Tõnu Trump

Tallinn University of Technology, Tallinn School of Economics and Business Administration, Faculty Member

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Journal of the Acoustical Society of America, 2007

Combined Tomlinson pre-equalization, trellis coded modulation and spectral shaping

Output signal based combination of two NLMS adaptive filters – transient analysis

Proceedings of the Estonian Academy of Sciences, 2011

A combination of two complex normalized least mean square (NLMS) adaptive filters that adapt on t... more A combination of two complex normalized least mean square (NLMS) adaptive filters that adapt on the same input signal at the same time is investigated. One of the filters has a large and the other one has a small step size. The outputs of the filters are combined together through a mixing parameter λ. This combination is an interesting new way of achieving simultaneously a fast initial convergence and a small steady state error of an adaptive algorithm. The mixing parameter is computed from the output signals of the individual filters. The expressions characterizing the time evolution of the mean square deviation and the excess mean square error of the combination scheme are derived. The theoretical results are verified by simulations.

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Leader selection in cooperative network based on MDL subspace algorithm for cognitive radio

This paper presents a simple and robust algorithm for determining a leader node in a cooperative ... more This paper presents a simple and robust algorithm for determining a leader node in a cooperative network based on MDL (Minimum Description Length) subspace algorithm. The algorithm aims to improve the performance of the cooperating network in a spectrum sensing problem for cognitive radio. The outline of the communication and selection process is described and the SNR (signal to noise ratio) estimation algorithm is given. Simulation results show that the algorithm outperforms the non-cooperating network and a cooperating diffusion network with uniform combination weights.

An output signal based combination of two NLMS adaptive algorithms

... We can, however, reach a result for white Gaus-sian input processes. ... K = 1 _ J.l2/J.lI 2(... more ... We can, however, reach a result for white Gaus-sian input processes. ... K = 1 _ J.l2/J.lI 2(J.12/J.lI - 1) (32) (33) (34) (35) Page 6. ... [8] D. Mandie, P. Vayanos, C. Boukis, B. Jelfs, SI Goh, T. Gautama and T. Rutkowski "Collaborative Adaptive Learning Using Hybrid Filters", In Proc. ...

An affine combination of adaptive filters for sparse impulse response identification

In this paper we present an affine combination strategy for two adaptive filters. One filter is d... more In this paper we present an affine combination strategy for two adaptive filters. One filter is designed to handle sparse impulse responses and the other one performs better if impulse response is dispersive. Filter outputs are combined using an adaptive mixing parameter and the resulting output shows better performance than each of the combining filters separately. We also demonstrate that affine combination results in faster convergence than convex combination of two adaptive filters.

Clock Skew Compensation

Double talk detector

Estimation of nominal direction of arrival and angular spread using an array of sensors

Signal Processing, Apr 1, 1996

... The nominal direction-of-arrival (DOA) estimates are compared with those provided by a standa... more

Distributed recursive energy detection

Recent studies have shown that, while the available licensed radio spectrum becomes more occupied... more Recent studies have shown that, while the available licensed radio spectrum becomes more occupied, the assigned spectrum is significantly underutilized. To alleviate the situation, cognitive radio (CR) technology has been proposed to provide an opportunistic access to the licensed spectrum areas. CR systems are able to serve the secondary users for detecting and utilizing so called spectrum holes by sensing and adapting to the environment without causing harmful effects or interference to the licensed primary users (PU). CR systems need to detect the presence of a primary user by continuously sensing the spectrum area of interest. Radiowave propagation effects like fading and shadowing often complicate sensing of spectrum holes because the PU signal can be weak in a particular area. Cooperative spectrum sensing is seen as a prospective solution to enhance the detection of PU signals. This paper studies distributed spectrum sensing in a cognitive radio context. We investigate a distributed energy detection scheme without using any fusion center. Due to reduced communication such a topology is more energy efficient. The PU signal is assumed to be in slow fading. A recursive distributed power estimation and detection scheme is proposed. The theoretical findings are verified through simulations.

Distributed Adaptive Largest Eigenvalue Detection with SNR Weighted Observations

Cognitive radio (CR) systems have to be able to detect the presence of a primary user (PU) signal... more Cognitive radio (CR) systems have to be able to detect the presence of a primary user (PU) signal by sensing the spectrum area of interest. Due to radiowave propagation effects like fading and shadowing, spectrum sensing is often complicated, because the PU signal can be attenuated in a particular area. In this paper, we explore a distributed spectrum sensing approach that exploits the largest eigenvalue of correlation matrices (CMs) that are adaptively estimated, based on the combine and adapt least (CTA) type of diffusion method with no fusion center (FC). More specifically, CR nodes exchange also observations with a subset of neighbouring nodes and combine the neighbouring observations based on the locally estimated signal to noise ratio (SNR) values. We analyse the resulting detection performance and verify the theoretical findings through simulations.

Distributed Largest Eigenvalue-Based Spectrum Sensing Using Diffusion LMS

IEEE Transactions on Signal and Information Processing over Networks, Jun 1, 2018

In this paper we propose a distributed detection scheme for cognitive radio (CR) networks, based ... more In this paper we propose a distributed detection scheme for cognitive radio (CR) networks, based on the largest eigenvalues (LEs) of adaptively estimated correlation matrices (CMs), assuming that the primary user signal is temporally correlated. The proposed algorithm is fully distributed, thereby avoiding the potential single point of failure that a fusion centre (FC) would imply. Different forms of diffusion least mean square (LMS) algorithms are used for estimating and averaging the CMs over the CR network for the LE detection and the resulting estimation performance is analyzed using a common framework. In order to obtain analytic results on the detection performance, the exact distribution of the CM estimates are approximated by a Wishart distribution, by matching the moments. The theoretical findings are verified through simulations.

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Distributed largest eigenvalue detection

Cognitive radio (CR) systems need to detect the presence of a primary user (PU) signal by continu... more Cognitive radio (CR) systems need to detect the presence of a primary user (PU) signal by continuously sensing the spectrum area of interest. Radiowave propagation effects like fading and shadowing often complicate sensing of spectrum holes because the PU signal can be weak in a particular area. Cooperative spectrum sensing is seen as a prospective solution to enhance the detection of PU signals. In this paper we study distributed spectrum sensing, based on the largest eigenvalue of adaptively estimated correlation matrices (CMs) of received signals. The PU signal is assumed to be temporally correlated. In this paper an Combine and Adapt (CTA) least mean square (LMS) diffusion based mean vector estimation scheme is proposed. No fusion center (FC) for estimation or detection is used. We analyse the resulting detection performance and verify the theoretical findings through simulations.

Maximum Likelihood Estimation of Nominal Direction of Arrival and Angular Spread Using and Array of Sensors

Maximum Likelihood Estimation of Nominal Direction of Arrival and Angular Spread Using and Array ... more

Distributed Signal Processing in Cognitive Radio Networks. Hajutatud signaalitöötlus kognitiivse raadio võrgus

Pure Delay Estimation

Reduction of digital filter delay

An energy detector for spectrum sensing in impulsive noise environment

In this paper we propose a robust energy detector for spectrum sensing in cognitive radio systems... more In this paper we propose a robust energy detector for spectrum sensing in cognitive radio systems. The detector is derived by modelling the noise process as consisting of two components, Gaussian noise and impulsive noise. The impulsive noise is modelled using a uniform distribution that appears with a certain probability. A convenient approximation using the max operator is then applied to the resulting probability density function to gain mathematical tractability. The performance of the proposed detector is analysed theoretically and the results of the theoretical analysis are verified in our simulation study.

Some Novel Algorithms for Processing Sensor Array Output Signals

This paper investigates a robust adaptive beamforming technique for two dimensional antenna array... more This paper investigates a robust adaptive beamforming technique for two dimensional antenna array. Robustness is needed against imperfect knowledge of desired signal direction of arrival as well as against other errors. Erroneous estimation of source direction is especially true in air surveillance systems because aircraft are moving in high speed. Need for two dimensional antenna arrays is also substantial as aircraft are often at the same azimuth but at different elevation angles relative to ground system. Proposed algorithm is based on Generalized Sidelobe Canceller and uses Discrete Prolate Spheroidal Window or often refered to as Slepian window function. Only Slepians corresponding to large spectral concentration are used in both steering and blocking branches.

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Accounting for measurement noise color in frequency domain adaptive algorithms

Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284)

... It turns out that a rather simple modification of the normal-ization factors can significantl... more

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