Joint Latency-Energy Optimization for Two-Tier Multiuser Multitask Offloading in AI-Agent Communication Networks

Artificial intelligence-agent communication networks (ACNs) in the sixth-generation (6G) enable collaborative task execution among agents and butler. However, compared with traditional mobile edge computing (MEC), in ACNs, a large number of agents possess comparable computing capabilities and task proportions need to be jointly determined rather than being predefined, causing high optimization complexity in large-scale deployment scenarios. In this paper, we propose an effective framework to solve the large-scale coupled optimization problem under acceptable complexity. Specifically, we model the joint task allocation, resource allocation, task offloading and computation frequency adjustment problem as an NP-hard nonconvex mixed-integer nonlinear programming (MINLP) problem, and derive its lower bound through Lagrangian relaxation. We decompose the problem into resource allocation and task offloading subproblems, which are solved via proximal policy optimization (PPO) and minimum-cost models within a block coordinate descent (BCD) framework. Simulations demonstrate the tightness of the lower bound, achieving stable convergence for 30 agents while reducing latency from 150 ms to 50 ms and the energy consumption by 28.18%. Our algorithm has great potential for ACNs with many agents deployed in future 6G scenarios, such as autonomous vehicles, robotic swarms and precision telemedicine.