Sectorized antennas are a promising class of antennas for enabling direction-of-arrival (DoA) estimation and successive transmitter localization. In contrast to antenna arrays, sectorized antennas do not require multiple transceiver branches and can be implemented using a single RF front-end only, thus reducing the overall size and cost of the devices. However, for good localization performance the underlying DoA estimator is of uttermost importance. In this paper, we therefore propose a novel high performance DoA estimator for sectorized antennas that does not require cooperation between the transmitter and the localizing network. The proposed DoA estimator is broadly applicable with different sectorized antenna types and signal waveforms, and has low computational complexity. Using computer simulations, we show that our algorithm approaches the respective Cramer-Rao lower bound for DoA estimation variance if the signal-to-noise ratio (SNR) is moderate to large and also outperforms the existing estimators. Moreover, we also derive analytical error models for the underlying DoA estimation principle considering both free space as well as multipath propagation scenarios. Furthermore, we also address the fusion of the individual DoA estimates into a location estimate using the Stansfield algorithm and study the corresponding localization performance in detail. Finally, we show how to implement the localization in practical systems and demonstrate the achievable performance using indoor RF measurements obtained with practical sectorized antenna units.

Knowledge about the location of licensed primary-users (PU) could enable several key features in cognitive radio (CR) networks including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. In this paper we consider the achievable accuracy of PU localization algorithms that jointly utilize received-signal-strength (RSS) and direction-of-arrival (DoA) measurements by evaluating the Cramer-Rao Bound (CRB). Previous works evaluate the CRB for RSS-only and DoA-only localization algorithms separately and assume DoA estimation error variance is a fixed constant or rather independent of RSS. We derive the CRB for joint RSS/DoA-based PU localization algorithms based on the mathematical model of DoA estimation error variance as a function of RSS, for a given CR placement. The bound is compared with practical localization algorithms and the impact of several key parameters, such as number of nodes, number of antennas and samples, channel shadowing variance and correlation distance, on the achievable accuracy are thoroughly analyzed and discussed. We also derive the closed-form asymptotic CRB for uniform random CR placement, and perform theoretical and numerical studies on the required number of CRs such that the asymptotic CRB tightly approximates the numerical integration of the CRB for a given placement.

Knowledge about the location of licensed primary users (PU) could enable several key features in cognitive radio (CR) networks including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. In this paper we discuss the problem of localizing multiple PUs based on direction-of-arrival (DoA) measurements. The key problem in such scenario is the difficulty and complexity of associating multiple DoA measurements to different PUs. We propose an iterative method that uses the probability of associating each DoA to a PU as the weighting coefficient of a modified Stansfield algorithm for DoA fusion. Results show the proposed algorithm obtains better accuracy than previous algorithms [1]-[3], and its complexity increases linearly with the number of CRs, compared with the exponential rate for algorithms based on exhaustive search of all possible associations.

Information about primary transmitter location is crucial in enabling several key capabilities in cognitive radio networks, including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. Compared to other proposed non-interactive localization algorithms, the weighted centroid localization (WCL) scheme uses only the received signal strength information, which makes it simple to implement and robust to variations in the propagation environment. In this paper we present the first theoretical framework for WCL performance analysis in terms of its localization error distribution parameterized by node density, node placement, shadowing variance, correlation distance and inaccuracy of sensor node positioning. Using this analysis, we quantify the robustness of WCL to various physical conditions and provide design guidelines, such as node placement and spacing, for the practical deployment of WCL. We also propose a power-efficient method for implementing WCL through a distributed cluster-based algorithm, that achieves comparable accuracy with its centralized counterpart.

Fully exploiting the phase and magnitude information in received data is crucial to the detection of cognitive radio signals, and this is usually done in the time domain. In fact, the same set of information can also be exploited in the frequency domain in the form of power spectrum. The most appealing one is the multitaper spectrum (MTS) estimator, which enjoys a high estimation accuracy and relatively low computational complexity making it an attractive candidate for use in CR networks. In this paper, we investigate a MTS-based CR detector, aiming to fully understand its capability in exploiting the advantage of MTS. We obtain simple expressions for detection probability and constant false-alarm rate whereby a threshold can be easily determined. Numerical results are presented to verify the theory and to demonstrate the superior performance of the proposed scheme.