Statistic division multiplexing for wireless communication systems

In this paper, wireless statistic division multiplexing (WSDM) is proposed for wireless communication systems, which is a multiplexing scheme that transmits multiple signals simultaneously in the same frequency band over wireless channels. Therefore, the spectrum efficiency of WSDM is high compared to that of time division multiplexing (TDM), frequency division multiplexing (FDM), and code division multiplexing (CDM). WSDM signal is different from TDM, FDM and CDM signal, which is limited in time interval or frequency band or code. The multiple source signals transmitted in WSDM based wireless communication systems are only required to be statistical independent or statistical distinguished. Source signals are recovered at the multiple-antenna receiver by statistical independence or statistical distinction from the received signals. We show theoretically that the information content of all the signal inputs can be recovered by WSDM system. Computer simulation and realistic experimental results validate the performance of our new WSDM system.

[1]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals , 2000, Int. J. Neural Syst..

[2]  William Stallings,et al.  Wireless Communications and Networks , 2001, 2020 International Conference on Smart Systems and Technologies (SST).

[3]  Peter Jung,et al.  Advantages of CDMA and spread spectrum techniques over FDMA and TDMA in cellular mobile radio applications , 1993 .

[4]  Dario Farina,et al.  Covariance and Time-Scale Methods for Blind Separation of Delayed Sources , 2011, IEEE Transactions on Biomedical Engineering.

[5]  Arie Yeredor,et al.  Performance Analysis of the Strong Uncorrelating Transformation in Blind Separation of Complex-Valued Sources , 2012, IEEE Transactions on Signal Processing.

[6]  Pierre Comon,et al.  A Contrast Function for Independent Component Analysis Without Permutation Ambiguity , 2010, IEEE Transactions on Neural Networks.

[7]  Jun Wang,et al.  Blind-Source Separation Based on Decorrelation and Nonstationarity , 2007, IEEE Transactions on Circuits and Systems I: Regular Papers.

[8]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[9]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[10]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[11]  Atsushi Okamura,et al.  Permutation Method for ICA Separated Source Signal Blocks in Time Domain , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[12]  Lan truyền,et al.  Wireless Communications Principles and Practice , 2015 .

[13]  Bhaskar D. Rao,et al.  An ICA-SCT-PHD Filter Approach for Tracking and Separation of Unknown Time-Varying Number of Sources , 2013, IEEE Transactions on Audio, Speech, and Language Processing.