Autonomics in Radio Access Networks

Dirani, Mariana; Altman, Zwi; Salaun, Mikael

doi:10.1016/b978-0-12-382190-4.00007-3

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…Therefore, the concept of self-organizing networks (SON), outlined in LTE 3GPP TS 36.300 [19], is utilized in RRM techniques for adaptive solutions [20]. SON integration in UDHN has been also proven profitable and cost-effective for the network operators [21], [22]. However, the integration of SON in UDHN requires some source of cognition or intelligence which can be provided through reinforcement learning (RL).…”

Section: A Related Workmentioning

confidence: 99%

Optimal Learning Paradigm and Clustering for Effective Radio Resource Management in 5G HetNets

et al. 2023

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Ultra-dense heterogeneous networks (UDHN) based on small cells are a requisite part of the future cellular networks as they are proposed as one of the enabling technologies to handle coverage and capacity problems. But co-tier and cross-tier interferences in UDHN severely degrade the quality of service due to K-tiered architecture. Machine learning based radio resource management either through independent learning or cooperative learning is a proven efficient scheme for interference mitigation and quality of service provision in UDHN in a both distributive and cooperative manner. However, an optimal learning paradigm selection, i.e., either independent or cooperative learning and optimal cooperative cluster size in cooperative learning for efficient radio resource management in UDHN is still an open research problem. In this article, a Q-learning based radio resource management scheme is proposed and evaluated for both distributive and cooperative schemes using independent and cooperative learning. The proposed Q-learning solution follows the ϵ−greedy policy for optimal convergence. The simulation results for the UDHN in an urban setup show that in comparison to the independent learning paradigm, cooperative learning has no significant impact on macro cell user capacity. However, there is a significant improvement in small cell user capacity and the sum capacity of the cooperating small cells in the cluster. A significant increase of 48.57% and 37.9% is observed in the small cell user capacity, and sum capacity of the cooperating small cells, respectively, using cooperative learning as compared to independent learning which sets cooperative learning as an optimal learning strategy in UDHN. The improvement in small cell user capacity is at cost of increased computational time which is directly proportional to the number of cooperating small cells. To solve the issue of computational time in cooperative learning, an optimal clustering algorithm is proposed. The proposed optimal clustering reduced the computational time by four times in cooperative Q-learning.

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Section: A Related Workmentioning

confidence: 99%

Optimal Learning Paradigm and Clustering for Effective Radio Resource Management in 5G HetNets

et al. 2023

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“…The key motivation behind clustering the network, dividing the area into subareas (cells), and adopting a frequency reuse approach is to minimize interference [5]. We assume that the network has an FL server in the central of each cell and the nodes (i.e., IoMT participants) distribute in a PPP [6] of the server of intensity (λ) over uniform areas as shown in Figure 1.…”

Section: A Topological Considerationsmentioning

confidence: 99%

FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA

Salama

Zaidi

McLernon

et al. 2023

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)

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Federated Learning (FL) has emerged as a promising approach for privacy preservation, allowing sharing of the model parameters between users and the cloud server rather than the raw local data. FL approaches have been adopted as a cornerstone of distributed machine learning (ML) to solve several complex use cases. FL presents an interesting interplay between communication and ML performance when implemented over distributed wireless nodes. Both the dynamics of networking and learning play an important role. In this article, we investigate the performance of FL on an application that might be used to improve a remote healthcare system over ad hoc networks which employ CSMA/CA to schedule its transmissions. Our FL over CSMA/CA (FLCC) model is designed to eliminate untrusted devices and harness frequency reuse and spatial clustering techniques to improve the throughput required for coordinating a distributed implementation of FL in the wireless network.In our proposed model, frequency allocation is performed on the basis of spatial clustering performed using virtual cells. Each cell assigns an FL server and dedicated carrier frequencies to exchange the updated model's parameters within the cell. We present two metrics to evaluate the network performance: 1) probability of successful transmission while minimizing the interference, and 2) performance of distributed FL model in terms of accuracy and loss while considering the networking dynamics.We benchmark the proposed approach using a well-known MNIST dataset for performance evaluation. We demonstrate that the proposed approach outperforms the baseline FL algorithms in terms of explicitly defining the chosen users' criteria and achieving high accuracy in a robust network.

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