Data-driven power control of ultra-dense femtocells: A clustering based approach

Wang, Li-Chun; Cheng, Shao-Hung; Tsai, Ang-Hsun

doi:10.1109/wocc.2017.7928996

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…The proposed algorithm predicts the change tendency of the path loss values of UEs. A clustering approach that groups users and femtocells based on line of sight connectivity was introduced in [18], while Wang et al [19] proposed a Data-Driven Power Control (DDPC) technique based on an Affinity Propagation (AP) clustering algorithm. It clusters the various femtocells based on the reference signal received power (RSRP).…”

Section: A Clustering Techniquesmentioning

confidence: 99%

A Machine Learning Solution for Video Delivery to Mitigate Co-Tier Interference in 5G HetNets

Anand,

Togou,

Muntean

2023

IEEE Trans. Multimedia

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The exponential demand for multimedia services is one reason behind the substantial growth of mobile data traffic. Video traffic patterns have significantly changed in the past two years due to the coronavirus disease . The worldwide pandemic has caused many individuals to work from home and use various online video platforms (e.g., Zoom, Google Meet, and Microsoft Teams). As a result, overloaded macrocells are unable to ensure high Quality of Experience (QoE) to all users. Heterogeneous Networks (HetNets) consisting of small cells (femtocells) and macrocells are a promising solution to mitigate this problem. A critical challenge with the deployment of femtocells in HetNets is the interference management between Macro Base Stations (MBSs), Femto Base Stations (FBSs), and between FBS and FBS. Indeed, the dynamic deployment of femtocells can lead to co-tier interference. With the rolling out of the 5G mobile network, it becomes imperative for mobile operators to maintain network capacity and manage different types of interference. Machine Learning (ML) is considered a promising solution to many challenges in 5G HetNets. In this paper, we propose a Machine Learning Interference Classification and Offloading Scheme (MLICOS) to address the problem of co-tier interference between femtocells for video delivery. Two versions of MLICOS, namely, MLICOS1 and MLICOS2, are proposed. The former uses conventional ML classifiers while the latter employs advanced ML algorithms. Both versions of MLICOS are compared with the classic Proportional Fair (PF) scheduling algorithm, Variable Radius and Proportional Fair scheduling (VR+PF) algorithm, and a Cognitive Approach (CA). The ML models are assessed based on the prediction accuracy, precision, recall and F-measure. Simulation results show that MLICOS outperforms the other schemes by providing the highest throughput and the lowest delay and packet loss ratio. A statistical analysis was also carried out to depict the degree of interference faced by users when different schemes are employed.

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Section: A Clustering Techniquesmentioning

confidence: 99%

A Machine Learning Solution for Video Delivery to Mitigate Co-Tier Interference in 5G HetNets

Anand,

Togou,

Muntean

2023

IEEE Trans. Multimedia

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“…The clustering of the femtocells is considered in order to address the interference challenge. An approach called data-driven power control (DDPC) was introduced in [ 28 ]. The DDPC scheme uses the affinity propagation (AP) clustering algorithm to cluster various femtocells based on collected data, such as reference signal received power (RSRP), for every femtocell’s UEs.…”

Section: Related Workmentioning

confidence: 99%

Power and Radio Resource Management in Femtocell Networks for Interference Mitigation

Alotaibi

Sinky

2021

Sensors

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The growth of mobile traffic volume has been exploded because of the rapid improvement of mobile devices and their applications. Heterogeneous networks (HetNets) can be an attractive solution in order to adopt the exponential growth of wireless data. Femtocell networks are accommodated within the concept of HetNets. The implementation of femtocell networks has been considered as an innovative approach that can improve the network’s capacity. However, dense implementation and installation of femtocells would introduce interference, which reduces the network’s performance. Interference occurs when two adjacent femtocells are operated with the same radio resources. In this work, a scheme, which comprises two stages, is proposed. The first step is to distribute radio resources among femtocells, where each femtocell can identify the source of the interference. A constructed table is generated in order to measure the level of interference for each femtocell. Accordingly, the level of interference for each sub-channel can be recognized by all femtocells. The second stage includes a mechanism that helps femtocell base stations adjust their transmission power autonomously to alleviate the interference. It enforces a cost function, which should be realized by each femtocell. The cost function is calculated based on the production of undesirable interference impact, which is introduced by each femtocell. Hence, the transmission power is adjusted autonomously, where undesirable interference can be monitored and alleviated. The proposed scheme is evaluated through a MATLAB simulation and compared with other approaches. The simulation results show an improvement in the network’s capacity. Furthermore, the unfavorable impact of the interference can be managed and alleviated.

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“…Our simulation results show that the proposed DDRM framework with power control and channel rearrangement can enhance energy efficiency and throughput in the plugand-play UDSC scenario, compared to the existing approach. In our previous work [12], we only designed power control process for enhancing the energy efficiency of UDSC, but sacrifice the QoS of the users at the cell edge.…”

Section: Objectives and Contributionsmentioning

confidence: 99%

Data-Driven Resource Management for Ultra-Dense Small Cells: An Affinity Propagation Clustering Approach

Wang

Cheng

2019

IEEE Trans. Netw. Sci. Eng.

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Deploying dense small cells is the key to providing high capacity, but raise the serious issue of energy consumption and inter-cell interference. To understand the behaviors of ultra-dense small cells (UDSC) with dynamic interference and traffic patterns, this paper presents a data-driven resource management (DDRM) framework to implement power control and channel rearrangement in UDSC. We find that the inter-cell interference can be used to describe the affinity of cells. Thus, we propose an unsupervised learning algorithm for UDSC, called affinity propagation power control (APPC) mechanism. In principle, APPC first groups small cells into different clusters and identifies cluster centers. Next, the transmission power of a cluster center is decreased to reduce the interference to the neighboring cells' users in this cluster. Since lowering transmission power of a cluster center cell may cause the performance degradation to the users at the cell edge, a victim-aware channel rearrangement (VACR) mechanism is further designed to adjust the channel usage bandwidth of the neighboring cells in order to guarantee the quality of service of these victimized users. Our simulation results show that the DDRM framework can significantly improve energy efficiency and throughput in UDSC compared to the existing approaches.

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Data-driven power control of ultra-dense femtocells: A clustering based approach

Cited by 7 publications

References 9 publications

A Machine Learning Solution for Video Delivery to Mitigate Co-Tier Interference in 5G HetNets

A Machine Learning Solution for Video Delivery to Mitigate Co-Tier Interference in 5G HetNets

Power and Radio Resource Management in Femtocell Networks for Interference Mitigation

Data-Driven Resource Management for Ultra-Dense Small Cells: An Affinity Propagation Clustering Approach

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