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Massive random access of devices in the emerging Open Radio Access Network(O-RAN) brings great challenge to the access control and management. Exploitingthe bursting nature of the access requests, sparse active user detection (SAUD)is an efficient enabler towards efficient access management, but the sparsitymight be deteriorated in case of uncoordinated massive access requests. Todynamically preserve the sparsity of access requests, a reinforcement-learning(RL)-assisted scheme of closed-loop access control utilizing the access classbarring technique is proposed, where the RL policy is determined throughcontinuous interaction between the RL agent, i.e., a next generation node base(gNB), and the environment. The proposed scheme can be implemented by thenear-real-time RAN intelligent controller (near-RT RIC) in O-RAN, supportingrapid switching between heterogeneous vertical applications, such as mMTC anduRLLC services. Moreover, a data-driven scheme of deep-RL-assisted SAUD isproposed to resolve highly complex environments with continuous andhigh-dimensional state and action spaces, where a replay buffer is applied forautomatic large-scale data collection. An actor-critic framework is formulatedto incorporate the strategy-learning modules into the near-RT RIC. Simulationresults show that the proposed schemes can achieve superior performance in bothaccess efficiency and user detection accuracy over the benchmark scheme fordifferent heterogeneous services with massive access requests.

Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach