Other affiliations: Synopsys
Bio: Petri Mahonen is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Cognitive radio & Wireless network. The author has an hindex of 41, co-authored 338 publications receiving 8000 citations. Previous affiliations of Petri Mahonen include Synopsys.
Papers published on a yearly basis
TL;DR: This paper provides a systematic overview on CR networking and communications by looking at the key functions of the physical, medium access control (MAC), and network layers involved in a CR design and how these layers are crossly related.
Abstract: Cognitive radio (CR) is the enabling technology for supporting dynamic spectrum access: the policy that addresses the spectrum scarcity problem that is encountered in many countries. Thus, CR is widely regarded as one of the most promising technologies for future wireless communications. To make radios and wireless networks truly cognitive, however, is by no means a simple task, and it requires collaborative effort from various research communities, including communications theory, networking engineering, signal processing, game theory, software-hardware joint design, and reconfigurable antenna and radio-frequency design. In this paper, we provide a systematic overview on CR networking and communications by looking at the key functions of the physical (PHY), medium access control (MAC), and network layers involved in a CR design and how these layers are crossly related. In particular, for the PHY layer, we will address signal processing techniques for spectrum sensing, cooperative spectrum sensing, and transceiver design for cognitive spectrum access. For the MAC layer, we review sensing scheduling schemes, sensing-access tradeoff design, spectrum-aware access MAC, and CR MAC protocols. In the network layer, cognitive radio network (CRN) tomography, spectrum-aware routing, and quality-of-service (QoS) control will be addressed. Emerging CRNs that are actively developed by various standardization committees and spectrum-sharing economics will also be reviewed. Finally, we point out several open questions and challenges that are related to the CRN design.
••03 Apr 2006
TL;DR: This paper carefully analyzes the properties and performance of IEEE 802.15.
Abstract: IEEE 802.15.4 was developed to meet the needs for simple, low-power and low-cost wireless communication. In the past couple of years it has become a popular technology for wireless sensor networks. It operates primarily in the 2.4 GHz ISM band, which makes the technology easily applicable and worldwide available. However, IEEE 802.15.4 is potentially vulnerable to interference by other wireless technologies working in this band such as IEEE 802.11 and Bluetooth. This paper gives a short overview of the IEEE 802.15.4 and carefully analyzes the properties and performance of IEEE 802.15.4 through measurement of the RSSI, PER and run lengths distribution using real off-the-shelf hardware. Furthermore we present simulation results from the evaluation of the IEEE 802.15.4 MAC protocol. Finally, we address the coexistence between IEEE 802.11 and IEEE 802.15.4 and measure the impact these two wireless technologies have on each other when operating concurrently and in range
01 Aug 2007
TL;DR: An extensive measurement campaign conducted in Aachen, Germany, comparing indoor-and outdoor measurement results is reported, confirming that the spectrum band 3-6 GHz is rarely occupied and providing a case study how the amplitude probability distribution can be used together with detailed regulatory information to infer additional information about the spectral usage.
Abstract: Dynamic spectrum access is an integral part of the Cognitive Radio paradigm. However, efficient spectrum sensing techniques are crucial on the way towards systems, which use idle spectrum bands opportunistically and increase the overall spectral efficiency. Current spectrum occupancy was evaluated in few measurement campaigns at different locations mostly located in the USA. In this paper we report about an extensive measurement campaign conducted in Aachen, Germany, comparing indoor-and outdoor measurement results. The highly sensitive measurement system enabled us to measure also man-made or ambient noise. Since an energy detector cannot differentiate such noise from other primary user signals we determine a very high spectrum occupancy in the outdoor scenario in the band from 20 MHz up to 3 GHz. Considerably less occupation was measured in the indoor scenario also because of less ambient noise. Our measurements confirm that the spectrum band 3-6 GHz is rarely occupied. We further provide a case study how the amplitude probability distribution can be used together with detailed regulatory information to infer additional information about the spectral usage. Such information is beneficial in order to optimize the spectrum sensing process and identify candidate bands for further investigation and possible secondary usage.
TL;DR: An overview of the existing solutions for service and resource discovery for a wide variety of network types is given and the various issues and complications that should be considered in future work in this domain are given.
Abstract: Service and resource discovery has become an integral part of modern networked systems. In this survey we give an overview of the existing solutions for service and resource discovery for a wide variety of network types. We cover techniques used in existing systems, as well as recent developments from the research front. We also provide taxonomy for discovery systems and architectures, and review the various algorithms and search methods applicable for such systems. Peer-to-peer overlays are discussed in detail and solutions for non-IP-based networks are also included in the review. We also specifically comment on issues related to wireless networks, and give an overview of the various issues and complications that should be considered in future work in this domain.
TL;DR: It is shown that spectrum use is clustered in the frequency domain and should be modelled in the time domain using geometric or lognormal distributions, and the listed model parameters enable accurate modelling of primary user spectrum use in time and frequency domain for future DSA studies.
Abstract: Dynamic spectrum access (DSA) has been proposed as a solution to the spectrum scarcity problem. However, the models for spectrum use, that are commonly used in DSA research, are either limited in scope or have not been validated against real-life measurement data. In this paper we introduce a flexible spectrum use model based on extensive measurement results that can be configured to represent various wireless systems. We show that spectrum use is clustered in the frequency domain and should be modelled in the time domain using geometric or lognormal distributions. In the latter case the probability of missed detection is significantly higher due to the heavy-tailed behaviour of the lognormal distribution. The listed model parameters enable accurate modelling of primary user spectrum use in time and frequency domain for future DSA studies. Additionally, they also provide a more empirical basis to develop regulatory or business models.
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
TL;DR: The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or her own research.
Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or
22 Jan 2006
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.