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A Predictive Model for Cognitive Radio

Such a model is needed to optimize performance in a software-defined radio.

A computational model that predicts effects of changing operational parameters of a cognitive radio has been developed as part of continuing research on cognitive / software - defined radio (C/SDR) data-communication networks. The term "cognitive radio," "software-defined radio," or "smart radio" denotes a radio transmitter, receiver, or transceiver, (1) much of the functionality of which is implemented in software and (2) that is able to reason about its configuration on the basis of requirements and its environment.

This Regression Tree is an example of a submodel for determining system configurations that yield various amounts of latency in a software-defined radio.
C/SDR has been made feasible by advances in digital data processing and communication electronics that have resulted in faster, smaller, and cheaper electronic devices: Emerging dataprocessor technology has enabled radio systems that traditionally have been implemented in custom silicon to migrate to general-purpose processors. C/SDR radio systems can, potentially, make more efficient use of the available radio-frequency (RF) spectrum and adapt to a wide range of protocols and environments. One of the key benefits of C/SDR is the ability to change operational parameters in response to changes in requirements and in the RF environment. A predictive computational model — perhaps such as the one reported here — is needed to enable a C/SDR to dynamically modify its own configuration in order to improve the performance of the overall communication system of which it is a part.

Given a set of possible system configurations and environmental conditions, a suitable model should be able to predict which configuration enables satisfaction of requirements, which could include a specified throughput or latency. Such prediction is expected to be part of a continuous, repetitive process: In a typical envisioned application (1) the model would be used to configure a software-defined radio; (2) that radio would be used for communication, and data on the achieved throughput or latency and other aspects of performance would be recorded; then (3) the recorded data would be used as input by a prediction mechanism to derive a new predictive model.

Thus far, in the development of a suitable predictive model, it has been found that multilinear regression submodels are useful for predicting bandwidth (and, hence, throughput). The use of multilinear regression submodels for this purpose is most easily understood in terms of, and has many characteristics in common with, design of experiments (DOE), which provides for performing structured experiments to explore the parameter space. In the experiments, the input parameters are permuted and results of the experiments are analyzed with respect to statistical significance.

It also has been found that the use of multilinear regression submodels is not sufficient for predicting latency. A regression tree, which is similar to a decision tree, was found to be better suited for this purpose. In a regression tree, predictions are made by "splitting" categories of data within nodes in such a way that they maximally reduce the variance. The figure shows an example of a regression tree. In using the tree, one would single out those leaves that satisfy one's latency requirements. The set of configurations for satisfying these requirements could be determined by tracing from the affected leaf nodes back to the root. [In this example, two leaves (5.25 and 5.38 ms) are candidates for satisfying the requirement for latency of less than 6 ms. For 5.38ms, the data rate would be 2 Mb/s and the frame size would be medium.] This process of tracing would be repeated for all affected leaf nodes to obtain the set of configurations that would satisfy the requirements. The regression-tree submodel of latency could be combined with the multilinear regression submodel of throughput (or with another such submodel for satisfying a different requirement such as minimizing transmitter power) to reduce the set of candidate configurations to those that best satisfy overall communication requirements.

This work was done by Troy Weingart, Douglas C. Sicker, and Dirk Grunwald of the University of Colorado for the Air Force Research Laboratory. For further information, download the free white paper at www.defensetechbriefs.com  under the Software category. AFRL-0002

Topics:
Communication systems Computer simulation Computer software and hardware Electronic equipment Medical, health and wellness Medical, health, and wellness Mental processes Optimization Psychiatry and psychology Radio equipment Simulation and modeling Software Telecommunications
This Brief includes a Technical Support Package (TSP).
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A Predictive Model for Cognitive Radio

(reference AFRL-0002) is currently available for download from the TSP library.

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    Defense Tech Briefs Magazine

    This article first appeared in the February, 2007 issue of Defense Tech Briefs Magazine (Vol. 1 No. 1).

    Read more articles from the archives here.

    Overview

    The document titled "A Predictive Model for Cognitive Radio" by Troy Weingart and colleagues from the University of Colorado at Boulder presents a comprehensive exploration of cognitive radio (CR) technology and its potential applications, particularly in optimizing spectrum usage. Cognitive radios are defined as radios that can adapt their operation based on environmental conditions and user requirements, making them particularly valuable in dynamic communication scenarios.

    The report emphasizes the importance of cross-layer optimization in cognitive radio networks, which involves enhancing performance metrics such as throughput, Quality of Service (QoS), and energy consumption by considering interactions between different layers of the protocol stack. The authors highlight the challenges associated with cross-layer design, including the risk of creating complex and inefficient systems, often referred to as "spaghetti design." They argue that while cross-layer optimization can yield performance improvements, it is crucial to understand the significance of these enhancements and the interactions among various system parameters.

    A key focus of the document is the development of a predictive model that can configure cognitive radios to achieve specific performance goals, such as desired throughput or latency. The authors propose a continuous process for refining this model, which involves collecting performance data from the cognitive radio system and using it to inform future configurations. This iterative approach allows for the adaptation of the system in response to changing environmental conditions and user demands.

    The report also discusses the Design of Experiments (DOE) methodology, which is employed to identify significant factors affecting system performance. DOE allows researchers to systematically explore the relationships between input parameters and performance metrics, providing a structured framework for optimizing cognitive radio configurations.

    In addition to the theoretical aspects, the document outlines practical applications of cognitive radio technology, particularly in the context of emergency communications and homeland security. The authors argue that effective use of cognitive radios can significantly improve the efficiency of spectrum allocation, especially in critical situations where traditional communication channels may be congested or unavailable.

    Overall, the document serves as a foundational piece for understanding the complexities of cognitive radio systems and the methodologies for optimizing their performance through predictive modeling and cross-layer optimization. It underscores the potential of cognitive radios to revolutionize wireless communication by enabling more flexible and efficient use of the radio spectrum.

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