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5G beyond is an end-edge-cloud orchestrated network that can exploitheterogeneous capabilities of the end devices, edge servers, and the cloud andthus has the potential to enable computation-intensive and delay-sensitiveapplications via computation offloading. However, in multi user wirelessnetworks, diverse application requirements and the possibility of various radioaccess modes for communication among devices make it challenging to design anoptimal computation offloading scheme. In addition, having access to completenetwork information that includes variables such as wireless channel state, andavailable bandwidth and computation resources, is a major issue. DeepReinforcement Learning (DRL) is an emerging technique to address such an issuewith limited and less accurate network information. In this paper, we utilizeDRL to design an optimal computation offloading and resource allocationstrategy for minimizing system energy consumption. We first present amulti-user end-edge-cloud orchestrated network where all devices and basestations have computation capabilities. Then, we formulate the jointcomputation offloading and resource allocation problem as a Markov DecisionProcess (MDP) and propose a new DRL algorithm to minimize system energyconsumption. Numerical results based on a real-world dataset demonstrate thatthe proposed DRL-based algorithm significantly outperforms the benchmarkpolicies in terms of system energy consumption. Extensive simulations show thatlearning rate, discount factor, and number of devices have considerableinfluence on the performance of the proposed algorithm.

Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond