Vol. 64, n° 7-8, July-August 2009
Content available on SpringerLink
Christophe Moy, Supélec, France
Linda Doyle, Trinity College, Ireland
Yukitoshi Sanada, Keio University, Japan
Christophe Moy, Linda Doyle, Yukitoshi Sanada
Cognitive radio architecture evolution
Joseph Mitola III
Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ 03070, USA
Abstract: Cognitive radio research has developed dynamic radio spectrum management to enhance spectrum efficiency, e.g., as secondary users in unused TV bands. The location and user context of the mobile wireless user that regulatory bodies and lawmakers view as significant to spectrum interference policies have not been addressed as thoroughly. In addition, quality of service (QoS) provides a starting point but does not guarantee quality of experience (QoE) that depends on quality of information (QoI) which is a function of place, time, and user state in a social setting (e.g., commuting, shopping, or in need of medical assistance). This paper considers the evolution of cognitive radio architecture (CRA) from dynamic spectrum access (DSA) to QoE via an interdisciplinary perspective. Machine perception in visual, acoustic, speech, and text domains can cue the automatic detection of user state in stereotypical situations, enabling cognitive nodes and networks to select from among radio bands and modes more appropriately, thus enabling cognitive wireless networks (CWNs) to deliver higher QoE within technical policy constraints, in a way that makes cost-effective use of embedded and distributed computational intelligence. The control of networks of such cognitive radios requires advances in policy language architectures, so this paper introduces cognitive linguistics for policy languages.
Keywords Cognitive radio architecture . Dynamic spectrum access . Cognitive wireless networks . Quality of experience . Location-based propagation modeling . Quality of information
A generic cognitive framework for supervising the radio dynamic reconfiguration: An AI approach based on design problem classification
Nicolas Colson* · Apostolos Kountouris* · Armelle Wautier** · Lionel Husson**
* Orange Labs, Meylan, France
** SUPELEC, Gif-sur-Yvette, France
Abstract Cognitive radio is a technological concept pushing for the introduction of intelligent radio operation that goes beyond traditional system adaptation. So far, a rather limited amount of work has been published on the cognitive mechanisms that should be embedded into communicating equipments to achieve such an intelligent behavior. This paper presents a generic cognitive framework for autonomous decision making with regard to multiple, possibly conflicting, operational objectives in a time-varying environment. The framework is based on the definition of two scales introducing order relationships between the configurations that help the reasoning and learning processes. The resulting cognitive engine learns to progressively identify the optimal configurations for the design objectives imposed given the current radio environment. The proposed approach is illustrated for a case of cognitive waveform design and extensive simulation results validate the cognitive engine behavior.
Keywords Cognitive radio · Autonomous decisions making · Waveform design · Evolving connectionist systems
An executable meta-model of a hierarchical and distributed architecture management for cognitive radio equipments
Loïg Godard, Christophe Moy, Jacques Palicot
SUPELEC/IETR, Rennes, France
Abstract A cognitive radio (CR) equipment is a radio device that supports the smart facilities offered by future cognitive networks. Even if several categories of equipments exist (terminal, base station, smart PDA, etc.), with their different processing capabilities (and associated cost or power consumption), this means that apart from the usual radio signal processing elements, these equipments have to integrate also a set of new capabilities for the CR support; this implies not only radio adaptation and sensing capabilities. We assert that it is necessary to add some management facilities for that, and we propose here an architecture management to be inserted inside CR equipments named thereafter Hierarchical and Distributed Cognitive Architecture Management (HDCRAM). This approach is based upon a Hierarchical and Distributed Reconfiguration Management (HDReM), which is derived from our previous research on software-defined radio. The HDCRAM extends the HDReM towards CR while adding new management features, in order to support sensing and decision-making facilities. It consists in the combination of one Cognitive Radio Management Unit (CRMU) with each reconfiguration management unit distributed in the equipment. Each of these CRMU is in charge of the capture, the interpretation, and the decision making according to its own goals. In this cognitive radio context, the term “decision” refers to the adaptation of the radio parameters to the equipment environment. This paper details the management functionality and structure of the HDCRAM. Moreover, this architecture has also been modeled with a metaprogramming language based on UML. The first goal is to propose a comprehensive specification of the CR management of future CR equipments. Beyond this objective, we have also derived a simulator from the obtained meta-model, which gives the opportunity to specify CR needs and play a wide variety of scenarios in order to validate the CR design. The example of a Blind Standard Recognition CR scenario illustrates the relevance of this approach.
Keywords Cognitive radio . Cognitive radio equipments design . Cognitive radio management . Meta-model
Enhanced network selections in a cognitive wireless B3G world
University of Piraeus, Piraeus, Greece
Abstract A network operator of the wireless beyond the third generation (B3G) era will need to be directing terminals to the most appropriate radio networks of its heterogeneous infrastructure. This requires advanced terminal management functionality for conducting optimal network selections, in a seamless to the user manner. This paper enhances network selection schemes by developing functionality for learning the quality of service (QoS) capabilities of candidate networks and by exploiting the acquired knowledge in the network selection. The resulting scheme selects networks that offer the best QoS, taking into account the user needs, policies, the context of operation, and the knowledge. The advantage is that the reliability of the knowledge-based decisions is increased. This enhances the probability of offering the desired QoS to the user and reduces some of the related operational overheads in the network. The learning functionality is influenced by Bayesian networks. The computational effort for the development of the knowledge is shown to be minimal. Results show the behavior of the scheme in terms of knowledge development, respective computational effort required, and network selections made. In general, the paper contributes to the introduction of cognitive systems in the wireless B3G world.
Keywords Terminal management functionality . Learning . Bayesian networks
Local control of cognitive radio networks
Christian Doerr, Dirk Grunwald, Douglas C. Sicker
University of Colorado at Boulder, USA
Abstract In a network deployment, a cognitive radio will have to perform two fundamental tasks. First, each cognitive radio needs to optimize its internal operation, and second, it needs to derive a configuration that will enable and optimize communication with other nodes in the network. This latter requirement, however, relies on knowledge about the other nodes’ current configuration settings, which needs to be incorporated into this decision-making process. Collecting and distributing such global knowledge is, however, a difficult and costly process, which, in the past, has been approached by introducing a centralized control authority, distributed negotiation policies, or a dedicated coordination channel in the network, each resulting in vulnerability and scaling issues. In this paper, we propose an alternative approach to the global configuration of a cognitive radio network that eliminates the need to collect global network state information and, instead, uses local information for its decision making process. This technique is built upon the principles of swarm intelligence, as seen in schools of fish and flocks of birds, and allows for efficient and robust coordination of a cognitive radio network in a variety of tasks. We have implemented a working prototype showing the feasibility of this technique in two simulation environments and in a hardware testbed, and find that a solution based on swarm intelligence is well suited to interoperate in heterogeneous deployment environments with other control algorithms, requires low computational overhead, and scales with the number of nodes and the amount of spectrum, thus making it a versatile control algorithm for many deployment scenarios.
Keywords Cognitive radio networks · Swarm intelligence · Emergent behavior · Local independent control · Dynamic spectrum access
Traffic modelling and forecasting using genetic algorithms for next-generation cognitive radio applications
Duminda Thilakawardana, Klaus Moessner
*University of Surrey, Guildford, UK
Abstract This article presents a genetic-algorithm-based prediction model for forecasting traffic demands of nextgeneration wireless networks that are expected to be chaotic in nature. The model approximates the best-fit mathematical equation that generates a given time series using a genetic algorithm. It estimates future traffic in wireless networks using the most recent traffic data points collected from the actual network. Such estimations will be beneficial for network operators helping to manage and optimise the limited radio resources efficiently and eventually to facilitate cognitive radio applications. The new model is compared with conventional regressions analysis and exponential smoothing models, and it has been found that the genetic algorithm model successfully recovers the underlying mathematical expression describing chaotic time series in less than 200 generations and the predictions achieved are by far better than those of regression and exponential smoothing models. The model also offers benefits for in cognitive communication systems with their intrinsic learning capabilities and distributed access decisions.
Keywords Genetic algorithm · Wireless network · Cognitive radio application
Power allocations in minimum-energy SER-constrained cooperative networks
Behrouz Maham1,2 · Are Hjørungnes1 · Mérouane Debbah3
1 University of Oslo, Norway
2 Stanford University, USA
3 SUPÉLEC, Gif-sur-Yvette, France
Abstract In this paper, we propose minimum power allocation strategies for repetition-based amplify-andforward (AF) relaying, given a required symbol error rate (SER) at the destination. We consider the scenario where one source and multiple relays cooperate to transmit messages to the destination. We derive the optimal power allocation strategy for two-hop AF cooperative network that minimizes the total relay power subject to the SER requirement at the destination. Two outstanding features of the proposed schemes are that the power coefficients have a simple solution and are independent of knowledge of instantaneous channel state information (CSI). We further extend the SER constraint minimum power allocation to the case of multibranch, multihop network and derive the closedform solution for the power control coefficients. For the case of power-limited relays, we propose two iterative algorithms to find the power coefficients for the SER constraint minimum-energy cooperative networks. However, this power minimization strategy does not necessarily maximize the lifetime of battery-limited systems. Thus, we propose two other AF cooperative schemes which consider the residual battery energy, as well as the statistical CSI, for the purpose of lifetime maximization. Simulations show that the proposed minimum power allocation strategies could considerably save the total transmitted power compared to the equal transmit power scheme.
Keywords Power allocation strategies · Symbol error rate · Constrained cooperative networks
An optimal OSA approach based on channel-usage estimate under collision probability constraint in cognitive radio systems
Qinghai Xiao*,** Yunzhou Li*, Ming Zhao*, Shidong Zhou*, Jing Wang*
* Tsinghua University, Beijing, China
** Information Engineering University, Zhengzhou, China
Abstract Opportunistic spectrum access system allows the secondary user to access spectrum holes not being utilized by the primary user. Traditional opportunistic spectrum access approaches only sense and utilize current spectrum holes. This can result in uncontrollable collision probability, which exceed the maximum collision probability allowed by the primary user network. In this paper, we consider a cognitive radio system with one primary channel and one secondary user, and then, we introduce a channel-usage pattern model and a fundamental access scheme in this system. Based on the fundamental access scheme, we adopt fixed detection duration and transmission duration ratio approach to analyze what and how to determine spectrum holes utilization and collision probability in this model. On the basis of this model and fundamental access scheme, we study optimal opportunistic spectrum access problem and formulate it as an optimization problem that the secondary user maximizes spectrum holes utilization under the constraint of collision tolerable level, and then we solve this optimization problem in two cases: one is that the idle period is exponential distribution, the other is that the idle period is Pareto distribution. According to the solution of the optimization problem, we respectively propose an optimal opportunistic spectrum access algorithm in each case. Theoretical analysis and simulation results both show that the optimal opportunistic spectrum access algorithms can maximize spectrum holes utilization under the constraint that the collision probability is bounded below collision tolerable level.
Keywords Opportunistic spectrum access . Spectrum holes utilization . Collision probability . Collision tolerable level
Dynamic spectrum access using the interference temperature model
T. Charles Clancy
*University of Maryland, USA
Abstract To combat spectral overcrowding, the FCC investigated new ways to manage RF resources. The idea was to let people use licensed frequencies, provided they can guarantee interference perceived by the primary license holders will be minimal.With advances in software and cognitive radio, practical ways of doing this are on the horizon. In 2003, the FCC released a memorandum seeking comment on the interference temperature model for controlling spectrum use. Analyzing the viability of this model and developing a medium access protocol around it are the main goals of this article. A model consisting of interference sources, primary licensed users, and secondary unlicensed users is modeled stochastically. If impact on licensed users is defined by a fractional decrease in coverage area, and this is held constant, the capacity achieved by secondary users is directly proportional to the number of unlicensed nodes, and is independent of the interference and primary users’ transmissions. Using the basic ideas developed in the system analysis, interference temperature multiple access, a physical and data-link layer implementing the interference temperature model, was formulated, analyzed, and simulated. A system implementing this model will measure the current interference temperature before each transmission. It can then determine what bandwidth and power it should use to achieve a desired capacity without violating an interference ceiling called the interference temperature limit. Ultimately, the resulting performance from the interference temperature model is low, compared to the amount of interference it can cause to primary users. Partly due to this research, in May 2007, the FCC rescinded its notice of proposed rule-making implementing the interference temperature model.
Keywords Spectral overcrowding · Dynamic spectrum access · Interference temperature model