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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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25.0%50.0%75.0%100.0% · Feb 199419922001200920182026
48 results for Sum-Rate Maximization

Study improves efficiency of MIMO systems' sum rate estimation.

problem Maximizing sum rate in MIMO systems with PAPC constraints.
method Proposes two new low-complexity approaches: alternating optimization and machine learning.
result Demonstrates superior performance compared to existing methods.

Deep learning optimizes user association in Massive MIMO networks.

problem Optimizing user cell association for maximum sum-rate in Massive MIMO networks.
method Training a deep neural network to learn optimal association rules based on user positions.
result The neural network achieves the same performance as traditional optimization methods with reduced computational complexity.

Deep learning optimizes constellation for two-way relaying networks, boosting sum rate.

problem Optimizing constellation for better performance in two-way relaying networks.
method Deep neural networks (DNNs) trained to minimize cross entropy loss for direct constellation optimization.
result Significant performance gain in achievable sum rate compared to conventional relaying schemes.

The paper tackles decision-oriented communications for energy-efficient resource allocation.

problem Maximizing utility functions under quantized information.
method Develops solutions for quantizing information to maximize utility functions under known and observed conditions.
result Quantizing the state roughly is optimal for sum-rate maximization but not for energy-efficiency metrics.

A deep neural network (DNN) based power control method is proposed, which aims at solving the non-convex optimization problem of maximizing the sum rate of a multi-user interference channel. Towards this end, we first present PCNet, which is a multi-layer fully connected neural network that is specifically designed for…

2018-07-26abs ↗pdf ↗

This letter tackles channel assignment in uplink wireless communication systems.

problem Maximizing the sum rate of all users in uplink wireless communication systems with integer channel assignment constraints.
method A convex optimization based algorithm is used to find the optimal channel assignment. Machine learning approaches, including CNNs, FNNs, random forest, and GRUs, are employed to reduce computation time.
result Machine learning methods largely reduce computation time with slightly compromised prediction accuracy.

This paper studies communication efficiency in federated learning by optimizing the sum-rate-distortion function for indirect multiterminal source coding.

problem Indirect multiterminal source coding in federated learning where edge devices send noisy gradients to the server.
method Analyzes the rate region for the quadratic vector Gaussian CEO problem under unbiased estimator and derives an explicit formula for the sum-rate-distortion function.
result Derives an explicit formula for the sum-rate-distortion function in the special case of identical gradients over edge devices.

Dynamic cell-free networks reduce complexity in serving many devices with distributed APs and DRL.

problem Designing efficient cell-free networks with many devices and APs.
method Dynamic architecture, SIC, DAS, DRL for optimization.
result DRL significantly improves performance in dynamic cell-free networks.

Graph neural network optimizes energy-efficient precoding for massive MIMO systems.

problem Energy bottleneck in massive MIMO systems due to high DAC complexity and power consumption.
method Proposes a graph neural network to directly output precoded quantized vectors from channel matrix and transmit symbols.
result Significant increase in achievable sum rate with reduced DAC power consumption.

Deep actor-critic learning optimizes power control in mobile networks.

problem Optimizing power control in large-scale wireless mobile networks.
method Multi-agent deep reinforcement learning with deep deterministic policy gradient.
result The algorithm maximizes a global utility function in a distributed manner.

Paper studies fundamental limits of communication in distributed learning.

problem Communication efficiency in model aggregation for distributed learning.
method Rate-Distortion approach to model aggregation as a vector Gaussian CEO problem.
result Derives rate region bound and sum-rate-distortion function for model aggregation.

Study finds Hilbert square of real surfaces can be maximal even when the surface has disconnected real locus.

problem Exploring conditions for maximality of Hilbert square of real surfaces.
method Analyzing Hilbert square of maximal real surfaces and examining specific examples.
result Hilbert square can be maximal even for surfaces with disconnected real locus.

The study classifies area-maximizing hypersurfaces with singularities and exterior domains.

problem Classifying area-maximizing hypersurfaces with singularities and exterior domains.
method Complete classification for entire area maximizing hypersurfaces with isolated singularities. Construction of an example. Partial result on asymptotic behavior for exterior domains. Solvability of exterior Dirichlet problems.
result Complete classification and partial results on asymptotic behavior for area maximizing hypersurfaces.

Maximal acceleration metrics limit spacetime curvature.

problem Bounding spacetime curvature under maximal acceleration.
method Developed a geometric framework for maximal acceleration metrics and associated connections, proving curvature bounds.
result Uniform bounds on curvature components follow from uniform bounds on maximal acceleration.

Study examines maximal domains of radial harmonic functions across different curvature types.

problem Understanding maximal domains of radial harmonic functions in various curvature settings.
method Analysis of harmonic spaces with positive, zero, and negative curvature.
result Characterization of maximal domains for radial harmonic functions in different curvature contexts.

The paper explores reflection principles for lightlike line segments on maximal surfaces.

problem Reflection property does not hold for lightlike line segments on maximal surfaces.
method Analyzes reflection properties for lightlike line segments connecting shrinking singularities.
result Shows a kind of reflection principle for lightlike line segments on maximal surfaces.

We shall investigate maximal surfaces in Minkowski 3-space with singularities. Although the plane is the only complete maximal surface without singular points, there are many other complete maximal surfaces with singularities and we show that they satisfy an Osserman-type inequality.

2003-07-23abs ↗pdf ↗

The study finds that maximizing median returns is the only viable strategy in portfolio selection.

problem Difficulties in studying optimal portfolio strategies due to discontinuity and time inconsistency in maximizing median and quantile returns.
method Used intra-personal equilibrium approach to analyze portfolio selection under median and quantile maximization.
result Median maximization is the only viable strategy, with no investment in risky assets for other quantiles.

Study of large group actions on surfaces, focusing on Hurwitz and handlebody groups.

problem Characterizing and understanding group actions on surfaces, especially maximal handlebody and Hurwitz groups.
method Analyzing various group actions, comparing Hurwitz and handlebody groups, and examining bounding actions.
result Relationship between Hurwitz groups and maximal handlebody groups, and insights into geometric bounding actions.

New guarantees for adaptive combinatorial maximization with various objectives.

problem Maximizing under cardinality constraints and minimum cost coverage in adaptive settings.
method Bayesian approach with comprehensive approximation guarantees for various utility functions.
result Maximal gain ratio is a new parameter that provides stronger approximation guarantees than greedy policies.

We study homologically maximizing timelike geodesics in conformally flat tori. A causal geodesic γγ in such a torus is said to be homologically maximizing if one (hence every) lift of γγ to the universal cover is arclength maximizing. First we prove a compactness result for homologically maximizing timelike geodesics…

2010-03-11abs ↗pdf ↗

Fast algorithms developed for adaptive and fully adaptive submodular maximization problems.

problem Maximizing submodular functions subject to constraints in linear time.
method Developed linear-time algorithms for two submodular maximization problems: adaptive and fully adaptive.
result Achieved (11/eε)(1-1/e-ε) approximation ratio for adaptive submodular maximization and $ rac{1-1/e-ε}{4-2/e-2ε}$ for fully adaptive submodular maximization.

We show that a positive braid knot has maximal topological 4-genus exactly if it has maximal signature invariant. As an application, we determine all positive braid knots with maximal topological 4-genus and compute the topological 4-genus for all positive braid knots with up to 12 crossings.

2015-11-12abs ↗pdf ↗

Maximal index vanishes for certain spin manifolds with positive scalar curvature.

problem Maximal index vanishing for spin manifolds with positive scalar curvature.
method Functional calculus for Dirac operator in maximal equivariant uniform Roe algebra.
result Maximal higher index vanishes in K-theory of maximal equivariant Roe algebra.

The geometry and topology of complete nonorientable maximal surfaces with lightlike singularities in the Lorentz-Minkowski 3-space are studied. Some topological congruence formulae for surfaces of this kind are obtained. As a consequence, some existence and uniqueness results for maximal Moebius strips and maximal Klei…

2009-05-13abs ↗pdf ↗

Study utility maximization with costs, proving convergence and strategies.

problem Utility maximization with proportional transaction costs.
method Extended weak convergence theory and Meyer--Zheng topology.
result Prove convergence of utility maximization problems and optimal trading strategies.

Differentially private algorithms for submodular maximization under various constraints.

problem Maximizing decomposable submodular functions under constraints while preserving privacy.
method Designing differentially private algorithms for both monotone and non-monotone decomposable submodular maximization under general matroid constraints.
result Improved utility guarantees and competitive performance compared to non-private algorithms.