IRS多层次算力网络能效最优计算卸载策略

Task Offloading Optimization in IRS-Assisted Multi-Tier Computing Networks

关键词:  智能反射面  多层次算力网络  能量效率  计算卸载  最优化

Key words:  intelligent reflecting surface  multi-tier computing network  energy efficiency  computation offloading  optimization

摘要:

智能反射面(IRS)技术在多层次算力网络中的应用能够有效提升频谱利用率，增强网络性能。然而，传统的频谱效率优化策略可能导致系统性能与网络能耗的失衡。针对这一问题，提出联合频谱效率与能耗优化，以网络计算卸载能效优化为目标，在IRS辅助的三层次算力网络中建立系统能效最优化模型；继而，提出能效最优的多层次计算卸载问题，对IRS与二层接入点的波束成形矩阵进行联合优化；针对原问题的参数耦合与非凸特性，利用时序规划将原问题分解为第一阶段的能量效率最大化问题和第二阶段的时延最小化问题，分别基于丁克尔巴赫和交替优化方法求解，并设计了优化算法。最后，通过仿真实验验证了所提方法在系统能效提升方面的有效性及所实现的系统性能增益。

Abstract:

The application of Intelligent Reflecting Surface (IRS) technology in multi-layer computing networks can effectively improve spectral efficiency and enhance network performance. However, traditional spectrum efficiency optimization strategies may lead to an imbalance between system performance and network energy consumption. To address this issue, a joint spectrum efficiency and energy consumption optimization approach is proposed, with the goal of optimizing energy-efficient computing offloading in an IRS-assisted three-layer computing network. A system energy efficiency optimization model is established. Subsequently, an energy-efficient multi-layer computing offloading problem is proposed, where the beamforming matrices of the IRS and the second-tier access points are jointly optimized. Due to the parameter coupling and non-convexity of the original problem, a two-stage approach is used, where the problem is decomposed into an energy efficiency maximization problem in the first stage and a delay minimization problem in the second stage. These problems are solved using the Dinkelbach method and alternating optimization, respectively, and an optimization algorithm is designed. Finally, simulation experiments validate the effectiveness of the proposed method in improving system energy efficiency and the performance gains achieved.