Analysis of the effect of nonlinear low noise amplifier with memory for wideband spectrum sensing

In wideband sensing receivers, especially in low-cost mass-product devices, the sensing operation is usually done in a range where the RF front-end components, such as the LNAs exhibit a nonlinear behavior. Consequently, intermodulation (IMD) and crossmodulation (XM) are generated and cause distortion in the spectrum sensing region. This paper investigates the impact on the threshold level setting of a wideband energy detection based spectrum sensing caused by a nonlinear LNA at the radio frequency (RF) front-end of a wideband cognitive radio receiver. The third and fifth order terms of the expansion of the nonlinearity characteristics are modeled using a memory-polynomial model. The main contribution of this study is the derivation of a proper threshold level for different frequency bins.

[1]  Georgios B. Giannakis,et al.  Compressed Sensing for Wideband Cognitive Radios , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[2]  M. Schetzen.  The Volterra and Wiener Theories of Nonlinear Systems , 1980 .

[3]  H. Vincent Poor,et al.  Wideband Spectrum Sensing in Cognitive Radio Networks , 2008, 2008 IEEE International Conference on Communications.

[4]  Cheng-Xiang Wang,et al.  Computationally Tractable Model of Energy Detection Performance over Slow Fading Channels , 2010, IEEE Communications Letters.

[5]  E. Parzen.  1. Random Variables and Stochastic Processes , 1999 .

[6]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[7]  Mikko Valkama,et al.  Joint Mitigation of Nonlinear RF and Baseband Distortions in Wideband Direct-Conversion Receivers , 2014, IEEE Transactions on Microwave Theory and Techniques.

[8]  Behzad Razavi,et al.  Design considerations for direct-conversion receivers , 1997 .

[9]  Georgios B. Giannakis,et al.  A Wavelet Approach to Wideband Spectrum Sensing for Cognitive Radios , 2006, 2006 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[10]  Cheng-Xiang Wang,et al.  Wideband Spectrum Sensing for Cognitive Radio Networks , 2013, ArXiv.

[11]  Stephen J. Shellhammer,et al.  Performance of power detector sensors of DTV signals in IEEE 802.22 WRANs , 2006, TAPAS '06.

[12]  Vladimir I. Kostylev,et al.  Energy detection of a signal with random amplitude , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[13]  Rick S. Blum,et al.  On the Approximation of Correlated Non-Gaussian Noise Pdfs using Gaussian Mixture Models , 1999 .

[14]  Dennis R. Morgan,et al.  Memory polynomial predistorter based on the indirect learning architecture , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[15]  Anant Sahai,et al.  What is a Spectrum Hole and What Does it Take to Recognize One? , 2009, Proceedings of the IEEE.

[16]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[17]  Behrouz Farhang-Boroujeny,et al.  Filter Bank Spectrum Sensing for Cognitive Radios , 2008, IEEE Transactions on Signal Processing.