TY - JOUR
T1 - Sparsity-Aware Intelligent Massive Random Access Control for Massive MIMO Networks
T2 - A Reinforcement Learning Based Approach
AU - Tang, Xiao
AU - Liu, Sicong
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2002-2012 IEEE.
PY - 2024
Y1 - 2024
N2 - Massive random access of devices brings great challenge to the management of radio access networks. Most of the time, the access requests in the network are sporadic. Exploiting the bursting nature, sparse active user detection (SAUD) is an efficient enabler towards efficient active user detection. However, the sparsity might be deteriorated in case of high concurrent request periods. To dynamically coordinate the access requests, a reinforcement-learning (RL)-assisted scheme of closed-loop access control utilizing the access class barring (ACB) technique is proposed, where the control policy is determined through continuous interaction between the RL agent and the environment. The proposed RL agent can be deployed at the next generation node base (gNB), supporting rapid switching between heterogeneous vertical applications, such as mMTC and uRLLC services. Moreover, a data-driven scheme of deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces, where a replay buffer is applied for automatic large-scale data collection. An Actor-Critic framework is formulated to incorporate the strategy-learning modules into the intelligent control agent. Simulation results show that the proposed schemes can achieve superior performance in both access efficiency and user detection accuracy over the benchmark scheme for different heterogeneous services with massive access requests.
AB - Massive random access of devices brings great challenge to the management of radio access networks. Most of the time, the access requests in the network are sporadic. Exploiting the bursting nature, sparse active user detection (SAUD) is an efficient enabler towards efficient active user detection. However, the sparsity might be deteriorated in case of high concurrent request periods. To dynamically coordinate the access requests, a reinforcement-learning (RL)-assisted scheme of closed-loop access control utilizing the access class barring (ACB) technique is proposed, where the control policy is determined through continuous interaction between the RL agent and the environment. The proposed RL agent can be deployed at the next generation node base (gNB), supporting rapid switching between heterogeneous vertical applications, such as mMTC and uRLLC services. Moreover, a data-driven scheme of deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces, where a replay buffer is applied for automatic large-scale data collection. An Actor-Critic framework is formulated to incorporate the strategy-learning modules into the intelligent control agent. Simulation results show that the proposed schemes can achieve superior performance in both access efficiency and user detection accuracy over the benchmark scheme for different heterogeneous services with massive access requests.
KW - active user detection
KW - compressed sensing
KW - massive MIMO
KW - Massive random access
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85186070634&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85186070634&partnerID=8YFLogxK
U2 - 10.1109/TWC.2024.3365153
DO - 10.1109/TWC.2024.3365153
M3 - Article
AN - SCOPUS:85186070634
SN - 1536-1276
VL - 23
SP - 9730
EP - 9744
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 8
ER -