QoE-Aware Analysis and Management of Multimedia Services in 5G and Beyond Heterogeneous Networks

Sultan, Mohamad T.; El-Sayed, Hesham

doi:10.1109/access.2023.3298556

Cited by 14 publications

(8 citation statements)

References 24 publications

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“…AlNagar et al [16] proposes a new active caching scheme that uses prior information of vehicle requirements and mobility patterns, considers RSU cooperation modes and non-cooperation modes, and minimizes communication delays in a vehicular ad-hoc network (VANET). In [17], the authors propose a framework in 5G and networks that are beyond heterogeneous to enhance support for mobile users' QoE. The framework utilizes user mobility prediction, user preference inference and fine-grained resource reservation to improve overall user QoE through personalized data caching and dissemination in HetNet.…”

Section: Seamless Mobility Support In Icnmentioning

confidence: 99%

“…In larger, denser network environments, multicast/broadcast interests can incur significant network overhead. Broadcast/multicastbased methods [11][12][13][14][15][16][17] use an active caching method to minimize Interest retransmission during handover. These methods assume that the data already exist and proactively push future requests to the network cache before handover.…”

Section: Introductionmentioning

confidence: 99%

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A Method for 5G–ICN Seamless Mobility Support Based on Router Buffered Data

Xing,

Deng,

Han

2024

Future Internet

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The 5G core network adopts a Control and User Plane Separation (CUPS) architecture to meet the challenges of low-latency business requirements. In this architecture, a balance between management costs and User Experience (UE) is achieved by moving User Plane Function (UPF) to the edge of the network. However, cross-UPF handover during communication between the UE and the remote server will cause TCP/IP session interruption and affect continuity of delay-sensitive real-time communication continuity. Information-Centric Networks (ICNs) separate identity and location, and their ability to route based on identity can effectively handle mobility. Therefore, based on the 5G-ICN architecture, we propose a seamless mobility support method based on router buffered data (BDMM), making full use of the ICN’s identity-based routing capabilities to solve the problem of UE cross-UPF handover affecting business continuity. BDMM also uses the ICN router data buffering capabilities to reduce packet loss during handovers. We design a dynamic buffer resource allocation strategy (DBRAS) that can adjust the buffer resource allocation results in time according to network traffic changes and business types to solve the problem of unreasonable buffer resource allocation. Finally, experimental results show that our method outperforms other methods in terms of average packet delay, weighted average packet loss rate, and network overhead. In addition, our method also has good performance in average handover delay.

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Section: Seamless Mobility Support In Icnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method for 5G–ICN Seamless Mobility Support Based on Router Buffered Data

Xing,

Deng,

Han

2024

Future Internet

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“…With the proliferation of intelligent devices and the growth of high-bandwidth applications, heterogeneous networks have become an effective solution for addressing the increased surge in data traffic [8,9]. Heterogeneous networks consist of different types of base stations, including micro base stations, macro base stations, and small-cell base stations, each differing in cache capacity, coverage area, and service modes [10].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching

Sun,

Chen

2024

Electronics

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With the popularity of smart devices and the growth of high-bandwidth applications, the wireless industry is facing an increased surge in data traffic. This challenge highlights the limitations of traditional edge-caching solutions, especially in terms of content-caching effectiveness and network-communication latency. To address this problem, we investigated efficient caching strategies in heterogeneous network environments. The caching decision process becomes more complex due to the heterogeneity of the network environment, as well as due to the diversity of user behaviors and content requests. To address the problem of increased system latency due to the dynamically changing nature of content popularity and limited cache capacity, we propose a novel content placement strategy, the long-short-term-memory–content-population-prediction model, to capture the correlation of request patterns between different contents and the periodicity in the time domain, in order to improve the accuracy of the prediction of content popularity. Then, to address the heterogeneity of heterogeneous network environments, we propose an efficient content delivery strategy: the multi-intelligent critical collaborative caching policy. This strategy models the edge-caching problem in heterogeneous scenarios as a Markov decision process using multi-base-station-environment information. In order to fully utilize the multi-intelligence information, we have improved the actor–critic approach by integrating the attention mechanism into a neural network. Whereas the actor network is responsible for making decisions based on local information, the critic network evaluates and enhances the actor’s performance. We conducted extensive simulations, and the results showed that the Long Short Term Memory content population prediction model was more advantageous, in terms of content-popularity-prediction accuracy, with a 28.61% improvement in prediction error, compared to several other existing methods. The proposed multi-intelligence actor–critic collaborative caching policy algorithm improved the cache-hit-rate metric by up to 32.3% and reduced the system latency by 1.6%, demonstrating the feasibility and effectiveness of the algorithm.

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“…Besides that, 6G networks are expected to support a wide range of users and applications with different requirements for QoE. Thus, personalizing QoE can ensure that users are able to optimize their experience and achieve the best possible experience in a timely and effective manner [ 74 ]. Therefore, prioritizing the personalization of QoE is a critical component of achieving success in the upcoming 6G network.…”

Section: 6g Visions and Requirementsmentioning

confidence: 99%

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Puspitasari,

An,

Alsharif

et al. 2023

Sensors

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The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

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QoE-Aware Analysis and Management of Multimedia Services in 5G and Beyond Heterogeneous Networks

Cited by 14 publications

References 24 publications

A Method for 5G–ICN Seamless Mobility Support Based on Router Buffered Data

A Method for 5G–ICN Seamless Mobility Support Based on Router Buffered Data

Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

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