Resource management algorithm for network slicing based on deep reinforcement learning
With the development of the 5th Generation Mobile Communication Technology(5G),various application scenarios continue to emerge.Network slicing can construct multiple logically independent virtual networks on a common physical network to meet the diverse service requirements of mobile communication networks.In order to enhance the ability of mobile communication networks to allocate resources on demand according to the traffic of each slice,this paper proposes a network slicing resource management algorithm based on deep reinforcement learning.The algorithm uses two Long Short-Term Memory(LSTM)networks to predict statistical data that cannot be reached in real time,and extracts dynamic characteristics of business data volume caused by user mobility,and then makes bandwidth allocation decisions that match the needs of slice services in combination with the Advantage Actor-Critic(A2C)algorithm.Experimental results show that compared with existing methods,this algorithm can improve the spectral efficiency by about 7.7%while ensuring the user's delay and rate requirements.
the 5th Generation Mobile Communication Technology(5G)network slicingdeep reinforcement learningresource allocation
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