Jul 2026· The 2026 International Conference on Optical Communication and Intelligent Algorithms (OCIA 2026)· Vol 14301, pp. 143011T - 143011T-12· 0 citations· 10 references
Engineering
TL;DR
A dual layer optimization for heterogeneous network user access and energy consumption perception is constructed, while the lower layer uses continuous optimization to handle resource and power allocation, and integrates NSGA-III multi-objective algorithm to achieve Pareto optimization of QoS and energy consumption.
Abstract
This article constructs a dual layer optimization for heterogeneous network user access and energy consumption perception. The upper layer uses mixed integer programming to solve user access decisions, while the lower layer uses continuous optimization to handle resource and power allocation, and integrates NSGA-III multi-objective algorithm to achieve Pareto optimization of QoS and energy consumption. The core innovation of this model includes a heterogeneous resource pool management mechanism, where macro-base stations are configured with 100 resource blocks with a power range of 10-40dBm, and three micro-base stations are each configured with 50 resource blocks and a power range of 10-30dBm, as well as decision-making strategies for energy consumption perception. It integrates a comprehensive energy consumption model of static power consumption, dynamic power consumption, and cooling power consumption. Design robust stochastic differential evolution algorithms at the algorithmic level to handle channel uncertainty and ensure multi-objective convergence mechanisms. The optimization results show that among the optimal access strategies of 70 users, 45% of users choose macro-base station services, 55% of users choose nearby micro-base station services, and the total QoS of the system is 6534.28.
A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system potential and lower carbon emissions, it is crucial to make effective decisions about job offloading and resource allocation. A carbon-aware MEC architecture that uses both grid and renewable energy sources is proposed in this paper. Our goal is to jointly manage resource allocation and task offloading while monitoring carbon emissions and task queue delays to optimize system behavior under uncertainty, specifically for stochastic workloads and variable renewable generation. To balance these two cost components (emissions and queue length), we create a combined optimization problem. We develop a deep deterministic policy gradient (DDPG)-based joint optimization technique to address this issue in a constantly changing environment. In the optimization, we consider greedy policy (GP) and full offloading (FO), as well as time-average carbon emission (TACE) and time-average queue length (TAQL) as performance metrics, and time-average queue length (TAQL) and full execution (FE) as baseline strategies; we also evaluate normalized time-average cumulative reward (NTACR). This method uses continuous-action reinforcement learning to generate efficient, real-time control policies. For the proposed MEC network, numerical statistics show that our approach can lead to effective offloading and lower carbon emissions.
M. Saeed, Rashid A Saeed, M. A. Ahmed et al.· 2026 6th International Confe...· 0 citations
This formulation provides a rigorous and tractable framework for distributed spectrum sharing in 6G O-RAN systems, with the potential to support intelligent and adaptive control in future wireless networks.
E. Spyrou, Chrysostomos D. Stylios, V. Kappatos et al.· Future Internet· 0 citations
This work demonstrates the viability of RL for distributed resource management and provides a reproducible simulation toolkit to support further research in AI-driven wireless communication systems.
Mugerwa Joseph, Ajaegbu Chigozirim· International Journal Of Eng...· 0 citations
Comparison shows that, under the evaluated DeepMIMO-based scenarios, the examined assignment-based methods exhibit different trade-offs in throughput, energy-consumption-related performance, bandwidth utilization, and adaptability to varying user demands, offering useful insights for 5G MIMO resource allocation studies.
Nikolaos Prodromos, Damianos Diasakos, V. Kokkinos et al.· Wireless personal communicat...· 0 citations
As wireless networks transition toward the 6G era, supporting strictly heterogeneous services such as eMBB, URLLC, and mMTC over a unified infrastructure becomes a fundamental challenge. Traditional Radio Access Network (RAN) slicing often relies on upper-layer logical abstractions, which fail to address physical inter-slice interference and the boundary effect of cellular architectures. This paper proposes a novel Profile-Aware Hierarchical RAN Slicing Framework for User-Centric Cell-Free Massive MIMO systems to overcome these limitations. The proposed framework comprises four hierarchical stages: utility-based profile-aware clustering, dynamic inter-slice resource partitioning for power and bandwidth, hybrid central processing unit-to-access point power budgeting, and real-time power allocation using a Bipartite Graph Convolutional Network (BiGCN). By incorporating service-specific requirements into physical layer resource management, the framework ensures strict quality-of-service isolation and global energy efficiency. Simulation results demonstrate that the proposed integrated framework achieves a high Jain’s Fairness Index, exceeding 0.95 for most profiles and provides up to a 48-fold energy efficiency improvement for battery-constrained devices compared to non-slicing baselines. Furthermore, the BiGCN-based allocation module attains near-optimal performance with millisecond-level inference latency, confirming its feasibility for mission-critical real-time applications. This comprehensive approach effectively eliminates the trade-off between aggregate throughput and individual reliability, providing a scalable solution for next-generation sliced networks.
Ja-Eun Kim, Hye-Yoon Jeong, Ji-Woo Lee et al.· IEEE Access· 0 citations
The 5G wireless networks are fast with low latency and support a very large number of connected devices, making it possible to support advanced applications and services. Such environment demands efficient management of network resources that can be achieved by evaluation of traffic load, distribution of devices, and state of signals to ensure quality-of-service (QoS) and network performance. Conventional resource allocation and mobility management techniques tend to have challenges of flexibility, computational effectiveness, as well as optimal choice of devices in disparate network conditions. In order to address these issues, this paper introduces a multi-stage resource management system that is adaptive in 5G networks to address QoS and mobility efficiency issues. The framework combines the set of network parameters with Adaptive Multivariate Kernel Resource Estimation (AMKRE) in case of assessing and normalizing available resources. Resource-Aware Device Selection (ORADS) is used to select the appropriate devices and then it is followed by Robust Filter-Based Rank Evaluation (RFBRE) where devices are ranked on the basis of reliability and link quality. The algorithm that optimizes the resources distribution taking into consideration the space constraints is known as DistanceAware Adaptive Particle Swarm Optimization (DAAPSO), and the algorithm that provides the continuity of mobility is known as Hybrid Predictive Soft Handover Control (HPSHC). The multi-dimensional paradigm improves the resource utilization, quality-of-service (QoS) provisioning, and mobility management in 5G networks.
G. Ramasamy, C. Chandrasekar· International journal of com...· 0 citations
