A multi-mode WPAN transceiver implemented in an SoC is presented. Fabricated in 40-nm CMOS, the chip supports IEEE 802.15.4 and all modes of Bluetooth. Consuming 7.8 mW from a 1.2-V supply, the receiver has a sensitivity of -104, -98, -95/-94/-88 dBm in 802.15.4, BLE and Bluetooth BDR/EDR2/EDR3, respectively. The transmitter has a power consumption of 10 mW to deliver 0 dBm output in the constant envelope mode and 14.5 mW in BT EDR.

Using collaborative sensors or other observing devices equipped with sectorized antennas provides a practical and low-cost solution to direction-of-arrival (DoA) and received signal strength (RSS) estimation, as well as noncooperative transmitter (TX) localization. In this paper, we study the performance and theoretical bounds of DoA/RSS estimation and localization using sectorized antennas. We first show that the sector-power measurements at an individual sensor form a sufficient statistic for DoA/RSS estimation and TX localization. Motivated by that, we then derive the Cramer-Rao bound (CRB) on DoA/RSS estimation based on sector-powers and study its asymptotic behavior. Moreover, we derive an analytical expression for the mean square error (MSE) of a practical sectorized-antenna-based DoA estimator, compare its performance to the derived CRB, and study its asymptotic properties. Next, we derive the CRB for localization based on sector-powers. The resulting CRB is a lower bound for a localization system where the DoA/RSS estimates, obtained from sector-powers at individual sensors, are fused together into a location estimate. Moreover, the CRB also covers the more general case of a localization system where the sector-powers from individual nodes are directly fused together, without an intermediate DoA/RSS estimation step. We compare the obtained CRB to a localization approach employing an intermediate DoA/RSS estimation step and observe that skipping this intermediate processing step may result in a substantially improved localization performance. Finally, we study the influence of various important system parameters, such as the number of sensors, sectors, and measurement samples, on the achievable estimation and localization performance. Overall, this paper demonstrates and quantifies the achievable DoA/RSS estimation and localization performance of sectorized antennas and provides comprehensive design guidelines for sector-power based low-complexity localization systems.

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.

In this letter, we consider the power control problem in cognitive radio (CR) networks when both primary user (PU) location and wireless channel are unknown. Prior work in power control assumes perfect knowledge of PU and CR locations, which is not practical due to localization errors and node mobility. We assume the distance estimation error in CR-PU links to model location uncertainties and derive the distribution of channel gain with distance-dependent path loss and shadowing. We then proceed to develop an optimization framework for CR power control, which maximizes the CR data rate under PU interference power constraint. Simulation results showing the CR data rate and interference probability to the PUs are presented to demonstrate the superior performance of the proposed algorithm compared with reference schemes.

In the above-named article [ibid., vol. 3, no. 2, pp. 237-240, Apr. 2014], the follwoing corrections specified in this erratum need to be made to Section III-A, page 238; Section III-A, page 238 in equations (5) and (7); and Section V, page 239 Fig. 1 is replaced with a corrected figure provided here.

In this letter we present analytical performance evaluation of primary user (PU) RSS/DoA estimation and localization through cooperating cognitive radios (CRs). We assume that the CRs are equipped with sectorized antennas, an antenna model we use to describe different types of directional antennas with a low hardware overhead (e.g. only a single RF front-end). We first derive the bias and mean square error (MSE) of RSS and DoA estimates based on the simplified least square (SLS) algorithm. We then proceed to derive the theoretical MSE of PU localization using the Stansfield algorithm that fuses SLS estimates from multiple nodes. Simulation results studying the impact of various system parameters on the RSS/DoA estimation and localization accuracy are presented to verify theoretical derivations, as well as provide design guidelines for localization systems based on sectorized antennas.

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.

Received-signal-strength (RSS) and direction-of arrival (DoA) are the sufficient measurements to solve the primary user localization problem in cognitive radio networks. In this paper we consider using energy measurements from sectorized antenna to estimate RSS and DoA of a primary user. Abstracting from practical antenna types, we define a sectorized antenna as an antenna that can be set to different operating modes, each of which resulting in a selectivity of those signals that arrive from within a certain, continuous range of angles, i.e. a sector. We first characterize the achievable performance of RSS and DoA estimations using energy measurements from sectorized antennas by means of the Cramer-Rao Bound (CRB), which provides a lower bound on the estimation accuracy of any unbiased estimator. We then propose a practical RSS and DoA estimator, namely the simplified least squares (SLS) algorithm. The SLS algorithm minimizes a cost function obtained from two largest energy measurements among all sectors, and its accuracy closely approaches the CRB. Simulation results studying the impact of important system parameters, such as SNR, number of sectors and number of samples, on the achievable accuracy specified by the CRB and the SLS algorithm are presented.

In this letter, we focus on the Stansfield localization algorithm, which is a direction-of-arrival (DoA) fusion algorithm with high accuracy and low complexity. We derive the mean square error of the Stansfield algorithm with estimated DoA estimation error variance. Our derivation considers the statistical variation of DoA, as well as the impact of receive signal strength variations and node self-positioning error. In addition, we propose a distributed implementation of the Stansfield algorithm based on diffusion adaptation, which obtains accuracy comparable to its centralized counterpart and saves total transmit power for sufficient node density.

Information about primary user (PU) location can enable several key capabilities in cognitive radio (CR) networks. In this paper we consider PU localization using received-signal-strength (RSS) and direction-of-arrival (DoA) estimates from sectorized antenna. Abstracting from practical antenna types, we define a sectorized antenna as an antenna that can be set to different operating modes, each of which resulting in a selectivity of those signals that arrive from within a certain, continuous range of angles, i.e. a sector. We propose a low complexity algorithm, the MaxE algorithm, that provides coarse RSS and DoA estimates, and derive the asymptotic bounds for its root mean square error (RMSE) as a function of the antenna parameters. We then propose a modified Stansfield algorithm with a novel RSS-based weighting scheme based on the Stansfield DoA fusion method, which obtains PU location estimates from measurements of the MaxE algorithm. The modified Stansfield algorithm improves the accuracy of the Stansfield algorithm with equal weights. Simulation results studying the impact of various system parameters, such as number of sectors, number of samples and signal-to-noise ratio, on the DoA/RSS estimation and localization accuracy are presented to provide design guidelines for localization systems based on sectorized antennas.

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.

In this contribution we address the problem of cooperative localization of primary users in cognitive networks. We formulate this problem as a special case of Bayesian tracking, where multiple secondary users obtain direction-of-arrival (DoA) measurements from the target primary user by means of directional antennas, multi-antenna receivers, or virtual arrays. As opposed to typical settings, time-of-arrival (ToA) measurements are not available due to unknown transmission power of primary users and propagation channel. We then highlight some of the key research challenges related to this problem, including DoA estimation, (virtual) array configuration, and information fusion.

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.

Information about primary user (PU) 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. The weighted centroid localization (WCL) scheme uses only the received signal strength information, which makes it simple 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, shadowing variance and correlation distance. Using this analysis, we quantify the robustness of WCL to various physical conditions and conclude that the performance gain by increasing node number in uncorrelated shadowing environment tends to saturate at large node density, and including more nodes in correlated shadowing environments can be harmful to the localization accuracy.

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.