RRM and CRAN

Radio resource management (RRM) can be seen as a bundle of network optimization functions aimed at efficiently utilizing the limited radio frequency spectrum resources and radio network infrastructure. The main functions may contain: a) radio resource allocation among macrocell and small cells; b) packets scheduling – physical resource block (PRB) allocation for maximizing the cell spectral efficiency and cell throughput; c) link adaptation – adaptive modulation and coding (AMC), and transmission power control; d) radio admission control and handover management.

RRM plays in radio access network (RAN) and it’s worthy to notice the differences between traditional network and target network. The infrastructures of the two different mobile network are shown in the figure. The traditional mobile network uses the radio/digital unit (RU/DU) integrated RAN infrastructure. It has several disadvantages, e.g., limited scalability, inefficient coordination and sub-optimal spectrum usage. For the target mobile network, the cloud radio access network (CRAN) are adopted. It overcomes many drawbacks of traditional network and has several advantages, e.g., high flexibility and scalability, centralized coordination, spectrum usage optimization and software-defined networking (SDN) managed slicing. In the target network, almost all the network functions are virtualized and managed by a control platform. It gives many benefits for RRM functions to coordinate and jointly
optimize.

Mobile central office re-architected as a data center (M-CORD) is a SotA platform implementing the target mobile network. It is an open source solution for enabling 5G mobile networks, which is built on SDN, network function virtualization (NFV) and cloud technologies. It includes virtualized RAN and virtualized evolved packet core (vEPC) to enable mobile edge and services.

An example of network slicing based on M-CORD is demonstrated. It contains two parts: RAN slicing and core slicing. Different services share the virtualized RRM functions in the RAN slicing and share vEPC functions in the core slicing. Each service has its own virtualized network function (VNF) chain which may be allocated and run on different general purpose servers.

Some state-of-the-art RRM techniques are listed as follows: Interference Management [1]; QoS/QoE Management [2]; Context-aware Radio Resource Management [3]; AI Empowered Radio Resource Management [4]; Joint Optimization of Resources [5].

[1] J. Liu, M. Sheng, L. Liu, and J. Li, “Interference Management in Ultra-Dense Networks: Challenges and Approaches,” IEEE Network, vol. 31, no. 6, pp. 70–77, 2017.
[2] Y. Wang, P. Li, L. Jiao, Z. Su, N. Cheng, X. S. Shen, and P. Zhang, “A Data-driven Architecture for Personalized QoE Management in 5g Wireless Networks,” IEEE Wireless Communications, vol. 24, no. 1, pp. 102–110, 2017.
[3] G. Caso, L. De Nardis, and M.-G. Di Benedetto, “Toward Context-Aware Dynamic Spectrum Management for 5G,” IEEE Wireless Communications, vol. 24, no. 5, pp. 38–43, 2017.
[4] E. Ghadimi, F. D. Calabrese, G. Peters, and P. Soldati, “A reinforcement learning approach to power control and rate adaptation in cellular networks,” in Communications (ICC), 2017 IEEE International Conference on, IEEE, 2017, pp. 1–7.
[5] C. Wang, C. Liang, F. R. Yu, Q. Chen, and L. Tang, “Computation offloading and resource allocation in wireless cellular networks with mobile edge computing,” IEEE Transactions on Wireless Communications, vol. 16, no. 8, pp. 4924–4938, 2017.