ABSTRACT
The recent worldwide sanitary pandemic has made it clear that changes in user traffic patterns can create load balancing issues in networks (e.g., new peak hours of usage have been observed, especially in suburban residential areas). Such patterns need to be accommodated, often with reliable service quality. Although several studies have examined the user association and resource allocation (UA-RA) issue, there is still no optimal strategy to address such a problem with low complexity while reducing the time overhead. To this end, we propose Performance-Improved Reduced Search Space Simulated Annealing (PIRS(3)A), an algorithm for solving UA-RA problems in Heterogeneous Networks (HetNets). First, the UA-RA problem is formulated as a multiple 0/1 knapsack problem (MKP) with constraints on the maximum capacity of the base stations (BS) along with the transport block size (TBS) index. Second, the proposed PIRS(3)A is used to solve the formulated MKP. Simulation results show that PIRS(3)A outperforms existing schemes in terms of variability and Quality of Service (QoS), including throughput, packet loss ratio (PLR), delay, and jitter. Simulation results also show that PIRS3 A generates solutions that are very close to the optimal solution compared to the default simulated annealing (DSA) algorithm.
ABSTRACT
The exponential growth of rich media services across the globe has led to a massive increase in data traffic. The recent COVID-19 pandemic has also contributed to this surge as user traffic patterns have witnessed a sharp growth in demand for rich media services, particularly video conferencing (e.g., Zoom, Skype, Teams) and entertainment (e.g., Netflix, Hulu, Amazon). This has put a significant pressure on the current Heterogeneous Network (HetNet) environments, impacting end users' Quality of Experience (QoE). One of the promising solutions to deal with this issue is the introduction of 5th Generation (5G) networks within HetNets and the deployment of small cells (i.e., femtocells) to shift the load from the traditional macrocells. Yet, the big challenge with this approach is the co-tier interference that can occur between different femtocell users. To mitigate this problem, we propose a Machine Learning Interference Classification and Offloading Scheme (MLICOS) that classifies users' traffic based on the level of experienced co-tier interference and offloads the most affected traffic to nearby femtocells, with the ultimate goal of improving the users' QoE. MLICOS performance was evaluated using various QoE metrics, including Peak signal-to-noise ratio (PSNR), Structural Similarity Index Measure (SSIM), and Video Multi method Assessment Fusion (VMAF), and was compared to Proportional Fair (PF) scheduling algorithm, Variable Radius and Proportional Fair scheduling (VR+PF) algorithm, and a Cognitive Approach (CA). Simulation results show that MLICOS generates the highest PSNR, SSIM, and VMAF compared to the other schemes, therefore providing high user QoE. © 2022 IEEE.
ABSTRACT
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 (COVID-19). 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. Author