Harri Saarnisaari

TLS-ESPRIT in a time delay estimation

In this paper TLS-ESPRIT is applied to a time delay estimation. The correlation method and a frequency domain deconvolution are used to convert the time delay estimation problem to a estimation of frequencies of complex sinusoids in a white nonstationary noise. TLS-ESPRIT is then applied to estimate the unknown frequencies, i.e., the time delays. It is shown that if the noise (after the deconvolution) is a white stationary process the TLS-ESPRIT derived returns to TLS-ESPRIT derived earlier for the stationary noise case. The computer simulations show that the derived method produces unbiased, high resolution time delay estimates.

Cognitive Radio Trial Environment: First Live Authorized Shared Access-Based Spectrum-Sharing Demonstration

Cognitive radio system (CRS) technology can help respond to the growing mobile traffic demand by improving network resource usage and gaining access to new shared spectrum bands. This article presents a cognitive radio trial environment (CORE) consisting of cognitive engines (CEs) to control different radio systems [e.g., long-term evolution (LTE) and wireless open-access research platform (WARP)-based networks]. Load balancing and authorized shared access (ASA) are demonstrated using the trial environment with promising results. The ASA-based spectrum sharing trial is presented for the first time with a real-life mobile network accessing a shared spectrum band under a licensed shared access (LSA) regime. Cognitive decision making brings intelligence to the usage of the radio and network resources and, at best, increases considerably end users? quality of service (QoS) compared to the standard systems, as shown for QoS-based offloading.

Spectrum Sensingwith Forward Methods

New technologies will require effective spectrum use. Opportunistic spectrum usage that is one application of so called cognitive radio techniques enables the use of unused frequencies. One possible way to locate these free frequency bands is to use so called spectrum sensing. In this paper, energy detection based spectrum sensing methods called the forward consecutive mean excision (FCME) and forward cell averaging (CA) methods are studied in the situations where the noise power is unknown. The detection and false alarm probabilities of the studied methods are also of interest. Numerical results show that the investigated approaches have good performance

Threshold setting strategies for a quantized total power radiometer

We analyze the impact of a uniform quantizer on the false-alarm probability of a total power radiometer. Different possibilities to set the detection threshold are discussed. The main emphasis is on methods that use the estimated noise level. In particular, we analyze the cell-averaging (CA) constant false-alarm rate threshold setting strategy. The numerical results show that the CA strategy offers the desired false-alarm probability.

CFAR strategies for channelized radiometer

The channelized radiometer is a well-known intercept receiver. We analyze its performance when different constant false alarm rate (CFAR) strategies are used to set a detection threshold based on values of some reference cells. The studied conventional methods are cell-averaging (CA) and order statistics. The proposed iterative methods for setting the detection threshold are forward consecutive mean excision (FCME) with the CA scaling factors in (final) detection decision (FCME+CA), backward consecutive mean excision (BCME) with the CA scaling factors in detection (BCME+CA) and a method that uses the CA scaling factors in both censoring and detection (CA+CA). The results show that iterative CFAR methods can improve detection performance compared to the baseline methods.

Iterative multidimensional impulse detectors for communications based on the classical diagnostic methods

This letter introduces diagnostic impulse-detection methods in general, and, based on those, presents three iterative impulse detectors for communication receivers with an antenna array. These detectors are implemented either in a backward or forward search mode. The forward methods are shown to perform better. They can detect all the impulses, even though 90% of all the samples are corrupted.

Double-threshold based narrowband signal extraction

A localization algorithm based on double-thresholding (LAD) is a computationally simple method for localizing narrowband signals in the frequency domain. The method does not need any a priori information about the narrowband signal. The localization is based on two thresholds. The lower threshold is used to compose adjacent signal samples into clusters whereas the upper threshold is used to detect signals. The LAD can be applied in narrowband signal detection as well as in interference suppression. The simulation results show that the LAD gives quite good localization accuracy and the LAD is able to determine correct number of narrowband signals even over 95% of the cases.

Impulse detection and rejection methods for radio systems

Impulses that exist in the nature or are artificial may severely decrease the performance of any radio system including communication and interception receivers and radars. This paper investigates impulse detection in receivers that can have either a single antenna or an antenna array. The proposed detector uses either a backward or forward outlier search strategy and assumes zero mean signals and spatially and temporally white background noise. Signal need not to be weak, i.e., signal-to-noise ratio may be large and the methods still works. The backward method uses all the data in an initial step whereas the forward method uses smaller, (hopefully) clean set in an initial step. The forward method is expected to perform better. Simulations confirm this expectation. Indeed, the used forward method can detect outliers even if their contamination exceeds 90% of all the samples.

ML time delay estimation in a multipath channel

A receiver based on the maximum likelihood (ML) method for estimating unknown parameters of a periodic signal in a multipath channel is presented. In an AWGN channel the ML-receiver reduces to the matched filter followed by a calculation of the ML-metric. A particular interest of this paper lies in time delay estimation which is crucial in spread spectrum radiolocation systems. Performance difference between the obtained ML-receiver and the conventional receiver (the matched filter followed by an envelope detector) in a multipath channel where temporal separation of multipath components is less than one chip interval is investigated by simulations. As excepted, the ML-receiver outperforms the conventional receiver if SNR is high enough, i.e., above the threshold SNR of the ML-receiver.

CFAR Outlier Detection With Forward Methods

Separation or classification of signal-present samples from noise-only samples is studied. The false-alarm probability implies how many noise-only samples are wrongly classified as outliers, and typically it should be smaller than some upper limit. The noise distribution parameters are not known a priori and have to be estimated. Multiple outliers have a strong influence to that estimation and may lead to uncontrollable false-alarm probability. The false-alarm probability control can be improved by robust estimators and/or by forward-detection methods. In this article, the false-alarm probability of the forward methods is analyzed. The forward consecutive mean excision (FCME) algorithm is enhanced to allow better false-alarm control. It is proposed that the forward method using the cell-averaging (CA) constant false-alarm rate (CFAR) technique can be applied for locating the outliers. The results show that its false-alarm probability stays close to the required value even in the presence of multiple outliers.