Following the roadmap of carbon neutrality, wireless communication systems are upgrading to use green energy that comes from renewable sources, e.g., sun, tide, and wind. Due to the volatile arrival of green energy, the on-grid energy is used as a backup for a green coordinated multiple point system. In this work, a weighted sum-rate maximization problem in the green coordinated multiple point system is investigated by expecting non-positive consumption of the on-grid energy in the long term. Motivated by the capacity-achieving property and simple implementation, an online zero-forcing dirty paper precoder is proposed to update the precoding matrices by combining statistical learning with the Lyapunov learning technique. A tradeoff relation is theoretically established to show that the long-term weighted sum rate approaches the O(V){\mathcal{ O}}(V) -neighbor of optimal value while the long-term on-grid energy increases at a rate of O(log2(V)/V√){\mathcal{ O}}({\scriptstyle {}^{\scriptstyle \log ^{2}(V)}}\hspace {-0.224em}/\hspace {-0.112em}{\scriptstyle \sqrt {V}}) , where VV is an introduced control parameter. Numerical results are used to verify the performance of the proposed online adaptive precoder.

中文翻译：

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用于绿色 CoMP 系统中加权和速率最大化的在线迫零预编码器


遵循碳中和路线图，无线通信系统正在升级以使用来自可再生能源的绿色能源，例如阳光、潮汐和风能。由于绿色能源到来的波动性较大，因此采用并网能源作为绿色协调多点系统的备用。在这项工作中，通过预期长期并网能源的非正消耗，研究了绿色协调多点系统中的加权和率最大化问题。受容量实现特性和简单实现的启发，提出了一种在线迫零脏纸预编码器，通过将统计学习与李亚普诺夫学习技术相结合来更新预编码矩阵。理论上建立了一个权衡关系，表明长期加权和率接近最优值的 O(V){\mathcal{ O}}(V) -邻居，而长期上网电量以一定速率增加O(log2(V)/V√){\mathcal{ O}}({\scriptstyle {}^{\scriptstyle \log ^{2}(V)}}\hspace {-0.224em}/\hspace { -0.112em}{\scriptstyle \sqrt {V}}) ，其中 VV 是引入的控制参数。数值结果用于验证所提出的在线自适应预编码器的性能。