Improved MIMO device network optimization with surrogate-enhanced fractional programming
Surrogate-Enhanced Fractional Programming for MIMO Device-to-Device Interference Networks
Information Theory
Summary
Managing interference in wireless networks where multiple devices communicate directly is tricky because signals can get mixed up. The authors build on a mathematical method called surrogate-enhanced fractional programming (SEFP) to handle this problem more efficiently for complex MIMO networks, where devices send multiple data streams simultaneously. They extend techniques from simpler cases to the more complex matrix-valued signals, improving how joint scheduling and beamforming decisions are made. Their new method shows better performance in simulations compared to previous approaches.
What this means in practice
- •For network schedulers: Optimize scheduling and beamforming in MIMO device-to-device wireless networks to improve data rates under interference.
- •For wireless network designers: Develop improved algorithms for power control and signal coordination in flexible-association MIMO D2D systems to enhance network efficiency.
Authors
Zihan Jiao, Xinping Yi, Shi Jin, Giuseppe Caire
Abstract
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. 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.