An introduction to convex optimization for communications and signal processing

By Zhi-Quan Tom Luo, Wei Yu. IEEE Journal on Selected Areas in Communications, 2006

578 citations recorded by the source corpus.

Subfields: Computational Mechanics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d6f8c34acbbe9a434f66815c028c1c339482f6f4

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In Computational Mechanics

In Electrical and Electronic Engineering

Citations in the corpus

Fractional Programming for Communication Systems—Part I: Power Control and Beamforming (2018, background): “O PTIMIZATION is a key aspect of communication system design [3], [4].”
Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming (2018, methodology): “in the multiuser MISO downlink broadcast channel, which can be efficiently solved by using second-order cone program (SOCP) [22], SDP [23], or a fixed-point iteration algorithm based on the uplink-downlink duality [24], [25].”
Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs (2015, background): “For more details of convex optimization, please refer to [324], [495].”
Optimized Iterative Clipping and Filtering for PAPR Reduction of OFDM Signals (2011, unknown): “In recent years, convex optimization has been widely used in communication signal processing applications, including PAPR reduction of OFDM signals [5]–[10].”
Optimal beamforming for two-way multi-antenna relay channel with analogue network coding (2008, methodology): “A commonly adopted method in the literature to obtain a feasible rank-one (but in general suboptimal) solution from the solution of SDP relaxation is via “randomization” (see, e.g., [28] and references therein).”
Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface With Discrete Phase Shifts (2019, unknown): “Note that (P3) is the conventional power minimization problem in the multiuser MISO downlink broadcast channel, which can be efficiently and optimally solved by using the fixedpoint iteration algorithm based on the uplink-downlink duality [41]–[43].”
Coordinated Scheduling and Power Allocation in Downlink Multicell OFDMA Networks (2009, background): “On the other hand, for any given user selection k ∈ K, the corresponding optimal set of powers must satisfy the Karush– Kuhn–Tucker (KKT) conditions2 [33], [34].”
ECOS: An SOCP solver for embedded systems (2013, background): “4] for numerous examples), signal processing [3] and image reconstruction [4].”
Dynamic Resource Allocation in Cognitive Radio Networks (2010, result): “s. In particular, this article will focus on DRA problem formulations unique to CR systems under the IT-SS model, and the associated solutions that are non-obvious in comparison with existing results [11] known for the traditional (non-CR) wireless networks. More importantly, we will emphasize the k”
Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G (2021, methodology): “By making use of the convexity of the problem, both types of methods can yield the globally optimal solution with polynomial time complexity in general [143].”
Theory and Applications of Robust Optimization (2010, background): “ss channels with lognormal fading. For more on applications to communication, particularly the application of geometric programming, we refer the reader to the monograph [58], and the review articles [100, 101]. For applications to coordination schemes and power control in wireless channels, see [13”
Multiuser Joint Task Offloading and Resource Optimization in Proximate Clouds (2017, methodology): “Note that constraintC3 is slack based on Karush–Kuhn–Tucker conditions, which has already been eliminated in (25) [27], [29].”
Principles of minimum variance robust adaptive beamforming design (2013, unknown): “For the future discussion, it is worth mentioning that many modern RAB techniques are based on convex optimization theory [91].”
Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013, background): “2), while infeasibility might be difficult to detect for general nonconvex problems [167].”
Parameter Estimation of Statistical Models Using Convex Optimization (2010, unknown): “After that, Gaussian mean vector mk can be updated based on the found SDP solution Zk *.”
A tutorial on decomposition methods for network utility maximization (2006, background): “For details, the reader is referred to books [8], [9] and another tutorial in this special issue [10].”