Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.

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Cognitive radio: brain-empowered wireless communications

Wireless Communications

Statistics for Spatial Data.

An Integrated Agent Architecture for Software Defined Radio. Rapid-prototype cognitive radio, CR1, was developed to apply these.The modern software defined radio has been called the heart of a cognitive radio. Cognitive radio: an integrated agent architecture for software defined radio. Http:bwrc.eecs.berkeley.eduResearchMCMACR White paper final1.pdf. The cognitive radio, built on a software-defined radio, assumes. Radio: An Integrated Agent Architecture for Software Defined Radio, Ph.D. The need for software-defined radios is underlined and the most important notions used for such. Mitola III, Cognitive radio: an integrated agent architecture for software defined radio, Ph.D. This results in the set-theoretic ontology of radio knowledge defined in the. Cognitive Radio An Integrated Agent Architecture for Software.This article first briefly reviews the basic concepts about cognitive radio CR. Cognitive Radio-An Integrated Agent Architecture for Software Defined Radio. Cognitive Radio RHMZ 2007. Software-defined radio SDR idea 1. Cognitive radio: An integrated agent architecture for software.Cognitive Radio SOFTWARE DEFINED RADIO, AND ADAPTIVE WIRELESS SYSTEMS2 Cognitive Networks. 3 Joseph Mitola III, Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio Stockholm.

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Cognitive Radio An Integrated Agent Architecture for Software Defined Radio

New research directions will lead to fundamental changes in the design of future fifth generation (5G) cellular networks. This article describes five technologies that could lead to both architectural and component disruptive design changes: device-centric architectures, millimeter wave, massive MIMO, smarter devices, and native support for machine-to-machine communications. The key ideas for each technology are described, along with their potential impact on 5G and the research challenges that remain.

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Five disruptive technology directions for 5G

Most cellular radio systems provide for the use of transmitter power control to reduce cochannel interference for a given channel allocation. Efficient interference management aims at achieving acceptable carrier-to-interference ratios in all active communication links in the system. Such schemes for the control of cochannel interference are investigated. The effect of adjacent channel interference is neglected. As a performance measure, the interference (outage) probability is used, i.e., the probability that a randomly chosen link is subject to excessive interference. In order to derive upper performance bounds for transmitter power control schemes, algorithms that are optimum in the sense that the interference probability is minimized are suggested. Numerical results indicate that these upper bounds exceed the performance of conventional systems by an order of magnitude regarding interference suppression and by a factor of 3 to 4 regarding the system capacity. The structure of the optimum algorithm shows that efficient power control and dynamic channel assignment algorithms are closely related. >

Performance of optimum transmitter power control in cellular radio systems

Distributed power control algorithms that use only the carrier-to-interference ratios (C/I ratios) in those links actually in use are investigated. An algorithm that successfully approximates the behavior of the best known algorithms is proposed. The algorithm involves a novel distributed C/I-balancing scheme. Numerical results show that capacity gains on the order of 3-4 times can be reached also with these distributed schemes. Further, the effects of imperfect C/I estimates due to noise vehicle mobility, and fast multipath fading are considered. Results show that the balancing procedure is very robust to measurement noise, in particular if C/I requirements are low or moderate. However, for required high C/I levels or for a rapidly changing path loss matrix, convergence may be too slow to achieve substantial capacity improvements. >

Distributed cochannel interference control in cellular radio systems

This paper describes a centralized power control scheme for cellular mobile radio systems. The power for the mobiles in the proposed scheme is computed based on signal strength measurements. All the mobiles using the same channel in this scheme will attain a common carrier-to-interference ratio. The proposed scheme is analyzed and shown to have an optimal solution. >

Centralized power control in cellular radio systems

From the Book:
The development in the field of wireless communications has been nothing short of astonishing in the past decades. We now are witnessing the transition between the mobile telephone era and the era of wireless computing. With the breakthrough advances of digital signal processing high data rate, many of the technical problems associated with the adverse and changing propagation conditions in mobile radio communication have been solved. Multimegabit data rates to portable mobile terminals are no longer science fiction, but reality. As the engineer seems to have the upper hand in this struggle against nature, very much of the development efforts are concentrated on the social struggle for scarce resources, such as the frequency spectrum and terminal battery power. The question is not only if we, as engineers, can provide wideband wireless communication everywhere, but rather, if we can afford it. These issues, the Radio Resource Management (RRM) problem are addressed in this book.
Chapter 1 provides a brief introduction to the problems in resource management and an historical outlook over the field and its relations to adjacent disciplines. In Chapter 2, a refresher of multi-user communication theory is given. Readers with a solid background in this area may omit the first section of this chapter. The last section of Chapter 2 introduces the analysis model used throughout the book. In Chapter 3, the radio resource management problem is defined in a more stringent way. Chapter 4 is probably the core chapter of the book where static resource management and cellular system design is introduced and the RUNE tool is put to work for the first time. In Chapters 5-7, various elements of resource management, such as handoffs, dynamic channel allocation, and transmitter power control are treated. Whereas the treatment in Chapters 1-7 mainly has been focused on F/TDMA type (orthogonal) waveforms, Chapters 8 and 9 deal with spread spectrum waveforms and the special problems in RRM for these systems. In particular Chapter 9, where resource management issues in CDMA systems are dealt with, is an important chapter in the book. Chapter 10 brings the focus on to RRM for data communication systems and the special characteristics and problems related to management of packet traffic. In Chapter 11, various aspects of system planning are investigated, in particular so-called "hierarchical cell structures" (HCS). The last chapter, Chapter 12, widens the scope of RRM to consider resources other than the frequency spectrum. Here also other resources are taken into account, mainly the infrastructure of fixed networks and wireless access ports. Throughout the book, examples from the application of RRM techniques, current 2G systems, as well as future 3G systems are provided.
The book is intended as a textbook for a second graduate course in wireless networks. The student and reader should be familiar with the fundamentals of radio communications, communication theory, and some queuing theory basics. Wireless networks are complicated systems, which makes the design and performance analysis inherently difficult. Several approaches are taken in the book. Classical analysis involves highly simplified models but renders easily tractable results. Slightly more elaborate models are analyzed by means of numerical analysis. The most interesting results, however, are those derived from the more realistic models for propagation and traffic conditions. Here, stochastic simulation has been the tool of choice in the field. For this purpose, RUNE, a MATLAB-based software tool for performance analysis in wireless networks, has been included in the book. This tool was originally developed at Ericsson, but has been developed by the software authors for pedagogical use. Most of the examples in the book that require simulations have been solved using this tool. The software solutions for these examples have been provided on the CD, enabling the reader to repeat and modify the experiments in the book. In addition, a number of problems are provided for each chapter. Some of the problems are marked with an asterisk and require simulation solutions with the RUNE tools. Most of the material has been used in courses given by the authors at the Royal Institute of Technology (KTH) in Stockholm, Sweden, and in various in-house courses at Ericsson.
The authors would like to thank the software authors, Magnus Almgren at Ericsson, who is the original architect of RUNE, and Olav Queseth at KTH, who made RUNE "student friendly," for their valuable contributions and discussions around the simulation examples in the book. We would also like to acknowledge the help received in the development of the course material and early versions of the manuscript. In particular we are grateful for the contributions of Magnus Frodigh, Hakan Olofsson, Anders Furuskar, and Sverker Magnusson at Ericsson Radio Systems. Thanks to all graduate students at the Radio Communication Systems laboratory at KTH that have been instrumental in solving and designing many of the problems. Last but not least, we would like to extend our gratitude to the anonymous reviewer whose valuable comments have helped us to clarify some of the more intricate concepts in the book.

Jens Zander
Seong-Lyun Kim

Radio Resource Management for Wireless Networks

High system capacities can be achieved by controlling the transmitter power in multiuser radio systems. Power control with no constraint on the maximum power level has been studied extensively in earlier work [1–18]. Transmitter power is at a premium in radio systems such as cellular systems and PCS. There is a limit on the maximum transmitter power especially at the terminals (e.g. mobile units and handsets) since the power comes from a battery. In this paper we study power control that maximizes the minimum carrier to interference ratio (CIR), with a constraint on the maximum power. The optimal power vector solution lies on the boundary of the constrained power vector set and achieves a balance in the CIR's. Results indicate that the constraints do not induce any stability problems. A distributed scheme with favourable convergence properties and close to optimum performance is presented. Simulation results show that the algorithm tries to maximize the number of terminals served with CIR greater than or equal to the target CIR, while conserving power.

Jens Zander

Papers

Performance of optimum transmitter power control in cellular radio systems

Distributed cochannel interference control in cellular radio systems

Centralized power control in cellular radio systems

Radio Resource Management for Wireless Networks

Constrained power control