Key result
Hybrid slicing via deep reinforcement learning achieves near-optimal SLA satisfaction, isolation, and spectrum efficiency.
Why the study?
Existing deep reinforcement learning approaches for resource allocation in network slicing do not consider the performance loss associated with the initial exploration phase.
A novel hybrid slicing framework using deep reinforcement learning guarantees service level agreements during the training phase and achieves near-optimal performance in 5G networks.
This is the authors' abstract. We don't add key points for this paper.
Network slicing is a critical driver for guaranteeing the diverse service level agreements (SLA) in 5G and future networks. Recently, deep reinforcement learning (DRL) has been widely utmzed for resource allocation in network slicing. However, existing related works do not consider the performance loss associated with the initial exploration phase of DRL. This paper proposes a new performance-guaranteed slicing strategy with a soft and hard hybrid slicing setting. Mainly, a common slice setting is applied to guarantee slices’ SLA when training the neural network. Moreover, the resource of the common slice tends to precisely redistribute to slices with the training of DRL until it converges. Furthermore, experiment results confirm the effectiveness of our proposed slicing framework: the slices’ SLA of the training phase can be guaranteed, and the proposed algorithm can achieve the near-optimal performance in terms of the SLA satisfaction ratio, isolation degree and spectrum efficiency after convergence.
Answers are grounded in this paper and cite their sources.
Supports SLA-guaranteed 5G slicing; leaves open real-world validation before adoption.
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Zhang et al. (2022) studied Network slicing in 5G networks. Soft and hard hybrid slicing framework via Deep Reinforcement Learning vs. Existing DRL resource allocation methods was evaluated on SLA satisfaction ratio, isolation degree, and spectrum efficiency. A soft and hard hybrid slicing framework via deep reinforcement learning guaranteed SLA during training and achieved near-optimal SLA satisfaction, isolation degree, and spectrum efficiency.
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