Optimal decision making in cognitive radio networks

Cognitive Radio Networks are being researched upon heavily in the various layers of the communication structure. The task of bringing software in the physical layer of communication system led to the concept of a smart radio being able to learn, adapt and make intelligent decisions in an autonomous manner by use of a Software Defined Radio. This work provides novel concepts in the areas of spectrum sensing, learning of ongoing transmissions through Reinforcment learning, use of a game theoretic concept such as Zero-sum game for resilience of authorized users in cases of jamming, and decision making of user transmissions through Markov Decision processes. This is highly applicable in dynamic radio environments such as emergency communications required during natural disasters, large scale events and in mobile wireless communications. Such applications come under the "Internet of Things"

Spectrum sensing and detection of incumbent-UEs in secondary-LTE based aerial-terrestrial networks for disaster recovery

In a disaster recovery scenario, there is a need for the deployment of a supplementary communications infrastructure when the regular communications infrastructure is considered to be damaged and is temporarily unavailable. Hence, we consider such a supplementary network operating as secondary user of the spectrum, which opportunistically utilizes the same spectral band as the incumbent regular network until the incumbent comes alive. When the incumbent network comes alive, it is important to vacate the corresponding spectral bands without interfering with its communications. In our work, we consider both the secondary and incumbent networks as LTE networks where the secondary network has an aerial base station with low altitude platform. In this paper, we propose a method to perform spectrum sensing at the aerial eNodeB for the secondary LTE network in order to detect the incumbent user equipments on ground to avoid interfering with them. A novel approach is presented to perform this task considering the 3GPP-LTE uplink transmission specifications. Moreover, we consider the energy based spectrum sensing for detecting the incumbent users on the ground by the aerial eNodeB, and extensive simulation results are presented in a Rice fading environment for the detection and the false alarm probabilities

Reinforcement learning based secondary user transmissions in cognitive radio networks

In this paper, we address the decision making criteria of a secondary user (SU) for deciding whether to transmit or not upon performing spectrum sensing and detecting the presence of any primary user (PU) in the environment in a cognitive radio network (CRN). We propose a reinforcement learning (RL) based approach by a Markov process at the SU node and present novel analytical methods to analyze the performance of such approaches. In particular, we define the probability of interference Pi and the probability of wastage Pw, and compare these metrics with a RL based and a non-RL based approach for SU transmission. The simulations show the presence of a tradeoff in the two probability metrics Pw and Pi, based on the Markov process. The simulation results are compared in the form of the transmitter operating characteristics (ToC) curves. Using our approach, one could control the interference to the PU by trading off with the spectral wastage

On the achievable rate in a D2D cognitive secondary network under jamming attacks

Ongoing developments of the LTE standard will allow for device-to-device (D2D) communications, which will enable direct connection of user equipments (UEs). Since UEs are becoming increasingly more powerful both in computational power and in the role they have in the network, a concrete threat is that a hand-held D2D-enabled device could be deployed to jam intentionally ongoing transmissions of other D2D users. In this context, a natural concern for operators will be the resilience of the legitimate user (LU) against a jammer's (J) attack. In this work, we model an LTE D2D system made of a pair of LUs and a J that tries to impair their communication. We model the adversarial scenario between the transmitting LU and J as a zero-sum game: in this game, J's target is to minimize the throughput of the legitimate D2D pair. We show the achievable channel rate of the D2D pair under jamming attacks and the existence of a Nash equilibrium. Finally, when both players learn each other strategy over time, e.g., employing fictitious play, such equilibrium becomes the system's operating point

An optimal transmission strategy in zero-sum matrix games under intelligent jamming attacks

Cognitive radio networks are more susceptible to jamming attacks due to the nature of unlicensed users accessing the spectrum by performing dynamic spectrum access. In such a context, a natural concern for operators is the resilience of the system. We model such a scenario as one of adversity in the system consisting of a single legitimate (LU) pair and malicious user (MU). The aim of the LU is to maximize throughput of transmissions, while the MU is to minimize the throughput of the LU completely. We present the achievable transmission rate of the LU pair under jamming attacks taking into account mainly on the transmission power per channel. Furthermore, we embed our utility function in a zero-sum matrix game and extend this by employing a fictitious play when both players learn each other's strategy over time, e.g., such an equilibrium becomes the system's global operating point. We further extend this to a reinforcement learning (RL) approach, where the LU is given the advantage of incorporating RL methods to maximize its throughput for fixed jamming strategies

Support for traceability management of software artefacts using natural language processing

One of the major problems in software development process is managing software artefacts. While software evolves, inconsistencies between the artefacts do evolve as well. To resolve the inconsistencies in change management, a tool named “Software Artefacts Traceability Analyzer (SAT-Analyzer)” was introduced as the previous work of this research. Changes in software artefacts in requirement specification, Unified Modelling Language (UML) diagrams and source codes can be tracked with the help of Natural Language Processing (NLP) by creating a structured format of those documents. Therefore, in this research we aim at adding an NLP support as an extension to SAT-Analyzer. Enhancing the traceability links created in the SAT-analyzer tool is another focus due to artefact inconsistencies. This paper includes the research methodology and relevant research carried out in applying NLP for improved traceability management. Tool evaluation with multiple scenarios resulted in average Precision 72.22%, Recall 88.89% and F1 measure of 78.89% suggesting high accuracy for the domain

Reinforcement learning-based spectrum management for cognitive radio networks: A literature review and case study

In cognitive radio (CR) networks, the cognition cycle, i.e., the ability of wireless transceivers to learn the optimal configuration meeting environmen- tal and application requirements, is considered as important as the hardware components which enable the dynamic spectrum access (DSA) capabilities. To this purpose, several machine learning (ML) techniques have been applied on CR spectrum and network management issues, including spectrum sensing, spectrum selection, and routing. In this paper, we focus on reinforcement learning (RL), an online ML paradigm where an agent discovers the optimal sequence of actions required to perform a task via trial-end-error interactions with the environment. Our study provides both a survey and a proof of concept of RL applications in CR networking. As a survey, we discuss pros and cons of the RL framework compared to other ML techniques, and we provide an exhaustive review of the RL-CR literature, by considering a twofold perspective, i.e., an applicationdriven taxonomy and a learning methodology-driven taxonomy. As a proof of concept, we investigate the application of RL techniques on joint spectrum sensing and decision problems, by comparing different algorithms and learning strategies and by further analyzing the impact of information sharing techniques in purely cooperative or mixed cooperative/competitive tasks