text: Recently, Cloud-based Radio Access Network (C-RAN) has been proposed as a potential solution to reduce energy cost in cellular networks. C-RAN centralizes the baseband processing capabilities of Base Stations (BSs) in a cloud computing platform in the form of BaseBand Unit (BBU) pool. In C-RAN, power consumed by the traditional BS system is distributed as wireless transmission power of the Remote Radio Heads (RRHs) and baseband processing power of the BBU pool. Different from previous work where wireless transmission power and baseband processing power are optimized individually and independently, this paper focuses on joint optimization of allocation for these two kinds of power and attempts to minimize the total power consumption subject to Quality of Service (QoS) requirements from users in terms of data rates. First, we exploit the load coupling model to express the coupling relations among power, load and user data rates. Based on the load coupling mode, we formulate the joint power optimization problem in C-RAN over both wireless transmission power and baseband processing power. Second, we prove that operating at full load may not be optimal in minimizing the total power consumption in C-RAN. Finally, we propose an efficient iterative algorithm to solve the target problem. Simulations have been performed to validate our theoretical and algorithmic work. The results show that the proposed algorithm outperforms existing schemes (without joint power optimization) in terms of power consumption.
  @cite_19: In cloud radio access networks (C-RANs), the baseband processing units (BBUs) from traditional base stations (BSs) are virtualized and moved into a single centralized location. By virtualization, the computing resources of BBUs can be dynamically shared among all cells, enabling a significant improvement in computing resource utilization and power efficiency. In this letter, we propose a BBUs virtualization scheme that minimizes the power consumption with a linear computational complexity order. The scheme is based on a heuristic simulated annealing (HSA) algorithm, which combines a bin packing algorithm with SA. Simulation results show that the HSA effectively decreases system power consumption when compared to standard approaches.
  @cite_8: In order to reduce high construction cost and energy consumption of distributed base stations in cellular networks, the centralized radio access Cellular network infrastructure with a Super BS(CSBS) architecture has been proposed in which the centralized processing resource can be flexibly organized. However, there is sill the problem of low processing resource efficiency in protocol processing. In this paper, based on a protocol processing resource management framework, distributed load diversity based processing resource allocation (D-LDA) as well as the improved hybrid load diversity based processing resource allocation(H-LDA) are proposed, which take load discrepancy into consideration in the situation that a large number of BSs are supported. Simulation results show that D-LDA and H-LDA algorithm significantly outperforms the conventional method respectively by more than 30 and 55 in the processing resource efficiency.
summary: There also exists some work on optimizing baseband processing power in C-RANs. By virtualization, the baseband processing resources in the BBU pool are dynamically shared among all RRHs. In @cite_8 , a dynamic programming scheme is proposed to minimize the baseband processing power consumption in the case of dynamic cell traffic load. However, the computational complexity of the proposed scheme is exponential. In order to reduce the computational complexity, the authors in @cite_19 propose a BBU virtualization scheme that minimizes the baseband processing power consumption with a linear computational complexity order.