Development of Radio Environment Map Enabled Case- and Knowledge-Based Learning Algorithms for IEEE 802.22 WRAN Cognitive Engines

Zhao, Youping; Gaeddert, Joseph; Morales, Lizdabel; Bae, K.K.; Um, Jungsun; Reed, Jeffrey H.

doi:10.1109/crowncom.2007.4549770

Cited by 31 publications

(11 citation statements)

References 5 publications

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“…The REM proposed as such, has been mainly considered for IEEE 802.22 WRAN (Wireless Regional Area Network) scenarios and applications [13,14,32] where the focus is on secondary access on TV WhiteSpaces (TVWS). In that respect, the functionality of the REM is more like the TVWS database put forward by the FCC rules where it exclusively stores environmental information which is available to CR devices trying to perform secondary access on TVWS.…”

Section: The Radio Environmental Map Conceptmentioning

confidence: 99%

From Self-Organizing to Cognitive Networks: How Can the Cellular Network Operator Make Use of the Cognitive Paradigm?

Sayraç

2012

Lecture Notes in Electrical Engineering

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This chapter provides a compact view on the use of cognitive radio (CR) principles in cellular Radio Access Network (RAN) operation and management. This is achieved through a set of tangible operator-centric scenarios where cognitive features have the potential to bring benefits. These benefits are primarily due to a powerful enabling concept that increases the radio environmental awareness: Radio Environmental Maps (REMs). REMs are obtained by collecting and processing geo-localized measurements/observations reported by multiple network nodes/entities with the purpose of optimizing RAN management and operations like resource allocation/usage efficiency, coverage/capacity/Quality of Service (QoS) optimization. The REM-based operator-centric scenarios which are presented in this chapter provide concrete examples where the cellular operator can apply the cognitive paradigm on its radio networks and have potential benefits/ opportunities which is translated into performance and OPerational EXpenditures (OPEX) gains. Apart from those scenarios, the chapter also presents the REM functional architecture together with a detailed REM system architecture mapped onto the existing 3GPP Long Term Evolution (LTE) RAN architecture for selfoptimization and self-configuration of femtocells. The REM system architecture details provided by this chapter allows us to make cost calculations for each specific scenario once the requirements in terms of spatial and temporal REM updates are known. An example of signaling cost calculation for a specific measurement reporting message is given at the end of the chapter.

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Section: The Radio Environmental Map Conceptmentioning

confidence: 99%

From Self-Organizing to Cognitive Networks: How Can the Cellular Network Operator Make Use of the Cognitive Paradigm?

Sayraç

2012

Lecture Notes in Electrical Engineering

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“…The techniques presented are artificial neural networks (ANN), metaheuristic algorithms, hidden Markov model (HMM), rule based system (RBS), and casebased system (CBS). The literature reports the application of those techniques to different processes of CR including classification of signals for spectrum sensing [22,23], radio parameter adaptation [24,25], spectrum occupancy prediction [26,27], and multi-objective optimization [28,29].…”

Section: Decidingmentioning

confidence: 99%

Improving the Reliability of Unmanned Aircraft System Wireless Communications through Cognitive Radio Technology

Reyes¹,

Kaabouch²

2013

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Unmanned Aircraft System networks are a special type of networks where high speeds of the nodes, long distances and radio spectrum scarcity pose a number of challenges. In these networks, the strength of the transmitted/received signals varies due to jamming, multipath propagation, and the changing distance among nodes. High speeds cause another problem, Doppler Effect, which produces a shifting of the central frequency of the signal at the receiver. In this paper we discuss a modular system based on cognitive to enhance the reliability of UAS networks.

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“…The CE is capable of learning and intelligently evolving a radio's PHY and MAC layers when faced with unanticipated wireless and network situations [14]. Virginia Tech researchers also developed a CE based on a knowledge-based reasoner and a genetic algorithm multi-objective optimizer [18,34]. This hybrid CE adopts a modular approach, thus the reasoner and optimizer modules could be used at any time during the algorithm adaptation procedure.…”

Section: B Artificial Intelligencementioning

confidence: 99%

Cognitive Radio: Forging ahead from Concept, Testbed to Large-Scale Deployment

Zhao

Morales-Tirado²

2012

JCM

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This article first briefly reviews the basic concepts about cognitive radio (CR) technology and CRenabled cognitive wireless communications, and then discusses the differentiating features of CR and the fundamental theories or principles behind CR, especially from the artificial intelligence and machine learning perspective. As a CR testbed plays an important role in the transition of CR technology from concept to large-scale deployment, a survey on the state-of-the-art development on CR testbeds is presented, which indicates the trend of future development. Emerging research topics and potential applications of CR technology in various areas are also discussed which could be further investigated in an innovative CR approach.

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Development of Radio Environment Map Enabled Case- and Knowledge-Based Learning Algorithms for IEEE 802.22 WRAN Cognitive Engines

Cited by 31 publications

References 5 publications

From Self-Organizing to Cognitive Networks: How Can the Cellular Network Operator Make Use of the Cognitive Paradigm?

From Self-Organizing to Cognitive Networks: How Can the Cellular Network Operator Make Use of the Cognitive Paradigm?

Improving the Reliability of Unmanned Aircraft System Wireless Communications through Cognitive Radio Technology

Cognitive Radio: Forging ahead from Concept, Testbed to Large-Scale Deployment

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