Noncoherent Short-Packet Communication via Modulation on Conjugated Zeros

We introduce a novel blind (noncoherent) communication scheme, called modulation on conjugate-reciprocal zeros (MOCZ), to reliably transmit short binary packets over unknown finite impulse response systems as used, for example, to model underspread wireless multipath channels. In MOCZ, the information is modulated onto the zeros of the transmitted signals $z-$transform. In the absence of additive noise, the zero structure of the signal is perfectly preserved at the receiver, no matter what the channel impulse response (CIR) is. Furthermore, by a proper selection of the zeros, we show that MOCZ is not only invariant to the CIR, but also robust against additive noise. Starting with the maximum-likelihood estimator, we define a low complexity and reliable decoder and compare it to various state-of-the art noncoherent schemes.

[1]  Babak Hassibi,et al.  Reconstruction of Signals From Their Autocorrelation and Cross-Correlation Vectors, With Applications to Phase Retrieval and Blind Channel Estimation , 2016, IEEE Transactions on Signal Processing.

[2]  Ivan R. Casella Fundamentals of digital communications , 2018 .

[3]  N. I. Miridakis,et al.  Linear Estimation , 2018, Digital and Statistical Signal Processing.

[4]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[5]  Jinho Choi,et al.  Noncoherent OFDM-IM and Its Performance Analysis , 2018, IEEE Transactions on Wireless Communications.

[6]  Hamid Jafarkhani,et al.  On the Minimum Average Distortion of Quantizers With Index-Dependent Distortion Measures , 2017, IEEE Transactions on Signal Processing.

[7]  Babak Hassibi,et al.  Short-message communication and FIR system identification using Huffman sequences , 2017, 2017 IEEE International Symposium on Information Theory (ISIT).

[8]  Babak Hassibi,et al.  Blind Deconvolution with Additional Autocorrelations via Convex Programs , 2017, ArXiv.

[9]  Yi Hong,et al.  Self-coherent OFDM for wireless communications , 2015, 2015 IEEE International Conference on Communications (ICC).

[10]  Holger Boche,et al.  Sparse Signal Processing Concepts for Efficient 5G System Design , 2014, IEEE Access.

[11]  Igor E. Pritsker,et al.  Zeros of polynomials with random coefficients , 2015, J. Approx. Theory.

[12]  Peter Jung,et al.  Sparse Model Uncertainties in Compressed Sensing with Application to Convolutions and Sporadic Communication , 2014, ArXiv.

[13]  Payam Heydari,et al.  Polar Quantizer for Wireless Receivers: Theory, Analysis, and CMOS Implementation , 2014, IEEE Transactions on Circuits and Systems I: Regular Papers.

[14]  Justin K. Romberg,et al.  Blind Deconvolution Using Convex Programming , 2012, IEEE Transactions on Information Theory.

[15]  C. Meinel,et al.  Digital Communication , 2014, X.media.publishing.

[16]  End Semester Mtec DIGITAL COMMUNICATION RECEIVERS , 2013 .

[17]  Yi Hong,et al.  Self-Heterodyne OFDM Transmission for Frequency Selective Channels , 2013, IEEE Transactions on Communications.

[18]  Holger Boche,et al.  PAPR and the Density of Information Bearing Signals in OFDM , 2011, EURASIP J. Adv. Signal Process..

[19]  A. Nikeghbali,et al.  The zeros of random polynomials cluster uniformly near the unit circle , 2004, Compositio Mathematica.

[20]  M. Klamkin,et al.  The product of the distances of a point inside a regular polytope to its vertices , 2007 .

[21]  Rida T. Farouki,et al.  Root neighborhoods, generalized lemniscates, and robust stability of dynamic systems , 2007, Applicable Algebra in Engineering, Communication and Computing.

[22]  S. Fisk Polynomials, roots, and interlacing , 2006, math/0612833.

[23]  Guang Gong,et al.  Signal Design for Good Correlation: For Wireless Communication, Cryptography, and Radar , 2005 .

[24]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[25]  Thomas Kailath,et al.  Optimal training for frequency-selective fading channels , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[26]  T. Tarnai,et al.  UPPER BOUND OF DENSITY FOR PACKING OF EQUAL CIRCLES IN SPECIAL DOMAINS IN THE PLANE , 2000 .

[27]  Andreas Polydoros,et al.  MLSE for an unknown channel .I. Optimality considerations , 1996, IEEE Trans. Commun..

[28]  Hui Liu,et al.  Recent developments in blind channel equalization: From cyclostationarity to subspaces , 1996, Signal Process..

[29]  Ramjee Prasad,et al.  Wideband indoor channel measurements and BER analysis of frequency selective multipath channels at 2.4, 4.75, and 11.5 GHz , 1996, IEEE Trans. Commun..

[30]  T. Kailath,et al.  A least-squares approach to blind channel identification , 1995, IEEE Trans. Signal Process..

[31]  Gradimir V. Milovanovic,et al.  Topics in polynomials - extremal problems, inequalities, zeros , 1994 .

[32]  Richard Zippel,et al.  Effective polynomial computation , 1993, The Kluwer international series in engineering and computer science.

[33]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[34]  D. Godard,et al.  Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication Systems , 1980, IEEE Trans. Commun..

[35]  G. David Forney,et al.  Maximum-likelihood sequence estimation of digital sequences in the presence of intersymbol interference , 1972, IEEE Trans. Inf. Theory.

[36]  M. Marden Geometry of Polynomials , 1970 .

[37]  David A. Huffman,et al.  The generation of impulse-equivalent pulse trains , 1962, IRE Trans. Inf. Theory.

[38]  K. Mahler An application of Jensen's formula to polynomials , 1960 .