TL;DR
Simulation results demonstrate that the IRFO-JORA algorithm could efficiently allocate resource according to system parameters and user QoE requirements, thereby significantly improving RF utilization efficiency.
Simulation results demonstrate that the proposed resource optimization scheme for NOMA VLC/RF networks outperforms NOMA VLC and OMA VLC and OMA VLC in ensuring highly reliable data transmission.
A mixed-integer linear programming (MILP) model is developed that jointly optimizes access points (APs) selection, wavelength assignment and fog computing resources allocation to serve the requests of users and achieves lower average total power consumption.
This paper explores the application of a fractional frequency reuse (FFR) strategy purposed for networks that integrate both D2D and RIS technologies in multicell cellular network scenarios, and demonstrates its effectiveness in improving network reliability and efficiency.
Simulation results demonstrate that the proposed GNN-RSMA interference management algorithm outperforms conventional multiple access schemes while achieving fairness and worst-user performance comparable to successive convex approximation (SCA)-based optimization at only a fraction of its computational cost.
The proliferation of applications and multimedia services has led users to demand more bandwidth while limiting access time to the target content. The current 5G specifications provide mechanisms to support these challenging requirements by leveraging both the slicing paradigm, which enables the allocation of network resources with dedicated Quality of Service (QoS), and the Multi-Access Edge Computing (MEC), bringing content as close as possible to end users. Two additional key enablers can further improve user experience: the 5G non-3GPP access and the use of Non-Terrestrial Networks (NTN). The former could extend the overall coverage by exploiting legacy technologies, largely deployed, such as Wi-Fi, which can also avoid frequency license issues in many cases. The latter represents the most efficient solution to support multicast/broadcast content distribution towards the edges of the network. These key enablers are addressed in the ESA 5G-EMERGE project, where 5G, satellite broadcasting, and a Content Delivery Network (CDN) are integrated to efficiently deliver IP-based multimedia services. In this framework, the contribution of this paper is to describe the innovative reference architecture and technologies proposed in 5G-EMERGE, focusing on the implementation and validation of a non-3GPP edge component compliant with the 5G MEC architecture, leveraging NTN as backbone, interworking with a commercial 5G core network, and providing support for QoS enforcement and multicast delivery. The experimental results demonstrate the effectiveness of the proposed solution in terms of both delay reduction and bandwidth optimization.
Fixed Wireless Access (FWA) has recently emerged as a cost-effective alternative to optical fiber in rural areas, particularly where fiber deployment is economically infeasible. To extend coverage and increase capacity, FWA networks have begun to integrate Integrated Access and Backhaul (IAB) with mid- and high-band spectrum. However, the energy consumption of multi-hop IAB networks scales significantly with the number of hops, a challenge that prior research has not adequately addressed. This paper proposes an energy-efficient framework that minimizes network energy consumption by maximizing Resource Block (RB) utilization while avoiding both over- and under-allocation in multi-hop IAB-based FWA deployments. The proposed method jointly allocates RBs and selects modulation and coding schemes across a mixed set of 5G numerologies to satisfy data rate requirements while minimizing energy consumption. The inherent dynamic interactions among IAB stations render the problem highly complex and non-convex; therefore, we design a disciplined multi-convex programming supported by dynamic programming algorithms to obtain tractable solutions. Furthermore, we introduce a transformer-based prediction to forecast RB distribution, thereby mitigating the need for frequent short-timescale coordination among IAB stations. Our simulation results demonstrate that the proposed approach achieves the required data rates while reducing energy consumption by 14%.