Prediction-based Hybrid Slicing Framework for Service Level Agreement Guarantee in Mobility Scenarios: A Deep Learning Approach

TL;DR

This paper introduces a deep learning-based hybrid slicing framework for 5G networks that predicts user mobility and traffic to optimize resource allocation, ensuring SLA guarantees despite mobility challenges.

Contribution

It proposes a novel hybrid slicing framework combined with LSTM and DQN algorithms for mobility-aware radio resource allocation in 5G networks.

Findings

The framework guarantees SLA satisfaction effectively.

The algorithm achieves near-optimal SLA performance.

It balances isolation and spectrum efficiency.

Abstract

Network slicing is a critical driver for guaranteeing the diverse service level agreements (SLA) in 5G and future networks. Inter-slice radio resource allocation (IS-RRA) in the radio access network (RAN) is very important. However, user mobility brings new challenges for optimal IS-RRA. This paper first proposes a soft and hard hybrid slicing framework where a common slice is introduced to realize a trade-off between isolation and spectrum efficiency (SE). To address the challenges posed by user mobility, we propose a two-step deep learning-based algorithm: joint long short-term memory (LSTM)-based network state prediction and deep Q network (DQN)-based slicing strategy. In the proposal, LSTM networks are employed to predict traffic demand and the location of each user in a slicing window level. Moreover, channel gain is mapped by location and a radio map. Then, the predicted channel gain and traffic demand are input to the DQN to output the precise slicing adjustment. Finally, experiment results confirm the effectiveness of our proposed slicing framework: the slices' SLA can be guaranteed well, and the proposed algorithm can achieve near-optimal performance in terms of the SLA satisfaction ratio, isolation degree and SE.