On Telecommunication Service Imbalance and Infrastructure Resource
Deployment
- URL: http://arxiv.org/abs/2104.03948v1
- Date: Thu, 8 Apr 2021 17:45:32 GMT
- Title: On Telecommunication Service Imbalance and Infrastructure Resource
Deployment
- Authors: Chuanting Zhang, Shuping Dang, Basem Shihada, Mohamed-Slim Alouini
- Abstract summary: We propose a fine-grained and easy-to-compute imbalance index, aiming to quantitatively link the relation among telecommunication service imbalance, telecommunication infrastructure, and demographic distribution.
Based on this index, we also propose an infrastructure resource deployment strategy by minimizing the average imbalance index of any geographical segment.
- Score: 95.80185574417428
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The digital divide restricting the access of people living in developing
areas to the benefits of modern information and communications technologies has
become a major challenge and research focus. To well understand and finally
bridge the digital divide, we first need to discover a proper measure to
characterize and quantify the telecommunication service imbalance. In this
regard, we propose a fine-grained and easy-to-compute imbalance index, aiming
to quantitatively link the relation among telecommunication service imbalance,
telecommunication infrastructure, and demographic distribution. The
mathematically elegant and generic form of the imbalance index allows
consistent analyses for heterogeneous scenarios and can be easily tailored to
incorporate different telecommunication policies and application scenarios.
Based on this index, we also propose an infrastructure resource deployment
strategy by minimizing the average imbalance index of any geographical segment.
Experimental results verify the effectiveness of the proposed imbalance index
by showing a high degree of correlation to existing congeneric but
coarse-grained measures and the superiority of the infrastructure resource
deployment strategy.
Related papers
- Hypergame Theory for Decentralized Resource Allocation in Multi-user Semantic Communications [60.63472821600567]
A novel framework for decentralized computing and communication resource allocation in multiuser SC systems is proposed.
The challenge of efficiently allocating communication and computing resources is addressed through the application of Stackelberg hyper game theory.
Simulation results show that the proposed Stackelberg hyper game results in efficient usage of communication and computing resources.
arXiv Detail & Related papers (2024-09-26T15:55:59Z) - Distributed Event-Based Learning via ADMM [11.461617927469316]
We consider a distributed learning problem, where agents minimize a global objective function by exchanging information over a network.
Our approach has two distinct features: (i) It substantially reduces communication by triggering communication only when necessary, and (ii) it is agnostic to the data-distribution among the different agents.
arXiv Detail & Related papers (2024-05-17T08:30:28Z) - Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks [94.2860766709971]
We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a wireless network with statistically-identical agents.
Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies.
arXiv Detail & Related papers (2024-04-04T06:24:11Z) - Emergency Computing: An Adaptive Collaborative Inference Method Based on
Hierarchical Reinforcement Learning [14.929735103723573]
We propose an Emergency Network with Sensing, Communication, Computation, Caching, and Intelligence (E-SC3I)
The framework incorporates mechanisms for emergency computing, caching, integrated communication and sensing, and intelligence empowerment.
We specifically concentrate on emergency computing and propose an adaptive collaborative inference method (ACIM) based on hierarchical reinforcement learning.
arXiv Detail & Related papers (2024-02-03T13:28:35Z) - Compressed Regression over Adaptive Networks [58.79251288443156]
We derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem.
We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned online by the agents.
arXiv Detail & Related papers (2023-04-07T13:41:08Z) - Learning Resilient Radio Resource Management Policies with Graph Neural
Networks [124.89036526192268]
We formulate a resilient radio resource management problem with per-user minimum-capacity constraints.
We show that we can parameterize the user selection and power control policies using a finite set of parameters.
Thanks to such adaptation, our proposed method achieves a superior tradeoff between the average rate and the 5th percentile rate.
arXiv Detail & Related papers (2022-03-07T19:40:39Z) - Federated Learning over Wireless IoT Networks with Optimized
Communication and Resources [98.18365881575805]
Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention.
It is of interest to investigate fast responding and accurate FL schemes over wireless systems.
We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
arXiv Detail & Related papers (2021-10-22T13:25:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.