Centralized and Distributed Algorithms for Energy and Spectrum Efficient User Association in Small Cell Networks

Javad-Kalbasi, Mohammad; Valaee, Shahrokh

doi:10.1109/tgcn.2021.3100358

Cited by 8 publications

(2 citation statements)

References 20 publications

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“…The authors in [12] compared both centralized and distributed algorithms regarding the BBU hotel location problem in C-RAN where their proposed solution is based on a distributed heuristic algorithm. Moreover, in [13], the authors compared the energy efficiency of their proposed distributed and centralized user association algorithms by sequentially minimizing the power consumption of the heterogeneous network they considered. In each of the aforementioned works, resource allocation was performed in either a centralized or distributed framework; none of the works considered the possibility of dynamically changing the framework.…”

Section: A Resource Allocation For Wireless Communicationmentioning

confidence: 99%

Toward a Smart Resource Allocation Policy via Artificial Intelligence in 6G Networks: Centralized or Decentralized?

Nouruzi¹,

Rezaei²,

Khalili³

et al. 2022

Preprint

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In this paper, we design a new smart softwaredefined radio access network (RAN) architecture with important properties like flexibility and traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation framework for the proposed smart soft-RAN model, where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation architectures to perform decision-making intelligently. In this paper, our aim is to make the network more scalable and more flexible in terms of achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, throughput overhead complexity (TOC), for the proposed machine learning-based algorithm, which makes a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL), which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic method, which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in other state-of-the-art network designs.

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Section: A Resource Allocation For Wireless Communicationmentioning

confidence: 99%

Toward a Smart Resource Allocation Policy via Artificial Intelligence in 6G Networks: Centralized or Decentralized?

Nouruzi¹,

Rezaei²,

Khalili³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The authors in [7] compared both centralized and distributed algorithms regarding the base band unit (BBU) location placement problem in C-RAN, where their proposed solution is based on a distributed heuristic algorithm. Moreover, in [8], the authors compared the energy efficiency of their proposed distributed and centralized user association algorithms by sequentially minimizing the power consumption of the heterogeneous network.…”

Section: Introductionmentioning

confidence: 99%

Smart Resource Allocation Model via Artificial Intelligence in Software Defined 6G Networks

Nouruzi¹,

Rezaei²,

Khalili³

et al. 2023

Preprint

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In this paper, we design a new flexible smart softwaredefined radio access network (Soft-RAN) architecture with traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation model for the proposed smart soft-RAN model where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation procedures to intelligently perform decisionmaking. In this paper, our aim is to make the network more scalable and more flexible in terms of conflicting performance indicators such as achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, i.e, throughputoverhead-complexity (TOC), for the proposed machine learningbased algorithm, which supports a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL) which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic (SAC) method which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in recent state-of-the-art network designs.The work of Eduard Jorswieck was supported in part by the Federal Ministry of Education and Research (BMBF, Germany) in the program of "Souverän. Digital. Vernetzt." joint project 6G-RIC, project identification number: 16KISK020K and 16KISK031.

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A novel approach to fairness-aware energy efficiency in green heterogeneous cellular networks

2022

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Centralized and Distributed Algorithms for Energy and Spectrum Efficient User Association in Small Cell Networks

Cited by 8 publications

References 20 publications

Toward a Smart Resource Allocation Policy via Artificial Intelligence in 6G Networks: Centralized or Decentralized?

Toward a Smart Resource Allocation Policy via Artificial Intelligence in 6G Networks: Centralized or Decentralized?

Smart Resource Allocation Model via Artificial Intelligence in Software Defined 6G Networks

A novel approach to fairness-aware energy efficiency in green heterogeneous cellular networks

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