# Carbon-aware decentralized dynamic task offloading in MIMO-MEC networks via multi-agent reinforcement learning

arXiv: https://arxiv.org/abs/2602.18797 · HTML: https://mlsanity.com/paper/2602.18797/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 21 Feb 2026.

## Builds on (most cited first, 6 of 6)

- [An Energy and Carbon Footprint Analysis of Distributed and Federated Learning](https://mlsanity.com/paper/2206.10380/) (2022)
- [Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1812.07394/) (2018)
- [Towards Carbon-Neutral Edge Computing: Greening Edge AI by Harnessing Spot and Future Carbon Markets](https://mlsanity.com/paper/2304.11374/) (2023)
- [Less Carbon Footprint in Edge Computing by Joint Task Offloading and Energy Sharing](https://mlsanity.com/paper/2211.05529/) (2022)
- [Task Graph offloading via Deep Reinforcement Learning in Mobile Edge Computing](https://mlsanity.com/paper/2309.10569/) (2023)
- [A Joint Time and Energy-Efficient Federated Learning-based Computation Offloading Method for Mobile Edge Computing](https://mlsanity.com/paper/2409.02548/) (2024)

## Abstract (arXiv)

Massive internet of things microservices require integrating renewable energy harvesting into mobile edge computing (MEC) for sustainable eScience infrastructures. Spatiotemporal mismatches between stochastic task arrivals and intermittent green energy along with complex inter-user interference in multi-antenna (MIMO) uplinks complicate real-time resource management. Traditional centralized optimization and off-policy reinforcement learning struggle with scalability and signaling overhead in dense networks. This paper proposes CADDTO-PPO, a carbon-aware decentralized dynamic task offloading framework based on multi-agent proximal policy optimization. The multi-user MIMO-MEC system is modeled as a Decentralized Partially Observable Markov Decision Process (DEC-POMDP) to jointly minimize carbon emissions and buffer latency and energy wastage. A scalable architecture utilizes decentralized execution with parameter sharing (DEPS), which enables autonomous IoT agents to make fine-grained power control and offloading decisions based solely on local observations. Additionally, a carbon-first reward structure adaptively prioritizes green time slots for data transmission to decouple system throughput from grid-dependent carbon footprints. Finally, experimental results demonstrate CADDTO-PPO outperforms deep deterministic policy gradient (DDPG) and lyapunov-based baselines. The framework achieves the lowest carbon intensity and maintains near-zero packet overflow rates under extreme traffic loads. Architectural profiling validates the framework to demonstrate a constant O(1) inference complexity and theoretical lightweight feasibility for future generation sustainable IoT deployments.