Distributed Algorithms for Spectral and Energy-Efficiency Maximization of K-User Interference Channels
Distributed Algorithms for Spectral and Energy-Efficiency Maximization of K-User Interference Channels
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DOI：
10.1109/access.2021.3094976
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
M. Soleymani;I. Santamaría;P. Schreier
中科院分区：
文献类型：
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作者：
M. Soleymani;I. Santamaría;P. Schreier
在本文中，我们提出了一个合作的分布式框架，以优化各种速率和能源效率（EE）的效用函数，如最小加权速率或全球EE，为$K$用户干扰信道。我们专注于单输入多输出（SIMO）的情况下，每个用户，仅基于本地信道状态信息（CSI）和来自其他用户的有限的交换信息，优化其发射功率和接收波束形成器，虽然该框架也可以扩展到多输出多输入（MIMO）的情况下。分布式框架将交替优化方法与优化最小化（MM）技术相结合，从而确保收敛到集中式成本函数的稳定点。对于某些效用函数，得到了幂更新规则的闭合形式，从而得到了非常快的收敛算法。接收机将干扰视为噪声（TIN），并应用最大化信号与干扰加噪声（SINR）的波束形成器。建议的合作分布式算法是强大的对信道变化和网络拓扑结构的变化，我们的模拟结果表明，他们执行接近的集中式解决方案，需要全球CSI。作为一个基准，我们还研究了一个非合作的分布式框架的基础上，所谓的“信号泄漏加噪声比”（SNLR），进一步降低了合作版本的开销。
In this paper, we propose a cooperative distributed framework to optimize a variety of rate and energy-efficiency (EE) utility functions, such as the minimum-weighted rate or the global EE, for the $K$ -user interference channel. We focus on the single-input multiple-output (SIMO) case, where each user, based solely on local channel state information (CSI) and limited exchange information from other users, optimizes its transmit power and receive beamformer, although the framework can also be extended to the multiple-output multiple-input (MIMO) case. The distributed framework combines an alternating optimization approach with majorization-minimization (MM) techniques, thus ensuring convergence to a stationary point of the centralized cost function. Closed-form power update rules are obtained for some utility functions, thus obtaining very fast convergence algorithms. The receivers treat interference as noise (TIN) and apply the beamformers that maximize the signal-to-interference-plus-noise (SINR). The proposed cooperative distributed algorithms are robust against channel variations and network topology changes and, as our simulation results suggest, they perform close to the centralized solution that requires global CSI. As a benchmark, we also study a non-cooperative distributed framework based on the so-called “signal-to-leakage-plus-noise ratio” (SNLR) that further reduces the overhead of the cooperative version.