Delay-Aware Energy-Saving Strategies for BBU Pool in C-RAN: Modeling and Optimization

Abstract

The surging mobile traffic poses serious challenges for mobile operators and the advancement of information and communication technologies (ICT). The cost and energy consumption cannot support affordable mobile services and sustainable growth. As a new architecture, cloud-based radio access network (C-RAN) is proposed to confront these challenges. C-RAN is a deployment paradigm that seeks to isolate baseband unit (BBU) from its remote radio unit (RRU) in base station (BS), consolidating the BBUs into a common place (i.e. BBU pool). In the BBU pool, the computing resources provided by the BBUs can be dynamically assigned to RRUs on demand by the BBU controller. Thus, with the fluctuation of data traffic from RRUs, a part of BBUs can be dynamically turned on or off. As a result, the energy consumption of the BBU pool can be reduced correspondingly. In this paper, we design a Markov chainbased calculation model to formulate the energy-saving operation of the BBU pool with an active/sleep mode under dynamic RRU traffic load. On this basis, we propose two different BBU state transition strategies for the BBU pool, i.e., "Cautious ON, Bold OFF (CO-BF)" strategy and "Bold ON, Cautious OFF (BO-CF)" strategy. We formulate the key performance indicators in terms of the energy saving efficiency (ESE), the consequent packet queuing delay (PQD) and the normalized tradeoff cost as well. In extensive simulations, we investigate the effect of these key parameter indicators on the system performances of the BBU pool. Achieving the minimum normalized cost, an optimal operating strategy for the BBU pool can be determined by adjusting the parameter values of the (N, M). The simulation results show when RRU traffic load is light (e.g., λ = 0.1), the proposed BO-CF strategy can lead to the minimum normalized cost, which corresponds to ESE of up to 75.4% and PQD of 0.0097ms. For heavier load (e.g., λ = 0.4), the minimum normalized cost can be obtained along with ESE of up to 74.1% and PQD of 0.1241ms, when the CO-BF strategy is adopted. Hence, we observe that the BO-CF strategy is slightly better than the CO-BF for the light traffic load of the BBU pool, while the CO-BF strategy would be more suitable for the BBU pool with the heavier traffic load. It proves the efficacy of our proposed Markov chain-based calculation model to pursuit an optimal operating strategy for the delay-aware and energyefficient BBU pool.INDEX TERMS Cloud-based radio access network (C-RAN), Energy-saving, Sleep mode, QoS, Markov chain.

Toward Greener 5G and Beyond Radio Access Networks—A Survey

“…Thus, this leads to the number of processing resources being minimised because resources can be assigned where they are needed, and de-activated when not needed. The work in [84] proposes two state transition strategies and shows energy saving efficiencies up to 74%. However, the energy saving efficiency is derived by dividing the maximum power of the BBU pool with the activation of all BBUs in the pool [84].…”

Energy-Efficient Hybrid Powered Cloud Radio Access Network (C-RAN) for 5G

“…Mobile operators are seeking new ideas to address the growing need for high data rates, large capacity, high security, and the fastest feasible connectivity. Novel designs, innovative digital signal processing systems and protocols are always welcome [5]. Energy consumption growth of the fifth-generation (5G) mobile network infrastructure can be significant due to the increased traffic demand for a massive number of end-users with increasing traffic volume, user density, and data rate [6].…”

Multiservice Loss Models in C-RAN Supporting Compound Poisson Traffic

“…More than 60% of this traffic is expected to be carried by 5G networks, which are also anticipated to cover around 75% of the world's population in 2027. In such a demanding environment where wireless traffic increases and mobile users (MUs) require quite high data rates, extremely low latency and "anytime anywhere" connectivity, a wireless architecture such as the cloud radio access network (C-RAN) is regarded as being a quite promising 5G RAN solution [2,3].…”

“…In the recent past, various 5G C-RAN aspects have been studied, including: (a) functional splits, dimensioning and capacity issues of the fronthaul network [8][9][10][11][12], (b) privacy and security challenges as well as detailed frameworks for potential security attacks and threats [13][14][15][16], (c) energy and cost saving issues together with energy saving operating strategies [3,[17][18][19][20] and (d) cooperation problems between different mobile network operators [21,22]. On the other hand, there exists a limited number of papers that investigate the quite interesting subject of MUs' call admission control in a 5G C-RAN and focus on proposing analytical algorithms (based on efficient and usually recursive formulas) for the computation of call blocking probabilities (CBP) [23][24][25][26][27][28].…”