Sending a Bivariate Gaussian Over a Gaussian MAC

被引:116
|
作者
Lapidoth, Amos [1 ]
Tinguely, Stephan [1 ]
机构
[1] ETH, Dept Informat Technol & Elect Engn, CH-8092 Zurich, Switzerland
基金
瑞士国家科学基金会;
关键词
Achievable distortion; combined source-channel coding; correlated sources; Gaussian multiple-access channel; Gaussian source; mean squared-error distortion; multiple-access channel; uncoded transmission; CORRELATED SOURCES; CHANNEL; TRANSMISSION;
D O I
10.1109/TIT.2010.2044058
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We study the power-versus-distortion tradeoff for the distributed transmission of a memoryless bivariate Gaussian source over a two-to-one average-power limited Gaussian multiple-access channel. In this problem, each of two separate transmitters observes a different component of a memoryless bivariate Gaussian source. The two transmitters then describe their source component to a common receiver via an average-power constrained Gaussian multiple-access channel. From the output of the multiple-access channel, the receiver wishes to reconstruct each source component with the least possible expected squared-error distortion. Our interest is in characterizing the distortion pairs that are simultaneously achievable on the two source components. We focus on the "equal bandwidth" case, where the source rate in source-symbols per second is equal to the channel rate in channel-uses per second. We present sufficient conditions and necessary conditions for the achievability of a distortion pair. These conditions are expressed as a function of the channel signal-to-noise ratio (SNR) and of the source correlation. In several cases, the necessary conditions and sufficient conditions are shown to agree. In particular, we show that if the channel SNR is below a certain threshold, then an uncoded transmission scheme is optimal. Moreover, we introduce a "source-channel vector-quantizer" scheme which is asymptotically optimal as the SNR tends to infinity.
引用
收藏
页码:2714 / 2752
页数:39
相关论文
共 50 条
  • [41] BIVARIATE GAUSSIAN FIBONACCI AND LUCAS POLYNOMIALS
    Asci, Mustafa
    Gurel, Esref
    [J]. ARS COMBINATORIA, 2013, 109 : 461 - 472
  • [42] On the Computation of Integrals of Bivariate Gaussian Distribution
    Savaux, Vincent
    Le Magoarou, Luc
    [J]. 2020 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (ISCC), 2020, : 612 - 617
  • [43] Depicting Bivariate Relationship with a Gaussian Ellipse
    Sarkar, Jyotirmoy
    Rashid, Mamunur
    [J]. STATISTICS AND APPLICATIONS, 2021, 19 (02): : 77 - 87
  • [44] Large deviations of bivariate Gaussian extrema
    van der Hofstad, Remco
    Honnappa, Harsha
    [J]. QUEUEING SYSTEMS, 2019, 93 (3-4) : 333 - 349
  • [45] GAUSSIAN CUBATURE AND BIVARIATE POLYNOMIAL INTERPOLATION
    XU, Y
    [J]. MATHEMATICS OF COMPUTATION, 1992, 59 (200) : 547 - 555
  • [46] Special Values of the Bivariate Gaussian Distribution
    Beaulieu, Norman C.
    [J]. IEEE SIGNAL PROCESSING LETTERS, 2013, 20 (01) : 99 - 101
  • [47] Computing the bivariate Gaussian probability integral
    Chandramouli, R
    Ranganathan, N
    [J]. IEEE SIGNAL PROCESSING LETTERS, 1999, 6 (06) : 129 - 131
  • [48] On Joint Source-Channel Coding for a Multivariate Gaussian on a Gaussian MAC
    Floor, Pal Anders
    Kim, Anna N.
    Ramstad, Tor A.
    Balasingham, Ilangko
    Wernersson, Niklas
    Skoglund, Mikael
    [J]. IEEE TRANSACTIONS ON COMMUNICATIONS, 2015, 63 (05) : 1824 - 1836
  • [49] Improved bounds on Gaussian MAC and sparse regression via Gaussian inequalities
    Zadik, Ilias
    Polyanskiy, Yury
    Thrampoulidis, Christos
    [J]. 2019 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT), 2019, : 430 - 434
  • [50] Capacity of the Energy Harvesting Gaussian MAC
    Inan, Huseyin A.
    Shaviv, Dor
    Ozgur, Ayfer
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2018, 64 (04) : 2347 - 2360