An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel

By Qingjiang Shi, Meisam Razaviyayn, Zhi-Quan Tom Luo, Chen He. 2011

2,193 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

Deep Learning for Distributed Optimization: Applications to Wireless Resource Management (2019, methodology): “• WMMSE: A locally optimal solution for (P4) is obtained by using the WMMSE power control algorithm [18] with the initial point p i = P A , ∀ i .”
A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization (2012, positive): “Deﬁne In iteration 2 r + 1 , the algorithm solves the following problem In iteration 2 r + 2 , the algorithm solves the following (unconstrained) problem The above BSUM algorithm for solving (40) is called WMMSE algorithm in the reference [28].”
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis (2020, methodology): “Here, we consider WMMSE [18] in the original paper’s setting, which includes (5) as a special case.”
Learning to Rest: A Q-Learning Approach to Flying Base Station Trajectory Design with Landing Spots (2018, methodology): “• Centralized WMMSE: Power is centrally determined with WMMSE scheme based on the perfect CGI using [13].”
Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks (2019, methodology): “This problem has been studied extensively in the literature, and one famous method to obtain the stationary solution is the WMMSE algorithm with the following iterative updating rule [47]:”
An Overview of Signal Processing Techniques for RIS/IRS-Aided Wireless Systems (2021, neutral): “Due to the usually complicated expressions for the data rate/SINR, advanced algorithms such as the weighted minimum mean-square error (WMMSE) algorithm [81] or fractional programming (FP) [82] are often used to convert the original intractable problem into a new tractable but approximated problem.”
Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication (2018, methodology): “In this letter, we assume that the transmit power of the DUE is initialized using the transmit power of a WMMSE scheme [10], where the SE of the DUE is maximized without considering the interference caused to the CUE.”
Optimal Joint Base Station Assignment and Beamforming for Heterogeneous Networks (2014, methodology): “To implement the WMMSE algorithm with greedy base station assignment, we assume that each user can sense the strongest channel.”
Fractional Programming for Communication Systems—Part II: Uplink Scheduling via Matching (2018, methodology): “Following [28], we evaluate the algorithm complexity with respect to each round of iteration.”
Intelligent Reflecting Surface Enhanced Wireless Networks: Two-Timescale Beamforming Optimization (2019, methodology): “On the other hand, with fixed IRS phase shifts, the short-term transmit precoding optimization problems over different channel realizations are efficiently solved by applying the weighted minimum mean-squared error (WMMSE) algorithm [28].”
Movable-Antenna Enhanced Multiuser Communication via Antenna Position Optimization (2023, methodology): “To evaluate the performance gain provided by MAs, it is essential to characterize the channel variation between the antenna array of the BS and each user’s antenna moving in a given 3D spatial region.”
A Deep Learning Framework for Optimization of MISO Downlink Beamforming (2019, methodology): “P3 is a difﬁcult nonconvex problem and is usually solved using the iterative WMMSE approach [9, 10], therefore, supervised learning alone is insufﬁcient for this case.”
Exploiting Known Interference as Green Signal Power for Downlink Beamforming Optimization (2015, background): “Recent works have focused on the utility maximization in MIMO interfering broadcast channels [23], [24] and full duplex radio [25].”
Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning (2017, methodology): “This method is referred to as cyclic minimization and appears in applications such as [37], [62], [93]–[96].”
A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications (2023, methodology): “The equivalent matrix-weighted MMSE was applied to transform the sum-rate maximization objective to a simpler equivalent objective, where the equivalent matrix-weighted MMSE problem was convex to the optimized variables [192].”
Learning to optimize: Training deep neural networks for wireless resource management (2017, methodology): “…water-ﬁlling type algorithms [3], [4], interference pricing [5], SCALE [6]), transmit/receive beamformer design (e.g., the WMMSE algorithm [7], pricing-based schemes [8], semideﬁnite relaxation based schemes [9]), admission M. Hong is supported in part by NSF under grant CCF-1526078,…”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “use a deep neural network to approximate the mapping between the input and output of the Weighted Minimum Mean Square Error resource allocation algorithm [400], in interference-limited wireless network environments [301].”
Constrained Deep Learning for Wireless Resource Management (2019, methodology): “In such a case, a local optimal solution for (P3) can be found by the WMMSE algorithm [7].”
Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems (2013, methodology): “on the best-response (termed MIMO-SJBR) with those of the schemes proposed in the literature for a similar problem, namely the MIMO-MDP [23], [24], and the MIMOWMMSE [30].”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, neutral): “minimum mean-square error (WMMSE) method for multiuser MIMO precoding design in [214], the GNN-based scheme has a significant complexity reduction.”