arXiv research

Develops deep learning for optimizing 5G radio resource allocation.

This paper presents a novel deep reinforcement learning-based resource allocation technique for the multi-agent environment presented by a cognitive radio network that coexists through underlay dynamic spectrum access (DSA) with a primary network. The resource allocation technique presented in this work is distributed,…

Meta-DRL improves resource allocation in O-RAN networks.

New RL approach optimizes resource allocation in multiservice networks.

Graph neural networks optimize radio resource management policies for wireless networks.

Interference among concurrent transmissions in a wireless network is a key factor limiting the system performance. One way to alleviate this problem is to manage the radio resources in order to maximize either the average or the worst-case performance. However, joint consideration of both metrics is often neglected as …

Supporting ultra-reliable low-latency communications (URLLC) is a major challenge of 5G wireless networks. Stringent delay and reliability requirements need to be satisfied for both scheduled and non-scheduled URLLC traffic to enable a diverse set of 5G applications. Although physical and media access control layer sol…

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicin…

Radio on Free Space Optics (RoFSO), as a universal platform for heterogeneous wireless services, is able to transmit multiple radio frequency signals at high rates in free space optical networks. This paper investigates the optimal design of power allocation for Wavelength Division Multiplexing (WDM) transmission in Ro…

Optimizes wireless network resource management with state-augmented policies.

In this paper, we introduce the problem of decision-oriented communications, that is, the goal of the source is to send the right amount of information in order for the intended destination to execute a task. More specifically, we restrict our attention to how the source should quantize information so that the destinat…

Mobile edge learning is an emerging technique that enables distributed edge devices to collaborate in training shared machine learning models by exploiting their local data samples and communication and computation resources. To deal with the straggler dilemma issue faced in this technique, this paper proposes a new de…

Optimal resource allocation in censored semi-bandits with unknown thresholds.

Optimizes data power control in cell-free networks for better spectral efficiency.

Study resource allocation strategies in sequential decisions with unknown rewards.

Paper tackles pandemic resource allocation challenges.

Study optimizes resource allocation in noisy systems for better control.

New loss function reduces outage probability in ML-assisted resource allocation.

Resource allocation improved using machine learning from terminal positions.

We study classification problems where features are corrupted by noise and where the magnitude of the noise in each feature is influenced by the resources allocated to its acquisition. This is the case, for example, when multiple sensors share a common resource (power, bandwidth, attention, etc.). We develop a method f…

Unified formula for arbitrary liquidity operations in weighted AMMs

This paper proposes a decentralized reinforcement learning method for multi-agent resource allocation.

Generative profiling improves real-time task timing for varied resource contexts.

Driven by the tremendous technological advancement of personal devices and the prevalence of wireless mobile network accesses, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a better viewers quality of experience (QoE) is the key to maximize the audiences number and increase streaming pro…

Optimal online learning for joint pricing and resource allocation.

The paper tackles resource allocation for arms with unknown and random rewards, achieving optimal regret bounds.

The paper evaluates index-based allocation policies using data from randomized control trials.

ATA optimizes task allocation in distributed machine learning.

We study a sequential resource allocation problem between a fixed number of arms. On each iteration the algorithm distributes a resource among the arms in order to maximize the expected success rate. Allocating more of the resource to a given arm increases the probability that it succeeds, yet with a cut-off. We follow…

RL optimizes resource allocation in MG by balancing experience and exploration.

This article proposes and evaluates a technique to predict the level of interference in wireless networks. We design a recursive predictor that estimates future interference values by filtering measured interference at a given location. The predictor's parameterization is done offline by translating the autocorrelation…

In urban environments, supply resources have to be constantly matched to the "right" locations (where customer demand is present) so as to improve quality of life. For instance, ambulances have to be matched to base stations regularly so as to reduce response time for emergency incidents in EMS (Emergency Management Sy…

Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements. The present paper leverages online learning advances to facilitate stochastic resource allocation tasks. By recognizing the central role of Lagrange multipliers, the unde…

Improved resource allocation method reduces procurement costs.

PASHA optimizes model tuning for large datasets with limited resources.

Study develops a smart contract framework for efficient and fair resource allocation.

New estimator improves policy evaluation in resource allocation RCTs.

Some agent-based models for growth and allocation of resources are described. The first class considered consists of conservative models, where the number of agents and the size of resources are constant during time evolution. The second class is made up of multiplicative noise models and some of their extensions to co…

Framework for online resource allocation using social welfare functions.

Paper proposes OPF policy for fair resource allocation with sublinear regret.

Designs for allocating resources to prioritize needy applicants while estimating treatment effects.

The virtualization of compute and network resources enables an unseen flexibility for deploying network services. A wide spectrum of emerging technologies allows an ever-growing range of orchestration possibilities in cloud-based environments. But in this context it remains challenging to rhyme dynamic cloud configurat…

New algorithm tackles resource allocation in multi-armed bandits to balance speed and throughput.

Algorithm allocates perishable resources online to minimize envy and inefficiency.

This work tackles resource allocation in asynchronous and stochastic systems.

This paper considers the design of optimal resource allocation policies in wireless communication systems which are generically modeled as a functional optimization problem with stochastic constraints. These optimization problems have the structure of a learning problem in which the statistical loss appears as a constr…

Study efficient resource allocation for detecting extreme values.

This paper optimizes cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.