On the SNR-Evolution of the MMSE Function of Codes for the Gaussian Broadcast and Wiretap Channels

This paper considers the signal-to-noise ratio (SNR)-evolution, meaning the behavior as a function of the SNR, of the minimum mean-square error (MMSE) function of code sequences in several multi-user settings in the additive white Gaussian noise regime. The settings investigated in this context include the Gaussian wiretap channel, the Gaussian broadcast channel (BC), and the Gaussian BC with confidential messages (BCC). This paper shows that the specific properties of the SNR-evolution of the MMSE and conditional MMSE functions are necessary and sufficient conditions for capacity or equivocation achieving code sequences. In some cases, the complete SNR-evolution of a family of code sequences can be determined, providing significant insight into the disturbance (in terms of MMSE) such codes have on unintended receivers at other SNRs. Moreover, the effects of an additional MMSE constraint on the capacity region and on the SNR-evolution of code sequences are considered in the BC and BCC settings. Such an analysis emphasizes the tradeoff between rates and limited disturbance on unintended receivers.

[1]  Martin E. Hellman,et al.  The Gaussian wire-tap channel , 1978, IEEE Trans. Inf. Theory.

[2]  Shlomo Shamai,et al.  Secure Communication Over Fading Channels , 2007, IEEE Transactions on Information Theory.

[3]  Imre Csiszár,et al.  Broadcast channels with confidential messages , 1978, IEEE Trans. Inf. Theory.

[4]  Shlomo Shamai,et al.  The Interplay Between Information and Estimation Measures , 2013, Found. Trends Signal Process..

[5]  R. Bartle The elements of integration and Lebesgue measure , 1995 .

[6]  Daniel Pérez Palomar,et al.  Gradient of mutual information in linear vector Gaussian channels , 2006, IEEE Transactions on Information Theory.

[7]  Mihir Bellare,et al.  Polynomial-Time, Semantically-Secure Encryption Achieving the Secrecy Capacity , 2012, IACR Cryptol. ePrint Arch..

[8]  Thomas M. Cover,et al.  Network Information Theory , 2001 .

[9]  Gregory W. Wornell,et al.  Secure Transmission With Multiple Antennas—Part II: The MIMOME Wiretap Channel , 2007, IEEE Transactions on Information Theory.

[10]  Shlomo Shamai,et al.  Information Theory On extrinsic information of good binary codes operating over Gaussian channels , 2007, Eur. Trans. Telecommun..

[11]  Patrick P. Bergmans,et al.  A simple converse for broadcast channels with additive white Gaussian noise (Corresp.) , 1974, IEEE Trans. Inf. Theory.

[12]  Thomas M. Cover,et al.  Elements of information theory (2. ed.) , 2006 .

[13]  Shlomo Shamai,et al.  Statistical Physics of Signal Estimation in Gaussian Noise: Theory and Examples of Phase Transitions , 2008, IEEE Transactions on Information Theory.

[14]  Robert G. Bartle,et al.  The Elements of Integration and Lebesgue Measure: Bartle/The Elements , 1995 .

[15]  Matthieu R. Bloch,et al.  Physical-Layer Security: From Information Theory to Security Engineering , 2011 .

[16]  Max H. M. Costa,et al.  Writing on dirty paper , 1983, IEEE Trans. Inf. Theory.

[17]  Frédérique E. Oggier,et al.  The secrecy capacity of the MIMO wiretap channel , 2007, 2008 IEEE International Symposium on Information Theory.

[18]  Abbas El Gamal,et al.  Network Information Theory , 2021, 2021 IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT).

[19]  Gregory W. Wornell,et al.  Secure Transmission With Multiple Antennas I: The MISOME Wiretap Channel , 2010, IEEE Transactions on Information Theory.

[20]  Ueli Maurer,et al.  Information-Theoretic Key Agreement: From Weak to Strong Secrecy for Free , 2000, EUROCRYPT.

[21]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[22]  Patrick P. Bergmans,et al.  Random coding theorem for broadcast channels with degraded components , 1973, IEEE Trans. Inf. Theory.

[23]  Te Sun Han,et al.  A new achievable rate region for the interference channel , 1981, IEEE Trans. Inf. Theory.

[24]  A. D. Wyner,et al.  The wire-tap channel , 1975, The Bell System Technical Journal.

[25]  Thomas M. Cover,et al.  Comments on Broadcast Channels , 1998, IEEE Trans. Inf. Theory.

[26]  Shlomo Shamai,et al.  Estimation in Gaussian Noise: Properties of the Minimum Mean-Square Error , 2010, IEEE Transactions on Information Theory.

[27]  A. Sridharan Broadcast Channels , 2022 .

[28]  Himanshu Tyagi,et al.  Explicit capacity-achieving coding scheme for the Gaussian wiretap channel , 2014, 2014 IEEE International Symposium on Information Theory.

[29]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[30]  Zhi Ding,et al.  On Secrecy Rate Analysis of MIMO Wiretap Channels Driven by Finite-Alphabet Input , 2011, IEEE Transactions on Communications.

[31]  Sergio Verdú,et al.  Approximation theory of output statistics , 1993, IEEE Trans. Inf. Theory.

[32]  Shlomo Shamai,et al.  On MMSE Crossing Properties and Implications in Parallel Vector Gaussian Channels , 2013, IEEE Transactions on Information Theory.

[33]  Shlomo Shamai,et al.  Secrecy-achieving polar-coding , 2010, 2010 IEEE Information Theory Workshop.

[34]  Masahito Hayashi,et al.  Construction of wiretap codes from ordinary channel codes , 2010, 2010 IEEE International Symposium on Information Theory.

[35]  Shlomo Shamai,et al.  MMSE of “Bad” Codes , 2013, IEEE Transactions on Information Theory.

[36]  Robert G. Gallager,et al.  Capacity and coding for degraded broadcast channels , 1974 .

[37]  Shlomo Shamai,et al.  Mutual information and minimum mean-square error in Gaussian channels , 2004, IEEE Transactions on Information Theory.

[38]  Sergio Verdú,et al.  Functional Properties of Minimum Mean-Square Error and Mutual Information , 2012, IEEE Transactions on Information Theory.