RFI (Radio Frequency Interference) Mitigation Toolbox 1.2.1 beta

Simulates environment for RFI & quantifies the performance of interference mitigation algorithms

NOTE: Though I have uploaded this toolbox, I have no contribution in this work. Programming and research in this field has been done by students given below

Kapil Gulati, Marcel Nassar, Aditya Chopra, Marcus DeYoung, Arvind Sujeeth, and Navid Aghasadeghi.

It is composed of various functions used to generate various types of noise statistics and to perform noise cancellation and detection.The current version (ver 1.2.1 ) supports the generation of Middleton Class A, Symmetric Alpha Stable, and the bivariate Middleton Class A random variables. For the evaluation of communication performance under the presence of the aforementioned noise types, the current version (ver 1.2.1) of the toolbox implements a PAM communication system with correlation detection, Wiener filtering followed by correlation detection, the optimal Bayes detection developed by Spaulding and Middleton [1], and the small-signal approximation of the optimal Bayes Detection. Further the toolbox implements a 2x2 MIMO communication system using M-QAM modulation, spatial multiplexing and alamouti transmission strategies with optimal Gaussian maximum likelihood (ML) receiver, optimal and suboptimal ML receivers in the presence of bivariate Middleton Class A noise [6]. In addition to that, it implements the following parameter estimation algorithms: Method of Moments [3], Zabin and Poor [4], Tsihrintzis [2]. This toolbox also includes various demos that illustrate the usage of the implemented functions, and generate various results. Ver 1.2.1 also added a demo providing capability to send user files through a impulsive noise channels.

References

[1] A. Spaulding and D. Middleton, “Optimum reception in an impulsive interference environment-part I: Coherent detection,” IEEE Transactions on Communications, vol. 25, no. 9, pp. 910–923, 1977.

[2] G. A. Tsihrintzis and C. L. Nikias, "Fast estimation of the parameters of alpha-stable impulsive interference", IEEE Transactions on Signal Processing, vol. 44, no 6, pp. 1492-1503, June 1996.

[3] D. Middleton, “Procedures for determining the properties of the first-order canonical models of Class A and Class B electromagnetic interference”, IEEE Transactions on Electromagnetic Compatibility, vol. 21, pp. 190-208, Aug. 1979.

[4] S. M. Zabin and H. V. Poor, “Efficient estimation of Class A noise parameters via the EM [Expectation-Maximization] algorithms”, IEEE Transaction on Information Theory, vol. 37, no. 1, pp. 60-72, Jan. 1991.

[5] J.R. Gonzalez and G.R. Arce. "Optimality of the myriad in practical impulsive-noise enviroments," IEEE Trans. on Signal Processing, vol 49,no.2, pp. 438-441, February 2001.

[6] K. Gulati, A. Chopra, R. W. Heath, Jr., B. L. Evans, K. R. Tinsley, and X. E. Lin, "MIMO Receiver Design in the Presence of Radio Frequency Interference", Proc. IEEE Int. Global Communications Conf., Nov. 30-Dec. 4th, 2008, New Orleans, LA USA.

[7] M. Nassar, K. Gulati, A. K. Sujeeth, N. Aghasadeghi, B. L. Evans and K. R. Tinsley, "Mitigating Near-Field Interference in Laptop Embedded Wireless Transceivers", Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Proc., Mar. 30-Apr. 4, 2008, Las Vegas, NV USA.