Adaptive noise tracking for Cognitive Radios under more realistic operation conditions

• Normal operation conditions of cognitive radio applications require signal processing techniques that can be executed in real time. One of the first steps is to sense the occupied or free frequency channels. Two major drawbacks in the current techniques are that they assume (i) the noise as white and (ii) the measured spectrum as time-invariant. In real world, the noise is (i) colored so it disturbs the signal unevenly and (ii) its spectrum changes over time. Hence, tracking the time-varying noise spectrum can become crucial to remove the noise contributions and enhance the estimate of the received signal. In this paper, we study an auto-regressive model to develop an adaptive noise tracking technique using a Kalman filter such that an extension of Boll's noise subtraction technique, designed for audio noise cancellation, becomes feasible when adjusted to cognitive radio scenarios. Simulation results show the performance of this technique. 

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

auto-reg ressive model

cognitive radio

denoising

noise tracking

power spectrum

spectral subtraction

Kalman filters

Planning

Signal processing

Spurious signal noise

Sustainable development

Auto regressive models

De-noising

Noise cancellation

Noise contributions

Operation conditions

Signal processing technique

Spectral subtractions

Radio systems

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