Coordinates-Based Resource Allocation Through Supervised Machine Learning

Imtiaz, Sahar; Schiessl, Sebastian; Koudouridis, Georgios P.; Gross, James

doi:10.1109/tccn.2021.3072839

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…Ensemble learning has been also applied to unmanned aerial vehicles (UAVs) for UAV power modelling [45], and sustainable multimodal UAV classification [46]. Further applications include the exploitation of a deep ensemble-based wireless receiver architecture for mitigating adversarial attacks in automatic modulation classification [47], and coordinates-based resource allocation based on random forests [48,49].…”

Section: Ensemble Learningmentioning

confidence: 99%

An architecture and performance evaluation framework for artificial intelligence solutions in beyond 5G radio access networks

Koudouridis

Dán

2022

J Wireless Com Network

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The evolution of mobile communications towards beyond 5th-generation (B5G) networks is envisaged to incorporate high levels of network automation. Network automation requires the development of a network architecture that accommodates multiple solutions based on artificial intelligence (AI) and machine learning (ML). Consequently, integrating AI into the 5th-generation (5G) systems such that we could leverage the advantages of ML techniques to optimize and improve the networks is one challenging topic for B5G networks. Based on a review of 5G system architecture, the state-of-the-art candidate AI/ML techniques, and the progress of the state of the art, and the on AI/ML for 5G in standards we define an AI architecture and performance evaluation framework for the deployment of the AI/ML solution in B5G networks. The suggested framework proposes three AI architectures alternatives, a centralized, a completely decentralized and an hybrid AI architecture. More specifically, the framework identifies the logical AI functions, determines their mapping to the B5G radio access network architecture and analyses the associated deployment cost factors in terms of compute, communicate and store costs. The framework is evaluated based on a use case scenario for heterogeneous networks where it is shown that the deployment cost profiling is different for the different AI architecture alternatives, and that this cost should be considered for the deployment and selection of the AI/ML solution.

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Section: Ensemble Learningmentioning

confidence: 99%

An architecture and performance evaluation framework for artificial intelligence solutions in beyond 5G radio access networks

Koudouridis

Dán

2022

J Wireless Com Network

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“…While the bulk of information processing of any wireless transceiver is related to the physical layer, deep learning has been also considered higher up in the (wireless) network stack. Focusing again first on works that consider to substitute individual functions of the network stack, efforts have been made for instance with respect to performing resource allocation for 5G networks by DNNs (Imtiaz 2021). Further works consider channel allocation for dynamic spectrum access (Naparstek 2019) or focus on improving sensing/classifying accuracy for dynamic spectrum access systems (Davaslioglu 2018).…”

Section: Dnns For Higher-layer Functionalitymentioning

confidence: 99%

NeuroRAN Rethinking Virtualization for AI‐native Radio Access Networks in 6G

Carbone

Dán

Gross

et al. 2022

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Network softwarization has revolutionized the architecture of cellular wireless networks. State‐of‐the‐art container based virtual radio access networks (vRAN) provide enormous flexibility and reduced life‐cycle management costs, but they also come with prohibitive energy consumption. We argue that for future AI‐native wireless networks to be flexible and energy efficient, there is a need for a new abstraction in network softwarization that caters for neural network type of workloads and allows a large degree of service composability. In this paper we present the NeuroRAN architecture, which leverages stateful function as a user facing execution model, and is complemented with virtualized resources and decentralized resource management. We show that neural network based implementations of common transceiver functional blocks fit the proposed architecture, and we discuss key research challenges related to compilation and code generation, resource management, reliability and security.

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“…Thus, the design philosophy to resource allocation needs to change to overcome these challenges and complexities. Today, the use of machine learning methods to overcome the problems of the next generation has been the subject of much research 13‐15 …”

Section: Introductionmentioning

confidence: 99%

“…Today, the use of machine learning methods to overcome the problems of the next generation has been the subject of much research. [13][14][15] In wireless technology, radio resources are dynamically distributed based on real-time data of the user, namely their CSI and QoS needs. Inexpensive cloud storage saves the information as data on historical scenarios in the cloud space.…”

Section: Introductionmentioning

confidence: 99%

Optimizing resources allocation in a heterogeneous cloud radio access network using machine learning

Shirmohamadi

Bakhshi

Dosaranian‐Moghadam

2022

Trans Emerging Tel Tech

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In this article, we propose an iterative algorithm based on lazy learning. The proposed scheme considers two consecutive algorithms for optimal resource allocation in heterogeneous cloud radio access networks (H-CRAN). In the conventional approach, resources allocation is usually presented as a mathematical optimization problem and solved online using a real-time scenario. Due to the non-convex nature of resource allocation problems, finding optimal solutions in real-time is a difficult challenge and has high computational complexity. To overcome these challenges in this paper, the problem is first formulated as a mixed-integer nonlinear programming (MINLP) to optimize the system capacity and assign the underlay users to the subchannels, which is NP-hard. To obtain the solution, in the first algorithm, the proposed problem is first reformulated as a convex problem using a matching theory and coalition formation method. Then, in the second algorithm, the lazy learning method is employed to find the optimal solution to the resources allocation problem. The lazy algorithm uses the output results of the first algorithm as training data, which are known as historical data. Similarities between current scenario data and historical scenario data, as well as the solution of resources allocation in historical scenarios, can be exploited to improve the resource allocation of the current scenario. Simulation results confirm that compared with the conventional approach, the suggested technique can significantly improve the system performance in terms of the total users assigned, overall throughput, the pace of resource allocation, and user fairness.

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Coordinates-Based Resource Allocation Through Supervised Machine Learning

Cited by 8 publications

References 25 publications

An architecture and performance evaluation framework for artificial intelligence solutions in beyond 5G radio access networks

An architecture and performance evaluation framework for artificial intelligence solutions in beyond 5G radio access networks

NeuroRAN Rethinking Virtualization for AI‐native Radio Access Networks in 6G

Optimizing resources allocation in a heterogeneous cloud radio access network using machine learning

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