用于MIMO设备到设备干扰网络的代理增强分数规划
Surrogate-Enhanced Fractional Programming for MIMO Device-to-Device Interference Networks
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中文总结 AI 辅助
针对MIMO D2D干扰网络，提出矩阵SEFP框架，通过厄米函数演算将RIT扩展到矩阵比值，统一SEFP与XMMSE，并开发SEFPLinQ实现联合调度与波束成形，数值验证性能提升。
中文摘要 AI 辅助
多流多输入多输出（MIMO）设备到设备（D2D）网络中的干扰管理通常导致加权和速率最大化问题，其中涉及包含矩阵值信干噪比的求和-对数-行列式。最先进的范式，包括加权最小均方误差（WMMSE）和分数规划（FP），在链路调度、功率控制和波束成形问题中已取得巨大成功。最近，一种升级的FP方法，即标量形式的代理增强FP（SEFP），在协调多小区SISO网络的联合上行链路调度和功率控制中获得了改进的性能。所提出的SEFP通过一种新颖的倒数-逆变换（RIT）改进了经典拉格朗日对偶变换加二次变换对对数分数目标的代理构造，但其扩展到MIMO设置的矩阵形式似乎并不直接，因为矩阵比值和辅助矩阵通常不可交换。本文通过利用厄米函数演算的数学工具，通过沿辅助矩阵的特征方向提升标量RIT并将所得方向构造重铸为算子形式，将RIT扩展到矩阵比值。随后，我们开发了一个用于加权和-对数-行列式最大化的矩阵SEFP框架。此外，我们建立了SEFP与最近提出的XMMSE方法的统一视角，明确了两种算法何时产生相同的极小化-极大化（MM）代理和变量更新轨迹，并证明了在算法族下XMMSE可被视为SEFP的特例。基于统一视角，我们开发了SEFPLinQ用于灵活关联MIMO D2D网络中的联合调度和波束成形优化。数值结果展示了一致的性能提升。
英文摘要
Interference management in multi-stream multi-input multi-output (MIMO) device-to-device (D2D) networks often leads to weighted sum-rate maximization with sum-log-determinant involving matrix-valued signal-to-interference-plus-noise ratios (SINRs). The state-of-the-art paradigms, including weighted minimum mean-square error (WMMSE) and fractional programming (FP), have achieved tremendous success in link scheduling, power control, and beamforming problems. Recently, an upgraded FP approach, named surrogate-enhanced FP (SEFP) in a scalar form, has attained improved performance in joint uplink scheduling and power control in coordinated multicell SISO networks. The proposed SEFP improves the surrogate construction of the classical Lagrangian dual transform plus quadratic transform for logarithmic fractional objectives with a novel reciprocal-inverse transform (RIT), yet its extension to the matrix form for MIMO settings does not seem straightforward because the matrix ratio and the auxiliary matrix generally do not commute. In this paper, by leveraging mathematical tools from Hermitian functional calculus, we extend RIT to matrix ratios by lifting the scalar RIT along the eigendirections of an auxiliary matrix and recasting the resulting directional construction in an operator form. Following, we develop a matrix SEFP framework for weighted sum-log-determinant maximization. Further, we establish a unified view of SEFP and the recently proposed XMMSE method, specify when the two algorithms attribute to identical minorization-maximization (MM) surrogates and variable update trajectories, and prove XMMSE can be considered as a special case of SEFP under the algorithmic family perspective. Inspired by the unified view, we develop SEFPLinQ for the joint scheduling and beamforming optimization in flexible-association MIMO D2D networks. Numerical results demonstrate consistent performance gains.
发表机构
- National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信重点实验室）
- Faculty of Electrical Engineering and Computer Science, Technical University of Berlin（柏林工业大学电气与计算机科学系）
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