Friedrich K. Jondral

Bio: Friedrich K. Jondral is an academic researcher from Karlsruhe Institute of Technology. The author has contributed to research in topics: Cognitive radio & Software-defined radio. The author has an hindex of 37, co-authored 296 publications receiving 6835 citations.

Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency

Abstract: This article describes the technical challenges that have to be met when implementing the interesting new technology of spectrum pooling. This notion represents the coexistence of two mobile radio systems within the same frequency range. It enables the secondary utilization of already licensed frequency bands as aimed at by several regulatory authorities worldwide. The goal of spectrum pooling is to enhance spectral efficiency by overlaying a new mobile radio system on an existing one without requiring any changes to the actual licensed system. Several demanding tasks originate from this idea. Some of them have been solved in recent research projects. Others are subject to ongoing investigations. Here, the state of the art in spectrum pooling is presented.

Mutual interference in OFDM-based spectrum pooling systems

Abstract: The public mobile radio spectrum has become a scarce resource while wide spectral ranges are only rarely used. Here, the new strategy called spectrum pooling is considered. It aims at enabling public access to these spectral ranges without sacrificing the transmission quality of the actual license owners. Unfortunately, using OFDM modulation in a spectrum pooling system has some drawbacks. There is an interaction between the licensed system and the OFDM based rental system due to the non-orthogonality of their respective transmit signals. This interaction is described mathematically, providing a quantitative evaluation of the mutual interference that leads to an SNR loss in both systems. However, this interference can be mitigated by windowing the OFDM signal in the time domain or by the adaptive deactivation of adjacent subcarriers providing flexible guard bands between licensed and rental system. It is obvious that both approaches sacrifice bandwidth of the rental system. A quantitative comparison of both approaches is given as a tradeoff between interference reduction and throughput in the rental system.

Software-defined radio: basics and evolution to cognitive radio

Abstract: We provide a brief overview over the development of software-defined or reconfigurable radio systems. The need for software-defined radios is underlined and the most important notions used for such reconfigurable transceivers are thoroughly defined. The role of standards in radio development is emphasized and the usage of transmission mode parameters in the construction of software-defined radios is described. The software communications architecture is introduced as an example for a framework that allows an object-oriented development of software-defined radios. Cognitive radios are introduced as the next step in radio systems' evolution. The need for cognitive radios is exemplified by a comparison of present and advanced spectrum management strategies.

A Tractable Model for Noncoherent Joint-Transmission Base Station Cooperation

Abstract: This paper presents a tractable model for analyzing noncoherent joint-transmission base station (BS) cooperation, taking into account the irregular BS deployment typically encountered in practice. In addition to cellular-network specific aspects, such as BS density, channel fading, average path loss, and interference, the model also captures relevant cooperation mechanisms, including user-centric BS clustering and channel-dependent cooperation activation. The locations of all BSs are modeled by a Poisson point process. Using tools from stochastic geometry, the signal-to-interference-plus-noise ratio (SINR) distribution with cooperation is precisely characterized in a generality-preserving form. The result is then applied to practical design problems of recent interest. We find that increasing the network-wide BS density improves the SINR, while the gains increase with the path loss exponent. For pilot-based channel estimation, the average spectral efficiency saturates at cluster sizes of around seven BSs for typical values, irrespective of backhaul quality. Finally, it is shown that intra-cluster frequency reuse is favorable in moderately loaded cells with generous cooperation activation, while intra-cluster coordinated scheduling may be better in lightly loaded cells with conservative cooperation activation.

Calculation of detection and false alarm probabilities in spectrum pooling systems

Abstract: The innovative new strategy of spectrum pooling enables public access to spectral ranges of already licensed yet rarely used frequency bands by overlaying a secondary mobile radio system (the rental system, RS) to an existing one (the licensed system, LS). Coexistence of both systems is realized by filling the idle time-frequency gaps of the LS. A key issue in spectrum pooling is the reliable and efficient detection of those spectral ranges that are currently accessed by the LS as those ranges have to be spared from the RS's transmission power. In this letter, formulas for the calculation of the detection and false alarm probability are derived for the general case of an arbitrary measurement covariance matrix, allowing for a maximum exploitation of the proposed distributed detection approach.

Machine learning

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.).

A survey of spectrum sensing algorithms for cognitive radio applications

Abstract: The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. Challenges associated with spectrum sensing are given and enabling spectrum sensing methods are reviewed. The paper explains the cooperative sensing concept and its various forms. External sensing algorithms and other alternative sensing methods are discussed. Furthermore, statistical modeling of network traffic and utilization of these models for prediction of primary user behavior is studied. Finally, sensing features of some current wireless standards are given.

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Cognitive Radio An Integrated Agent Architecture for Software Defined Radio

Abstract: 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.