Dynamic Spectrum Management: Complexity and Duality

By Zhi-Quan Tom Luo, Shuzhong Zhang. IEEE Journal on Selected Topics in Signal Processing, 2008

943 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/db05a1d161ddd3b3d210a896194f430d1e4f9e6b

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Citations in the corpus

Sub-Channel Assignment, Power Allocation, and User Scheduling for Non-Orthogonal Multiple Access Networks (2016, background): “Since the channel coefficients vary in different channels due to frequency sel ectivity, the noise power is nonconstant if normalized, which makes it difficult to convert the proble m into a convex one [29].”
Full-Duplex Communication for ISAC: Joint Beamforming and Power Optimization (2022, background): “Compared to the power minimization criterion, (12) focuses on the sum rate maximization which is generally NP-hard, even in communication-only systems [37], [38].”
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks (2018, background): “Problem (5) is in general nonconvex and has been shown to be NP-hard [22].”
Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both? (2019, background): “allocation for sum-rate maximization is known to be NP-hard in interference-limited networks [78], which implies that also beamforming problems and energy efficiency maximization problems are NP-hard [3] as well.”
An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel (2011, background): “From the optimization’s perspective, this problem is nonconvex and NP-hard even in the single-antenna case [1].”
Asynchronous Incremental Stochastic Dual Descent Algorithm for Network Resource Allocation (2017, background): “Although such problems are infinite dimensional in nature, they can be solved in an online fashion via stochastic dual descent methods, allowing real-time resource allocation that is also asymptotically near-optimal [8]–[12].”
Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013, background): “transmission scheme is generally difficult to obtain [157, 168], even in those single-cell scenarios where the capacity region is fully characterized.”
Dynamic Resource Allocation in Cognitive Radio Networks (2010, background): “Furthermore, for the class of DRA problems in the form of (P6), the associated time-sharing conditions are usually satisfied asymptotically as L S ` under some cautious considerations on the continuity of channel distributions [46].”
Optimal Joint Base Station Assignment and Beamforming for Heterogeneous Networks (2014, background): “(5) As it is shown in the following table, many popular utility functions belong to [23], [23].”
Learning to Optimize: Training Deep Neural Networks for Interference Management (2017, background): “Problem (1) is known to be NP-hard [1].”
Learning to Cooperate in Decentralized Wireless Networks (2018, background): “This optimization problem is NP-hard and non-convex problem even when the knowledge of perfect channel gain information (PCGI) is available at all Tx-Rx pairs [12].”
A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization (2012, positive): “It has been shown in [20] that solving (40) is NP-hard.”
Two-Timescale Channel Estimation for Reconfigurable Intelligent Surface Aided Wireless Communications (2019, background): “The precoding problem for multi-user MIMO is proven to be non-convex and NP-hard [9], hence researchers tried to find the near-optimal joint-beamforming solutions for RISaided communications [10], [11].”
Energy- and Spectral-Efficiency Tradeoff in Downlink OFDMA Networks (2011, background): “And the duality gap is already quite small for practical number of subcarriers as observed by simulation [14]–[17], [19], [20].”