Near-Optimal Coding for Many-User Multiple Access Channels

被引：2
作者：
Hsieh K. [1 ]
Rush C. [2 ]
Venkataramanan R. [1 ]
机构：
[1] Department of Engineering, University of Cambridge, Cambridge
[2] Department of Statistics, Columbia University, New York, 10027, NY
来源：
IEEE Journal on Selected Areas in Information Theory | 2022年 / 3卷 / 01期
关键词：
approximate message passing; Multiple access; sparse superposition codes; spatial coupling;
D O I：
10.1109/JSAIT.2022.3158827
中图分类号：
学科分类号：
摘要：
This paper considers the Gaussian multiple-access channel in the asymptotic regime where the number of users grows linearly with the code length. We propose efficient coding schemes based on random linear models with approximate message passing (AMP) decoding and derive the asymptotic error rate achieved for a given user density, user payload (in bits), and user energy. The tradeoff between energy-per-bit and achievable user density (for a fixed user payload and target error rate) is studied. It is demonstrated that in the large system limit, a spatially coupled coding scheme with AMP decoding achieves near-optimal tradeoffs for a wide range of user densities. Furthermore, in the regime where the user payload is large, we also study the tradeoff between energy-per-bit and spectral efficiency and discuss methods to reduce decoding complexity. © 2020 IEEE.
引用
收藏
页码：21 / 36
页数：15
相关论文
共 45 条
  • [1] Cover T.M., Thomas J.A., Thomas, Elements of Information Theory, (2006)
  • [2] El Gamal A., Kim Y.-H., Network Information Theory, (2011)
  • [3] Chen X., Chen T.-Y., Guo D., Capacity of Gaussian many-access channels, IEEE Trans. Inf. Theory, 63, 6, pp. 3516-3539, (2017)
  • [4] Polyanskiy Y., A perspective on massive random-access, Proc. IEEE Int. Symp. Inf. Theory, Aachen, Germany, pp. 2523-2527, (2017)
  • [5] Zadik I., Polyanskiy Y., Thrampoulidis C., Improved bounds on Gaussian MAC and sparse regression via Gaussian inequalities, Proc. IEEE Int. Symp. Inf. Theory, pp. 430-434, (2019)
  • [6] Ravi J., Koch T., Capacity per unit-energy of Gaussian random many-access channels, Proc. IEEE Int. Symp. Inf. Theory, pp. 2763-2767, (2019)
  • [7] Ravi J., Koch T., On the per-user probability of error in Gaussian many-access channels, Proc. Int. Zurich Seminar Inf. Commun., pp. 139-143, (2020)
  • [8] Ravi J., Koch T., Scaling laws for Gaussian random many-access channels, (2021)
  • [9] Kowshik S.S., Andreev K., Frolov A., Polyanskiy Y., Energy efficient random access for the quasi-static fading MAC, Proc. IEEE Int. Symp. Inf. Theory, Paris, France, pp. 2768-2772, (2019)
  • [10] Kowshik S.S., Polyanskiy Y., Fundamental limits of many-user MAC with finite payloads and fading, IEEE Trans. Inf. Theory, 67, 9, pp. 5853-5884, (2021)
12345