Journal
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
Volume 17, Issue 1, Pages 473-487Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSM.2019.2948457
Keywords
Energy consumption; Handover; Computer architecture; Quality of experience; Microprocessors; Copper; Central unit (CU); distributed unit (DU); flexible functional splits; handovers; OpenAirInterface (OAI); CU pool
Categories
Funding
- project CCRAN: Energy Efficiency in Converged Cloud Radio Next Generation Access Network, Intel India
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The recent adoption of virtualized technologies in Next Generation Radio Access Network (NG-RAN) has driven a significant impact on energy consumption by subsequently decreasing the number of active base stations. The base station (gNodeB) of 5G is segregated into cost-efficient Central Units (CU) hosted on virtual platforms and cheaper & smaller Distributed Units (DU) present at the cell sites. Multiple CUs are pooled together in a single powerful central cloud, known as CU pool. The logical connection between DU and CU can be dynamically adjusted and can potentially affect the energy consumption of the CU pool. The deployment of NG-RAN imposes strict latency requirements on the fronthaul link that connects DUs to CU. To relax these strict latency requirements, various alternate architectures such as Flexible RAN Functional Splits have been proposed by 3GPP. In this paper, we first evaluate the energy consumption of DU and CU for various functional split options using OpenAirInterface (OAI), a real-time open source software radio solution. We find that lower layer splits have high energy consumption at CU as compared to higher layer split options. We also observe the variation in energy consumption due to traffic heterogeneity. Motivated by the above study, we formulate an optimization model, Apt-RAN, that optimizes the energy consumption of the CU pool and the number of handovers, considering different functional splits. To address the computational complexity of solving the optimization model, a lightweight polynomial time heuristic algorithm is proposed. Simulation results demonstrate that our proposed model outperforms existing state-of-art schemes.
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