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.
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