards and an increasing demand for higher throughput, methods to improve the spectral efficiency of wireless systems have become very important. In the context of cognitive radio, a substantial increase in throughput is possible if the secondary user can make smart decisions regarding which channel to sense and when or how often to sense. Here, we propose an algorithm to not only select a channel for data transmission but also to predict how long the channel will remain unoccupied so that the time spent on channel sensing can be minimized. Our algorithm learns in two stages - a reinforcement learning approach for channel selection and a Bayesian approach to determine the optimal duration for which sensing can be skipped. Comparisons with other learning methods are provided through extensive simulations. We show that the number of sensing is minimized with negligible increase in primary interference; this implies that lesser energy is spent by the secondary user in sensing and also higher throughput is achieved by saving on sensing.

cs.IT↗