Ai for Cognitive Radios
AI FOR COGNINITIVE RADIO
Abstract: Cognitive radio (CR) is an enabling technology for numerous new capabilities such as spectrum access, spectrum sensing, spectrum decision, spectrum sharing and self organizing networks. This paper reviews several AI techniques used in cognitive radios such as : artificial neural network (ANN), hidden Markov models (HMMs), metahueristic algorithms, these techniques are proposed to provide the cognition capability in a cognitive engine.
The modern software defined radio is the heart of a cognitive radio. The applications executing on the radio distinguish a cognitive radio from a software-defined radio. Additional hardware in the form of sensors and actuators enables more cognitive radio applications. Various artificial intelligence approaches to machine learning and decision making may be applied to the cognitive radio system. In this paper, a survey of related cognitive radio topics is reviewed.
Keywords: software defined radio, cognitive radio, cognition cycle, artificial intelligence techniques for CR, characteristics of CR, types of CR .
1. INTRODUCTION
Software Defined Radio (SDR) and Artificial Intelligence (AI) technology enable the new field of Cognitive Radio (CR). An overview of these two areas is presented in this section :
A. Software Defined Radio :
A software-defined radio has several major sub-systems, but is essentially a distributed computing system. The major sub-systems are :
• RF Front End :
A SDR requires a general purpose RF front end. This typically requires a wide tuning range, ideally it tunes from DC to light notwithstanding the technical problems. A pre-selector / power amplifier allows the selection of a subset of the spectrum, and either tunable filters or fixed filters are used for this purpose. A Rx or Tx chain processes the radio signal in the analog domain. These chains may be combined into a single, bi-directional
Abstract: Cognitive radio (CR) is an enabling technology for numerous new capabilities such as spectrum access, spectrum sensing, spectrum decision, spectrum sharing and self organizing networks. This paper reviews several AI techniques used in cognitive radios such as : artificial neural network (ANN), hidden Markov models (HMMs), metahueristic algorithms, these techniques are proposed to provide the cognition capability in a cognitive engine.
The modern software defined radio is the heart of a cognitive radio. The applications executing on the radio distinguish a cognitive radio from a software-defined radio. Additional hardware in the form of sensors and actuators enables more cognitive radio applications. Various artificial intelligence approaches to machine learning and decision making may be applied to the cognitive radio system. In this paper, a survey of related cognitive radio topics is reviewed.
Keywords: software defined radio, cognitive radio, cognition cycle, artificial intelligence techniques for CR, characteristics of CR, types of CR .
1. INTRODUCTION
Software Defined Radio (SDR) and Artificial Intelligence (AI) technology enable the new field of Cognitive Radio (CR). An overview of these two areas is presented in this section :
A. Software Defined Radio :
A software-defined radio has several major sub-systems, but is essentially a distributed computing system. The major sub-systems are :
• RF Front End :
A SDR requires a general purpose RF front end. This typically requires a wide tuning range, ideally it tunes from DC to light notwithstanding the technical problems. A pre-selector / power amplifier allows the selection of a subset of the spectrum, and either tunable filters or fixed filters are used for this purpose. A Rx or Tx chain processes the radio signal in the analog domain. These chains may be combined into a single, bi-directional
References: 1. http://hal.inria.fr/docs/00/67/87/73/PDF/CITIM-benmammar.pdf 2. http://www.trai.gov.in/WriteReadData/Events/Presentation/PPT/201111230248091562500Lokesh_Chauhan.pdf 3. http://www.internetworks.my/NetApps2008/Proceedings/accepted%20paper/Learning%20Ability%20in%20Cognitive%20Radio.pdf 4. http://bsciftler.etu.edu.tr/Artificial%20Intelligence%20Applications%20in%20Wireless%20Networks.pdf 5. http://www.authorstream.com/Presentation/heyletshavfun-874437-cognitive-radio/ 6. http://www.slideshare.net/omeralich/analysis-of-ofdm-parameters-using-cyclostationary-spectrum-sensing