Statistical Resource Allocation Based on Cognitive Interference Estimation in Ultra-Dense HetNets

Liu, Fangfang; Zhao, Shulun

doi:10.1109/access.2020.2987360

Cited by 11 publications

(3 citation statements)

References 20 publications

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“…Resource allocation techniques basically help allocate resources in an efficient manner in HetNets, along with reducing the co-tier interference. In [27], the authors proposed a statistical resource allocation scheme that helps mitigate the cross-tier, co-tier and cross-link interference in ultra-dense heterogeneous networks. They considered Time Division Duplex (TDD) mode for uplink and downlink transmission, which can lead to cross-slot interference.…”

Section: Resource Allocationmentioning

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: Resource Allocationmentioning

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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“…As a result, the overall network capacity degradation has been considered one of the significant challenges of future wireless communication networks. Therefore, ultra-dense heterogeneous networks (HetNets) have been proposed as a promising approach to support the dramatic increase of wireless data and improve user experience [ 1 , 2 , 3 ]. The ultra-dense HetNets can significantly improve capacity and spectral efficiency with low transmission power resulting from enhanced frequency reuse [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

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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“…Also the Internet of Things (IoTs), vying on huge demand for wireless bandwidth finds a solution in the CR systems [6][7]. But wireless communication is prone to numerous challenges such as co-channel interference (CCI) [8] and multipath fading being the significant ones [9]. To palliate the adverse effects of fading, diversity combining schemes such as Equal gain combining (EGC), Maximal ratio combining (MRC) and Selection combining (SC) have been used traditionally [10].…”

mentioning

confidence: 99%

Comparative Analysis of Diversity Combining Schemes in Power Limited Cognitive Radio under the impact of Co-Channel Interference

Bindra

Sood

Khanna

et al. 2023

Preprint

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The authors investigate the performance of various diversity combining schemes in Internet of Things- Cognitive Radios (IOT-CR) system under the influence of co-channel interference limited environment. An approximate expression for probability density function (PDF) of maximum signal to interference ratio (SIR) at the receiver has been derived. By utilizing the derived PDF, closed loop expressions for Average post processing SIR, Ergodic capacity and Average bit error rate (ABER) have been derived. The proposed CR system is studied and analyzed under the impact of Peak transmit power (PTP) and Peak interference power (PIP) constraints. The achieved result shows that the Optimum combining (OC) at the receiver outperforms the traditional combining schemes in co-channel interference (CCI) scenario.

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Statistical Resource Allocation Based on Cognitive Interference Estimation in Ultra-Dense HetNets

Cited by 11 publications

References 20 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

Comparative Analysis of Diversity Combining Schemes in Power Limited Cognitive Radio under the impact of Co-Channel Interference

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