Mitigating the Impact of Cross-Tier Interference on Quality in Heterogeneous Cellular Networks

Agarwal, Bharat; Togou, Mohammed Amine; Ruffini, Marco; Muntean, Gabriel‐Miro

doi:10.1109/lcn48667.2020.9314834

Cited by 11 publications

(14 citation statements)

References 12 publications

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“…• QoS: QoS measures the networks' transport performance related to a service. QoS is generally not linked to a client, but to content delivery or network support [49]. QoS can be quantitatively measured in terms of metrics such as delay, throughput, jitter and packet loss ratio (PLR).…”

Section: Rrm Taxonomymentioning

confidence: 99%

“…• QoE: QoE is a measure of the pleasure or frustration associated with the experience a customer has with a service. QoE is a strictly subjective indicator from the point of view of the consumer [49]. QoE provision can be qualitatively measured in terms of metrics that include peak-to-signal-noise-ratio (PSNR) [49], structural similarity identity matrix (SSIM) [49], visual multi-method opinion score (VMAF) [51] and mean opinion score (MoS) [49].…”

Section: Rrm Taxonomymentioning

confidence: 99%

“…QoE is a strictly subjective indicator from the point of view of the consumer [49]. QoE provision can be qualitatively measured in terms of metrics that include peak-to-signal-noise-ratio (PSNR) [49], structural similarity identity matrix (SSIM) [49], visual multi-method opinion score (VMAF) [51] and mean opinion score (MoS) [49]. The third category of the proposed ontology includes different models adopted by various schemes to address open challenges.…”

Section: Rrm Taxonomymentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Survey on Radio Resource Management in 5G HetNets: Current Solutions, Future Trends and Open Issues

Agarwal

Togou

Ruffini

et al. 2022

IEEE Commun. Surv. Tutorials

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The 5G network technologies are intended to accommodate innovative services with a large influx of data traffic with lower energy consumption and increased quality of service and user quality of experience levels. In order to meet 5G expectations, heterogeneous networks (HetNets) have been introduced. They involve deployment of additional low power nodes within the coverage area of conventional high power nodes and their placement closer to user underlay HetNets. Due to the increased density of small-cell networks and radio access technologies, radio resource management (RRM) for potential 5G HetNets has emerged as a critical avenue. It plays a pivotal role in enhancing spectrum utilization, load balancing, and network energy efficiency. In this paper, we summarize the key challenges i.e., cross-tier interference, co-tier interference, and user association-resource-power allocation (UA-RA-PA) emerging in 5G HetNets and highlight their significance. In addition, we present a comprehensive survey of RRM schemes based on interference management (IM), UA-RA-PA and combined approaches (UA-RA-PA + IM). We introduce a taxonomy for individual (IM, UA-RA-PA) and combined approaches as a framework for systematically studying the existing schemes. These schemes are also qualitatively analyzed and compared to each other. Finally, challenges and opportunities for RRM in 5G are outlined, and design guidelines along with possible solutions for advanced mechanisms are presented.

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Section: Rrm Taxonomymentioning

confidence: 99%

Section: Rrm Taxonomymentioning

confidence: 99%

Section: Rrm Taxonomymentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey on Radio Resource Management in 5G HetNets: Current Solutions, Future Trends and Open Issues

Agarwal

Togou

Ruffini

et al. 2022

IEEE Commun. Surv. Tutorials

Self Cite

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“…The authors of this paper have previously introduced a novel quality efficient femtocell offloading scheme (QEFOS) which mitigates the effect of interferences and improves QoS and user quality of experience (QoE) [23] and a preliminary version of an enhanced SA for solving the UA-RA problem as a MKP, which this paper extends [24]. Many works suggest solving UA-RA problems, but none of the proposed work considers the TBS index, user demand, and maximum BS capacity in a single problem.…”

Section: Related Workmentioning

confidence: 99%

Reduced Complexity Optimal Resource Allocation for Enhanced Video Quality in a Heterogeneous Network Environment

Agarwal

Ruffini

Muntean

2022

IEEE Trans. Wireless Commun.

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The latest Heterogeneous Network (HetNet) environments, supported by 5th generation (5G) network solutions, include small cells deployed to increase the traditional macrocell network performance. In HetNet environments, before data transmission starts, there is a user association (UA) process with a specific base station (BS). Additionally, during data transmission, diverse resource allocation (RA) schemes are employed. UA-RA solutions play a critical role in improving network load balancing, spectral performance, and energy efficiency. Although several studies have examined the joint UA-RA problem, there is no optimal strategy to address it with low complexity while also reducing the time overhead. We propose two different versions of simulated annealing (SA): Reduced Search Space SA (RS 3 A) and Performance-Improved Reduced Search Space SA (P IRS 3 A), algorithms for solving UA-RA problem in HetNets. First, the UA-RA problem is formulated as a multiple knapsack problem (MKP) with constraints on the maximum BS capacity and transport block size (TBS) index. Second, the proposed RS 3 A and P IRS 3 A are used to solve the formulated MKP. Simulation results show that the proposed scheme P IRS 3 A outperforms RS 3 A and other existing schemes such as Default Simulated Annealing (DSA), and Default Genetic Algorithm (DGA) in terms of variability and DSA and RS 3 A in terms of Quality of Service (QoS) metrics, including throughput, packet loss ratio (PLR), delay and jitter. Simulation results show that P IRS 3 A generates solutions that are very close to the optimal solution.

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“…Femtocells do not face any challenges related to site availability, as users install FBS themselves and use existing user broadband connections. They also introduce very little overhead on mobile operators [5]. A femtocell provides three types of access modes to its users.…”

Section: Introductionmentioning

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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Mitigating the Impact of Cross-Tier Interference on Quality in Heterogeneous Cellular Networks

Cited by 11 publications

References 12 publications

A Comprehensive Survey on Radio Resource Management in 5G HetNets: Current Solutions, Future Trends and Open Issues

A Comprehensive Survey on Radio Resource Management in 5G HetNets: Current Solutions, Future Trends and Open Issues

Reduced Complexity Optimal Resource Allocation for Enhanced Video Quality in a Heterogeneous Network Environment

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

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