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With highly heterogeneous application requirements, 6G and beyond cellular networks are expected to be demand-driven, elastic, user-centric, and capable of supporting multiple services. A redesign of the one-size-fits-all cellular architecture is needed to support heterogeneous application needs. This paper addresses this need by proposing an intelligent, demand-driven, elastic user-centric cloud radio access network (UCRAN) architecture capable of providing services to a diverse set of use cases ranging from augmented/virtual reality to high-speed rails to industrial robots to E-health applications, and more. The proposed framework leverages deep reinforcement learning to adjust the size of a user-centered virtual cell based on each application’s heterogeneous throughput and latency requirements. Finally, numerical results are presented to validate the convergence and network adaptability of the proposed …