Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond
This work addresses energy efficiency in multi-user wireless networks for enabling computation-intensive applications, but it is incremental as it applies an existing DRL method to a specific domain problem.
The paper tackles the challenge of designing an optimal computation offloading and resource allocation strategy in 5G beyond networks to minimize system energy consumption, using a Deep Reinforcement Learning (DRL) approach, and demonstrates that the proposed algorithm significantly outperforms benchmark policies in energy efficiency.
5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous capabilities of the end devices, edge servers, and the cloud and thus has the potential to enable computation-intensive and delay-sensitive applications via computation offloading. However, in multi user wireless networks, diverse application requirements and the possibility of various radio access modes for communication among devices make it challenging to design an optimal computation offloading scheme. In addition, having access to complete network information that includes variables such as wireless channel state, and available bandwidth and computation resources, is a major issue. Deep Reinforcement Learning (DRL) is an emerging technique to address such an issue with limited and less accurate network information. In this paper, we utilize DRL to design an optimal computation offloading and resource allocation strategy for minimizing system energy consumption. We first present a multi-user end-edge-cloud orchestrated network where all devices and base stations have computation capabilities. Then, we formulate the joint computation offloading and resource allocation problem as a Markov Decision Process (MDP) and propose a new DRL algorithm to minimize system energy consumption. Numerical results based on a real-world dataset demonstrate that the proposed DRL-based algorithm significantly outperforms the benchmark policies in terms of system energy consumption. Extensive simulations show that learning rate, discount factor, and number of devices have considerable influence on the performance of the proposed algorithm.