Analysis of energy efficiency in cloud based heterogeneous RAN with large-scale antenna systems

Abstract

To address the capacity requirements resulting from huge growth in mobile data traffic, the mobile network operators (MNOs) are densifying their networks with more base stations, and with more spectrum layers. The addition of base stations and spectrum layers increases the energy consumption of the access network. Hence the network needs to be optimized to lower the power consumption (e.g.) through cloud, or deployment of spectral and power efficient radio units like Large-Scale Antenna Systems (LSAS) or massive Multiple Input Multiple Output (MIMO) radio units.In this paper, we analyse the network evolution towards Cloud based Radio Access Network (C-RAN) for a mix of base stations with Macro and LSAS Remote Radio Units (RRU). We derive the computational complexity of the base station components using a flexible power model. We evaluate the need for base station architecture split, when C-RAN is used with LSAS radio units and large antenna configurations. We use a combination of simulators viz.NS3 (for LTE radio network simulation) and CloudSIM Plus (for Cloud based base station simulation). For each base station type, we compare the computational complexity of different sub-components. Further, based on the selected power model, we study the effect of base station's scheduling algorithm (viz.) Proportionate-Fair or Round-Robin, on the computational complexity of the various base station sub-components. We consider Time and Wavelength Division Multiplexed Passive Optical Network (TWDM-PON) for the high capacity low latency fronthaul network. Using the overall radio cluster throughput and power consumption information from Cloud base station, Radio site, and fronthaul network, we assess the energy efficiency metrics for the Cloud RAN and LSAS (with different antenna configurations) with Macro RRUs as reference.Through the simulations, we observe that, the computation complexity was relatively higher for RR scheduling (compared to PF scheduling). But the variations of energy efficiency metric were similar for both the scheduling schemes for different base station configurations.We observe that compared to Macro2TR, LSAS variants are highly energy efficient. This gain is mainly associated with higher throughput due to MU-MIMO (where multiple UEs are accommodated in the same set of radio resources) and lower power consuming components in LSAS. Between LSAS32TR, LSAS64TR and LSAS128TR, we find that LSAS32TR had lower energy efficiency. Further, we observe that the energy efficiency of LSAS64TR is almost same as LSAS128TR, though LSAS64TR supports just half the number of MU-MIMO layers. This implies that the additional number of layers that LSAS128TR system supports, is compensated by power consumed to support the higher antenna chains and spatially multiplexed layers.Thus, MNOs interested in deploying high spectral efficiency solution along with high energy efficiency, may prefer to deploy LSAS64 systems for tower-top deployments of Radio unit deployment, since it offers higher energy efficiency with lower wind-load effect due to smaller antenna size.