Rakesh Rajbanshi

KUAR: A Flexible Software-Defined Radio Development Platform

In this paper, we present the details of a portable, powerful, and flexible software-defined radio development platform called the Kansas University Agile Radio (KUAR). The primary purpose of the KUAR is to enable advanced research in the areas of wireless radio networks, dynamic spectrum access, and cognitive radios. The KUAR hardware implementation and software architecture are discussed in detail. Radio configurations and applications are presented. Future research made possible by this flexible platform is also discussed.

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.

Parametric Adaptive Spectrum Sensing Framework for Dynamic Spectrum Access Networks

The opportunistic usage of the spectrum must be done without causing any interference to the licensed spectrum users. Conventional non-adaptive wideband spectrum sensing approaches could potentially be inefficient since they generally employ the same scanning resolution, even though the spectrum might contain different types of signals, individually requiring scans with different resolutions. In this paper, we present a novel spectrum sensing framework that adapts its parameters across the spectrum of interest according to the characteristics of its occupancy. We also propose a dynamic scheduling algorithm for spectrum sensing which allocates different time resolutions to different portions of the spectrum. We demonstrate that the proposed algorithm improves the efficiency of spectrum sensing over a non-adaptive approach.

Quantitative Comparison of Agile Modulation Techniques for Cognitive Radio Transceivers

In this paper, we present a quantitative comparison of two agile modulation techniques employed by cognitive radio transceivers operating in a dynamic spectrum access (DSA) network. One of the modulation technique is non-contiguous orthogonal frequency division multiplexing (NC-OFDM), which is designed to avoid interference with the transmissions of incumbent users by deactivating subcarriers within their vicinity. The other modulation technique under study is a variant of multicarrier code division multiple access (MC-CDMA). Although several studies comparing conventional OFDM and MC-CDMA has been conducted in literature to justify robust error performance of MCCDMA, a quantitative performance evaluation of these schemes has not been performed when employed in a DSA network. Due to deactivated subcarriers in DSA networks, in this paper we showed their performance can be significantly different from the conventional setup. Analytical expressions for the error probability of an NC-OFDM transceiver have been derived and compared with computer simulation results. The results show that the error robustness of NC-OFDM is relatively constant regardless of the number of deactivated subcarriers, unlike MC-CDMA transmissions, whose error performance degrades with an increase in deactivated subcarriers.

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.

Feasibility of Dynamic Spectrum Access in Underutilized Television Bands

This paper presents the preliminary results from a feasibility study regarding the operation of secondary spectrum users within unused television spectrum. Television spectrum is known within the wireless communications community as being underutilized, making it a prime candidate for dynamic spectrum access. Nevertheless, the quality of this spectrum for enabling secondary transmissions has never been assessed. Two unique scenarios are examined:(i) the possibility of unlicensed devices interfering with digital TV reception, and (ii) the possibility of secondary users experiencing interference when operating within close proximity to television towers. With respect to the former, we investigate the critical operating parameters for developing the technical rules for device operation in bands adjacent to a digital television transmission. Regarding the latter, we examine, via measurement campaign, how non-ideal transmission properties of television broadcasts, including intermodulation and saturation effects, can potentially impair the performance of secondary transmissions.

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.

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.

COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS - An Agile Radio for Wireless Innovation

We present the details of a portable, powerful, and flexible software-defined radio development platform called the Kansas University Agile Radio (KUAR). The primary purpose of the KUAR is to enable advanced research in the areas of wireless radio networks, dynamic spectrum access, and cognitive radios. We describe the KUAR hardware implementation and software architecture and present example application of the KUAR to modulation, spectrum measurement, channel estimation, and rapid configuration and adaptation. We outline research directions enabled by the KUAR