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Capacity-Achieving Distributions in Gaussian Multiple Access Channel With Peak Power Constraints

机译:具有峰值功率约束的高斯多路访问信道中的容量达到分布

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This paper addresses a two-user Gaussian multiple access channel (MAC) under peak power constraints at the transmitters. It is shown that generating the code-books of both users according to discrete distributions with a finite number of mass points achieves the largest weighted sum-rate in the network. This verifies that any point on the boundary of the capacity region of a two-user MAC under peak power constraints at both transmitters is achieved by discrete distributions with a finite number of mass points. Although the capacity-achieving distributions are not necessarily unique, it is verified that only discrete distributions with a finite number of mass points can achieve a point on the boundary of the capacity region. It is shown that there exist an infinite number of sum-rate-optimal points on the boundary of the capacity region. In contrast to the Gaussian MAC with average power constraints, we verify that time division (TD) cannot achieve any of the sum-rate-optimal points in the Gaussian MAC with peak power constraints. Using the so-called I-MMSE identity of Guo et al., the largest achievable sum-rate by orthogonal code division (OCD) is characterized where it is shown that Walsh–Hadamard spreading codes of length 2 are optimal. In the symmetric case where the peak power constraints at both transmitters are identical, we verify that OCD can achieve a sum-rate that is strictly larger than the highest sum-rate achieved by TD. Finally, it is demonstrated that there are values for the maximum peak power at the transmitters such that OCD can not achieve any of the sum-rate-optimal points on the boundary of the capacity region.
机译:本文讨论了在发射机的峰值功率限制下的两用户高斯多址访问信道(MAC)。结果表明,根据有限数量质量点的离散分布生成两个用户的密码本,可以实现网络中最大的加权求和率。这验证了在两个发射机处的峰值功率约束下,两个用户MAC的容量区域边界上的任何点都是通过具有有限数量的质量点的离散分布来实现的。尽管获得容量的分布不一定是唯一的,但已证实只有数量有限的质量点的离散分布才能在容量区域的边界上达到一个点。结果表明,在容量区域的边界上存在无限数量的总和最优点。与具有平均功率约束的高斯MAC相比,我们验证了时分(TD)无法在具有峰值功率约束的高斯MAC中获得任何总和最优点。利用Guo

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