Aug 2026· PeerJ Computer Science· Vol 12, pp. e4004· 0 citations· 64 references
TL;DR
Numerical simulations demonstrate that proactive caching based on clustered-BSVM outperforms existing methods in terms of CHR and USR, while the branch-and-bound method effectively resolves the content delivery problem, minimizing system latency.
Abstract
Proactive caching in multi-tier cellular networks (MTCNs) is an efficient technique for alleviating heavy data traffic on backhaul links, thereby reducing content latency and increasing the overall cache hit ratio (CHR) and user satisfaction ratio (USR). The CHR and USR can be further enhanced by incorporating cache-enabled device-to-device (D2D) and cloud cache into MTCNs, forming cloud and D2D-assisted multi-tier cellular networks (CD2DMTCNs). In this context, we formulate two problems: the first is the joint optimization of the overall CHR and USR of the system, while the second focuses on minimizing the average content latency. The popularity prediction of newly published content using machine learning algorithms has become increasingly attractive, with support vector machines (SVMs) showing promising results in this regard. However, for improved performance, an uncertainty assessment is necessary. To address this, bootstrapping is applied to SVM to obtain confidence and prediction intervals. Additionally, K-means clustering is used to form virtual clusters within both D2D networks and MTCNs, facilitating content popularity prediction. This enables the maximization of CHR and USR through a clustered-bootstrapped support vector machine (clustered-BSVM). In addition, establishing criteria for content delivery is essential. Since multiple cache-enabled transmitters may store the content requested by users, only one transmitter can deliver the requested content at a time. This constraint is addressed using the branch-and-bound method of linear programming, which minimizes the average latency of the system. Numerical simulations demonstrate that proactive caching based on clustered-BSVM outperforms existing methods in terms of CHR and USR, while the branch-and-bound method effectively resolves the content delivery problem, minimizing system latency.
— The increase in mobile data traffic in Fifth-Generation (5G) networks means that new handover management and content delivery solutions are needed to keep the network running smoothly and the user experience high. This paper introduces an innovative integration of Named Data Networking (NDN) into the 5G architecture, incorporating an Enhanced Popularity-Based Caching mechanism at the Multi-access Edge Computing (MEC) layer of the 5G user plane. Our design is different from previous ones because it changes how content is replicated based on how mobile and dense the User Equipment (UE) and gNB are in real time. Using Python-based models, we ran a lot of simulations to compare baseline 5G, edge-caching, and full NDN configurations. The proposed solution had a Handover Success Rate (HSR) of over 90%, a Cache Hit RAtio (CHR) of between 78% and 80%, an average latency of about 20 ms, and a packet loss rate of less than 1.0% across a wide range of network scenarios. The NDN integrated architecture cuts latency by up to 35%, boosts throughput by 40%, makes fallback efficiency improved by 36.8%, and raises average HSR by 25 – 40%. All of these changes improve the Quality of Experience (QoE) in environments with a lot of movement. The research we conduct aims to facilitate seamless, scalable, and resilient content delivery for next-generation 5G edge networks.
Ade Nurhayati· Journal of Communications· 0 citations
The built-in caching capability of Named Data Networking (NDN) is one of the most transformative proposals of next-generation network architecture, as it simultaneously realizes network traffic reduction, resilience to node failures, and prompt data retrieval. However, existing caching policies either make too many caches across the network by naively copying content everywhere or incur excess communication overhead for efficient caching through cache state exchange. Hence, we propose a machine learning-based caching policy along with a communication-efficient method to share the cache state. As the intelligent caching policy is capable of learning request patterns and estimating cache states of other nodes, our proposal realizes a more efficient utilization of the cache capacity by virtually considering the cache spaces of neighboring NDN nodes as an aggregated, larger cache space. The extensive evaluation experiments demonstrate the effectiveness of our proposal in increasing the cache hit ratio and reducing the average distance that each data travels to reach a requesting user. The effectiveness of the proposed cache state sharing method is empirically verified through an interpretable machine learning technique.
Deep Pradipbhai Shah, Sai Sameer Yanamandra, Siva Girish Ramesh et al.· International Conference on...· 0 citations
The coexistence of Ultra-Reliable Low-Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB) can simultaneously meet the reliability and real-time performance requirements of critical services as well as the requirements of high-bandwidth services. In 5G and beyond 5G (B5G) networks, radio access network (RAN) slicing can ensure the differentiated quality of service (QoS) for coexisting heterogeneous services. This paper investigates a dynamic resource allocation framework, aiming to maximize resource utilization subject to QoS constraints. The optimization problem is an NP-hard integer program. To address this issue, we propose a hierarchical proximal policy optimization (PPO) model assisted by a traffic prediction algorithm, namely graph-aggregated Extended Long Short-Term Memory (GxLSTM), which decouples the resource allocation behavior between eMBB and URLLC. Transfer learning is introduced to improve the learning efficiency, forming a Transfer Learning-assisted Hierarchical PPO algorithm (TL-HPPO). In addition, considering the lightweight algorithm deployment requirements for edge networks, based on angular knowledge distillation (AKD), GxLSTM and TL-HPPO are respectively distilled to obtain AKD-traffic prediction (AKD-TP) and AKD-resource allocation (AKD-RA), thereby reducing computational complexity. Simulation results show that the proposed algorithms outperform comparative algorithms while ensuring QoS, effectively improving resource utilization.
Yixuan Bai, Heng Wang· IEEE Transactions on Communi...· 0 citations
The results demonstrated that integrating C-NOMA with caching and m-MIMO leads to better performance in sum rate, latency reduction, and throughput for different file sizes, different networks traffic, different users and stations numbers and different power allocation levels.
S. Ahmed, S. Ameen· passer of basic and applied...· 0 citations
This research is among the first to employ MARL to this extent, and it offers an end-to-end solution that combines cellular, Wi-Fi, and device-to-device (D2D) communications and considers practical network environments like user mobility and channel conditions.
Nabeel Abdolrazagh Yaseen Alrashedi, Rasool Sadeghi, Wael Hussein Zayer Al-Lamy et al.· Journal of universal compute...· 0 citations
With the deployment of 5G NR-U (5th Generation New Radio in Unlicensed Spectrum) in unlicensed spectrum and the rise of sparse large-scale models (exemplified by Switch Transformers) in edge computing, edge-cloud collaborative training faces the dual challenges of dynamic spectrum contention and heterogeneous load distribution. This paper addresses the coupled uncertainty of time-varying 5G NR-U channels and the random arrival of expert tasks in MoE (Mixture of Experts) by introducing a dynamic scheduling algorithm for edge-cloud collaboration based on MADDPG (Multi-Agent Deep Deterministic Policy Gradient). This algorithm models each edge server as an agent and, based on local observations, jointly decides on task offloading targets (terminal, edge, or cloud), expert cache replacement, and 5G NR-U contention window parameters through a continuous action space, achieving cross-layer collaborative optimization of communication and computing cache resources. A centralized critic network is used for global training, and the reward function integrates average iterative latency, communication energy consumption, system throughput, and load balancing. On a simulation platform comprising eight heterogeneous edge nodes and one cloud center, three scenarios—baseline, high-interference, and burst load—were implemented for validation. Experimental results show that the proposed MADDPG algorithm outperforms benchmark algorithms such as CO (Cloud-Only), GO (Greedy Offloading), SEP (Static Expert Partitioning), DDPG (Deep Deterministic Policy Gradient), COMA (Counterfactual Multi-Agent Policy Gradients), and MAPPO (Multi-Agent Proximal Policy Optimization) in terms of average iteration latency (minimum 78 ms), system throughput (maximum 185 K tokens/s), expert load balancing (minimum standard deviation 15.3), and communication energy consumption (minimum 52 J). It demonstrates superior adaptability, robustness, and collaborative efficiency under scenarios of severe channel fluctuations and burst load, providing a feasible intelligent scheduling solution for efficient training of sparse large models in dynamic edge environments.
De-Feng Duan, Hong Liu, Li-Yun Huang et al.· Tsinghua Science and Technol...· 0 citations
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