scholarly journals Laplace Functional Ordering of Point Processes in Large-Scale Wireless Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Junghoon Lee ◽  
Cihan Tepedelenlioğlu
Keyword(s):  
Resource Allocation ◽  
Monte Carlo ◽  
Wireless Networks ◽  
Coverage Probability ◽  
Large Scale ◽  
Point Processes ◽  
Performance Metrics ◽  
Stochastic Order ◽  
Stochastic Orders ◽  
Achievable Rate

Stochastic orders on point processes are partial orders which capture notions like being larger or more variable. Laplace functional ordering of point processes is a useful stochastic order for comparing spatial deployments of wireless networks. It is shown that the ordering of point processes is preserved under independent operations such as marking, thinning, clustering, superposition, and random translation. Laplace functional ordering can be used to establish comparisons of several performance metrics such as coverage probability, achievable rate, and resource allocation even when closed form expressions of such metrics are unavailable. Applications in several network scenarios are also provided where tradeoffs between coverage and interference as well as fairness and peakyness are studied. Monte-Carlo simulations are used to supplement our analytical results.

scholarly journals Stochastic Order and Generalized Weighted Mean Invariance

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 662
Author(s):  
Mateu Sbert ◽  
Jordi Poch ◽  
Shuning Chen ◽  
Víctor Elvira
Keyword(s):  
Monte Carlo ◽  
Probability Distributions ◽  
Stochastic Order ◽  
Arithmetic Mean ◽  
Stochastic Orders ◽  
Arithmetic Means ◽  
Increasing Convex Order ◽  
Monte Carlo Techniques ◽  
Multiple Importance Sampling ◽  
Invariance Properties

In this paper, we present order invariance theoretical results for weighted quasi-arithmetic means of a monotonic series of numbers. The quasi-arithmetic mean, or Kolmogorov–Nagumo mean, generalizes the classical mean and appears in many disciplines, from information theory to physics, from economics to traffic flow. Stochastic orders are defined on weights (or equivalently, discrete probability distributions). They were introduced to study risk in economics and decision theory, and recently have found utility in Monte Carlo techniques and in image processing. We show in this paper that, if two distributions of weights are ordered under first stochastic order, then for any monotonic series of numbers their weighted quasi-arithmetic means share the same order. This means for instance that arithmetic and harmonic mean for two different distributions of weights always have to be aligned if the weights are stochastically ordered, this is, either both means increase or both decrease. We explore the invariance properties when convex (concave) functions define both the quasi-arithmetic mean and the series of numbers, we show its relationship with increasing concave order and increasing convex order, and we observe the important role played by a new defined mirror property of stochastic orders. We also give some applications to entropy and cross-entropy and present an example of multiple importance sampling Monte Carlo technique that illustrates the usefulness and transversality of our approach. Invariance theorems are useful when a system is represented by a set of quasi-arithmetic means and we want to change the distribution of weights so that all means evolve in the same direction.

scholarly journals Backhaul-Aware Resource Allocation and Optimum Placement for UAV-Assisted Wireless Communication Network

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1397
Author(s):  
Yishi Xue ◽  
Bo Xu ◽  
Wenchao Xia ◽  
Jun Zhang ◽  
Hongbo Zhu
Keyword(s):  
Communication Network ◽  
Resource Allocation ◽  
Wireless Communication ◽  
Large Scale ◽  
Optimization Problem ◽  
Matrix Theory ◽  
Base Station ◽  
Achievable Rate ◽  
Multiple User ◽  
Wireless Communication Network

Driven by its agile maneuverability and deployment, the unmanned aerial vehicle (UAV) becomes a potential enabler of the terrestrial networks. In this paper, we consider downlink communications in a UAV-assisted wireless communication network, where a multi-antenna UAV assists the ground base station (GBS) to forward signals to multiple user equipments (UEs). The UAV is associated with the GBS through in-band wireless backhaul, which shares the spectrum resource with the access links between UEs and the UAV. The optimization problem is formulated to maximize the downlink ergodic sum-rate by jointly optimizing UAV placement, spectrum resource allocation and transmit power matrix of the UAV. The deterministic equivalents of UE’s achievable rate and backhaul capacity are first derived by utilizing large-dimensional random matrix theory, in which, only the slowly varying large-scale channel state information is required. An approximation problem of the joint optimization problem is then introduced based on the deterministic equivalents. Finally, an algorithm is proposed to obtain the optimal solution of the approximate problem. Simulation results are provided to validate the accuracy of the deterministic equivalents, and the effectiveness of the proposed method.

On the statistics of the linked stress release model

2001 ◽  
Vol 38 (A) ◽  
pp. 176-187 ◽  
Author(s):  
Mark Bebbington ◽  
David S. Harte
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Numerical Analysis ◽  
Large Scale ◽  
Point Processes ◽  
Information Criterion ◽  
Stress Release ◽  
Stress Release Model ◽  
Using Data ◽  
Release Model

The paper reviews the formulation of the linked stress release model for large scale seismicity together with aspects of its application. Using data from Taiwan for illustrative purposes, models can be selected and verified using tools that include Akaike's information criterion (AIC), numerical analysis, residual point processes and Monte Carlo simulation.

scholarly journals Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

2021 ◽  
Author(s):  
Abdulmalik Alwarafy ◽  
Mohamed Abdallah ◽  
Bekir Sait Ciftler ◽  
Ala Al-Fuqaha ◽  
Mounir Hamdi
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
Reinforcement Learning ◽  
Large Scale ◽  
Heterogeneous Wireless Networks ◽  
Future Research ◽  
Radio Resource Allocation ◽  
Next Generation ◽  
Radio Resource ◽  
Next Generation Wireless Networks

<div>Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.</div>

Data-Driven Joint Resource Allocation in Large-scale Heterogeneous Wireless Networks

IEEE Network ◽  
2020 ◽  
Vol 34 (3) ◽  
pp. 163-169
Author(s):  
Kai Lin ◽  
Chensi Li ◽  
Joel J. P. C. Rodrigues ◽  
Pasquale Pace ◽  
Giancarlo Fortino
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
Large Scale ◽  
Heterogeneous Wireless Networks ◽  
Data Driven ◽  
Joint Resource Allocation

scholarly journals Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

2021 ◽  
Author(s):  
Abdulmalik Alwarafy ◽  
Mohamed Abdallah ◽  
Bekir Sait Ciftler ◽  
Ala Al-Fuqaha ◽  
Mounir Hamdi
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
Reinforcement Learning ◽  
Large Scale ◽  
Heterogeneous Wireless Networks ◽  
Future Research ◽  
Radio Resource Allocation ◽  
Next Generation ◽  
Radio Resource ◽  
Next Generation Wireless Networks

<div>Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.</div>

New graph colouring algorithm for resource allocation in large-scale wireless networks

2014 ◽  
Author(s):  
Labeeb Mohsin Abdullah ◽  
Mohd Dani Baba ◽  
Sinan Ghassan Abid Ali ◽  
Azman Osman Lim ◽  
Yasuo Tan
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
Large Scale ◽  
Graph Colouring

On the statistics of the linked stress release model

2001 ◽  
Vol 38 (A) ◽  
pp. 176-187 ◽  
Author(s):  
Mark Bebbington ◽  
David S. Harte
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Numerical Analysis ◽  
Large Scale ◽  
Point Processes ◽  
Information Criterion ◽  
Stress Release ◽  
Stress Release Model ◽  
Using Data ◽  
Release Model

The paper reviews the formulation of the linked stress release model for large scale seismicity together with aspects of its application. Using data from Taiwan for illustrative purposes, models can be selected and verified using tools that include Akaike's information criterion (AIC), numerical analysis, residual point processes and Monte Carlo simulation.

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