A reinforcement learning approach to cognitive radio

In wireless communication system there are several routing problems. To overcome this problem we use the future generation wireless communication system known as Cognitive Radio Network (CRN). It allows the less priority user to use the unused or underutilized spectrum of priority user. However, dynamic conditions of CRN (Priority user activity and channel availability) more routing more challenging. This challenge is overcome with the help of clustering mechanism. Cluster-Based Routing in CRN enhances network scalability and stability. Additionally, an artificial intelligence approach Reinforcement Learning (RL) is used to maximize the network performance. We present a model SMART, a cluster-based routing scheme and evaluate the performance using stimulation in order to show the effectiveness of Cluster-Based routing in CRNs using RL.

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2020

Learning-Based Approaches for Intelligent Cognitive Radio

Vehicular …, 2010

Abstract—Cognitive radio (CR) is an enabling technology for numerous new capabilities such as dynamic spectrum access, spectrum markets, and self-organizing networks. To realize this diverse set of applications, CR researchers leverage a variety of artificial intelligence (AI) ...

2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA)

Cognitive radio (CR) system being an intelligencebased communication device has been considered as the next generation emerging technologies to Wireless Communication Systems (WCS). This CR's embedded-intelligent agent is called Cognitive Engine (CE), and is responsible for the dynamic adaptation between the WCS's environment and the radio operational parameters. As a result of CR's intelligence capability, the WCS's quality of service (QoS) and its connectivity operations get enhanced. In order to evaluate the CR engine performance in respect to its learning, timing, and its computational performances. This paper proposes an alternative state-of-the-art enhanced CR learning engine based on Random Neural Network (RNN). Unlike Artificial Neural Network (ANN) systems, RNN establishes strong data generalization, converges faster and produces relatively smaller levels of prediction errors. Subjected to similar environmental conditions, the simulation cumulative results show that the performance of the proposed RNN system is satisfactory and produces 36.895% performance improvement above the ANN learning engine.

Computer Communications, 2008

Intelligence is needed to keep up with the rapid evolution of wireless communications, especially in terms of managing and allocating the scarce, radio spectrum in the highly varying and disparate modern environments. Cognitive radio systems promise to handle this situation by utilizing intelligent software packages that enrich their transceiver with radio-awareness, adaptability and capability to learn. A cognitive radio system participates in a continuous process, the "cognition cycle", during which it adjusts its operating parameters, observes the results and, eventually takes actions, that is to say, decides to operate in a specific radio configuration (i.e., radio access technology, carrier frequency, modulation type etc.) expecting to move the radio toward some optimized operational state.

IEEE Journal of Selected Topics in Signal Processing

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.

IEEE Communications Magazine, 2016

IEEE Communications Surveys & Tutorials, 2000

Cognitive radio networks challenge the traditional wireless networking paradigm by introducing concepts firmly stemmed into the Artificial Intelligence (AI) field, i.e., learning and reasoning. This fosters optimal resource usage and management allowing a plethora of potential applications such as secondary spectrum access, cognitive wireless backbones, cognitive machine-to-machine etc. The majority of overview works in the field of cognitive radio networks deal with the notions of observation and adaptations, which are not a distinguished cognitive radio networking aspect. Therefore, this paper provides insight into the mechanisms for obtaining and inferring knowledge that clearly set apart the cognitive radio networks from other wireless solutions.

IEEE Local Computer Network Conference, 2010

Cognitive Radio (CR) is a next-generation wireless communication system that enables unlicensed users to exploit underutilized licensed spectrum to optimize the utilization of the overall radio spectrum. A Distributed Cognitive Radio Network (DCRN) is a distributed wireless network established by a number of unlicensed users in the absence of fixed network infrastructure. Context awareness and intelligence are the capabilities to enable each unlicensed user to observe and carry out its own action as part of the joint action on its operating environment for network-wide performance enhancement. These capabilities can be applied in various application schemes in CR networks such as Dynamic Channel Selection (DCS), congestion control, and scheduling. In this paper, we apply Reinforcement Learning (RL), including single-agent and multi-agent approaches, to achieve context awareness and intelligence. Firstly, we show that the RL achieves a joint action that provides better network-wide performance in respect to DCS in DCRNs. Secondly, we show that RL achieves high level of fairness. Thirdly, we show the effects of network density and various essential parameters in RL on the network-wide performance.