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A DRL-based resource allocation framework for multimedia multicast in 5G cellular networks | IEEE Conference Publication | IEEE Xplore

A DRL-based resource allocation framework for multimedia multicast in 5G cellular networks


Abstract:

Cloud Radio Access Network (C-RAN) is a key enabling network architecture for the next generation (5G) wireless communications and has advantages of providing multimedia ...Show More

Abstract:

Cloud Radio Access Network (C-RAN) is a key enabling network architecture for the next generation (5G) wireless communications and has advantages of providing multimedia service on the basis of properly resource allocation. However, the resource allocation strategy still needs to be further improved in order to allocate resource where unicast or multicast multimedia services are requested simultaneously. Aiming at minimizing power consumption and meeting quality of service (QoS) requirement of users, this paper proposes a deep reinforcement learning based unicast-multicast resource allocation framework (DRL-CRAF) for it with high energy efficiency. Specifically, we define the state space, action space and reward function for the DRL agent, and formally formulate the resource allocation problem in a multimedia service scenario as a convex optimization problem. We evaluate the performance of the proposed framework by comparing it with three widely-used baselines via simulation. Simulation results show that the proposed method can not only meet the requirements of both unicast and multicast users but also effectively reduce 10% transmission energy consumption compared to full connectivity method.
Date of Conference: 05-07 June 2019
Date Added to IEEE Xplore: 30 January 2020
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Conference Location: Jeju, Korea (South)

References

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