Gary J. Minden

A survey of active network research

Active networks are a novel approach to network architecture in which the switches (or routers) of the network perform customized computations on the messages flowing through them. This approach is motivated by both lead user applications, which perform user-driven computation at nodes within the network today, and the emergence of mobile code technologies that make dynamic network service innovation attainable. The authors discuss two approaches to the realization of active networks and provide a snapshot of the current research issues and activities. They illustrate how the routers of an IP network could be augmented to perform such customized processing on the datagrams flowing through them. These active routers could also interoperate with legacy routers, which transparently forward datagrams in the traditional manner.

Sidelobe Suppression for OFDM-Based Cognitive Radios Using Constellation Expansion

In this paper, we present a novel algorithm for reducing sidelobe interference power levels in OFDM-based cognitive radios. Existing techniques for sidelobe suppression can be computationally intensive when determining the complex-valued amplitude levels for the cancellation subcarriers. Exploiting the fact that different sequences have different sidelobe power levels, the proposed algorithm employs a constellation expansion-based iterative approach in order to suppress the sidelobe power levels. An important advantage of the proposed technique is that, no side information needs to be transmitted. Simulation results show that the proposed algorithm can be employed in a wide range of operating conditions at the cost of a slight increase in the bit error rate and the peak-to-average power ratio characteristics.

A Spectrum Surveying Framework for Dynamic Spectrum Access Networks

Dynamic spectrum access networks and wireless spectrum policy reforms heavily rely on accurate spectrum utilization statistics, which are obtained via spectrum surveys. In this paper, we propose a generic spectrum-surveying framework that introduces both standardization and automation to this process, as well as enables a distributed approach to spectrum surveying. The proposed framework outlines procedures for the collection, analysis, and modeling of spectrum measurements. Furthermore, we propose two techniques for processing spectrum data without the need for a priori knowledge. In addition, these techniques overcome the challenges associated with spectrum data processing, such as a large dynamic range of signals and the variation of the signal-to-noise ratio across the spectrum. Finally, we present mathematical tools for the analysis and extraction of important spectrum occupancy parameters. The proposed processing techniques have been validated using empirical spectrum measurements collected from the FM, television (TV), cellular, and paging bands. Results show that the primary signals in the FM band can be classified with a miss-detection rate of about 2% at the cost of 50% false-alarm rate, while nearly 100% reliability in classification can be achieved with the other bands. However, the classification accuracy depends on the duration and the range of frequencies over which data are collected, as well as the RF characteristics of the spectrum measurement receiver.

Population Adaptation for Genetic Algorithm-based Cognitive Radios

Genetic algorithms are best suited for optimization problems involving large search spaces. The problem space encountered when optimizing the transmission parameters of an agile or cognitive radio for a given wireless environment and set of performance objectives can become prohibitively large due to the high number of parameters and their many possible values. Recent research has demonstrated that genetic algorithms are a viable implementation technique for cognitive radio engines. However, the time required for the genetic algorithms to come to a solution substantionally increases as the system complexity grows. In this paper, we present a population adaptation technique for genetic algorithms that takes advantage of the information from previous cognition cycles in order to reduce the time required to reach an optimal decision. Our simulation results demonstrate that the amount of information from the previous cognition cycle can be determined from the environmental variation factor (EVF), which represents the amount of change in the environment parameters since the previous cognition cycle.

Population adaptation for genetic algorithm-based cognitive radios

Genetic algorithms are best suited for optimization problems involving large search spaces. The problem space encountered when optimizing the transmission parameters of an agile or cognitive radio for a given wireless environment and set of performance objectives can become prohibitively large due to the high number of parameters and their many possible values. Recent research has demonstrated that genetic algorithms are a viable implementation technique for cognitive radio engines. However, the time required for the genetic algorithms to come to a solution substantially increases as the system complexity grows. In this paper, we present a population adaptation technique for genetic algorithms that takes advantage of the information from previous cognition cycles in order to reduce the time required to reach an optimal decision. Our simulation results demonstrate that the amount of information from the previous cognition cycle can be determined from the environmental variation factor, which represents the amount of change in the environment parameters since the previous cognition cycle.

A framework for R.F. spectrum measurements and analysis

Dynamic spectrum access networks and spectrum policy depend on accurate spectrum utilization statistics. This paper presents the architecture of a collaborative framework to measure, characterize and model the utilization of the spectrum. It provides statistical methods to classify measurements as either signal or noise. Finally, it introduces algorithms for estimating signal thresholds and spectrum occupancy, and presents a performance evaluation to test the accuracy of the algorithms

Peak-to-Average Power Ratio Analysis for NC-OFDM Transmissions

In this paper, we present a statistical analysis of the peak-to-average power ratio (PAPR) for non-contiguous orthogonal frequency division multiplexing (NC-OFDM) signals. When studying contiguous OFDM signals, most PAPR analysis techniques assume the symbols to be identically and independently distributed (i.i.d.). However, in an NC-OFDM transmission, where a large number of subcarriers could be deactivated, this assumption is no longer valid. The proposed PAPR analysis is derived specifically for the NC-OFDM transmission scenario. Results show that NC-OFDM signal exhibit higher PAPR values relative to contiguous OFDM transmission at the same information rate.

Adaptive-Mode Peak-to-Average Power Ratio Reduction Algorithm for OFDM-Based Cognitive Radio

In this paper, we present a novel low complexity algorithm for reducing the peak-to-average power ratio (PAPR) occurring in OFDM-based cognitive radios. Although several PAPR reduction algorithms exist in the literature, they are often only effective for specific scenarios. Our proposed algorithm exploits the agility of cognitive radio technology to rapidly choose and employ the appropriate PAPR reduction approach from a set of approaches to achieve a large decrease in PAPR, given the current operating conditions. The results show that for a wide range of operating conditions, the proposed algorithm achieves a large decrease in PAPR, unlikely the PAPR results when only a single reduction approach is employed across the same wide range.

Networking on the battlefield: challenges in highly dynamic multi-hop wireless networks

A crucial aspect of effective networking on the battlefield is choosing the correct networking architecture. Multi-hop wireless networks provide the best model for tactical networking because of their ability to self-organize and rapidly adapt to change. We focus on a multi-hop wireless network model that is highly dynamic and that consists of mobile base stations and mobile hosts. In this model, there are two key requirements for enabling an effective networking infrastructure for the battlefield: the support of highly mobile nodes and the scalability to a large number of nodes. We present some of the system-level challenges encountered in highly dynamic multi-hop wireless networks. In particular, we address the topology model, the location model, and the routing model in light of the aforementioned challenges.

Agile Radio Implementation of OFDM Physical Layer for Dynamic Spectrum Access Research

In this paper we present the design process of an orthogonal frequency division multiplexing (OFDM) implementation for the Kansas University Agile Radio (KUAR). The KUAR is a portable, experimental, FPGA-based software-defined radio unit employed as a test bed to facilitate advanced research into frequency agile and cognitive radios. The baseband processing in this implementation is accomplished entirely on a Xilinx Virtex-II Pro FPGA that is built into the KUAR Our OFDM PHY implementation demonstrates the capabilities of the KUAR and serves as an important first step towards conducting actual experiments on multicarrier schemes for dynamic spectrum access communications, such as non-contiguous (NC)-OFDM.