# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Radio Resource Management Techniques
#### 3.1.1 Power Control Schemes  
[PERSON] et al. proposed a two-step deep reinforcement learning (DRL)-based framework for dynamic allocation of resources in C-RAN, to minimize the total power consumption while satisfying the user demand in the network [39]. The authors defined the states space, action space, and reward function for the DRL agent and proposed a two-stage DRL technique: an offline deep neural network (DNN) construction phase and an online dynamic deep Q learning phase. First, the DRL agent determines the active set of RRHs by turning itself on or off, to reduce the action space size. It then determines an optimal resource allocation solution (beamforming) with the active set of RRHs by solving a convex optimization problem. [PERSON] et al. investigated the power and bandwidth allocation problem in C-RAN with the aim of minimizing the total transmission power for a specified set of RRHs while satisfying the average rate and spectral efficiency requirement [40]. The problem was formulated as a traffic density-based RRH selection task with minimum power consumption. A series of network constraints were considered,  
Figure 4: Resource management techniques in C-RAN. such as transmission power budget, spectrum limitation, traffic demand, and spectral efficiency requirement. To solve this problem, they developed an efficient local search algorithm based on the optimal values of the power and bandwidth allocation problem and introduced three local improvement operations (\"add,\" \"open,\" and \"close\") to determine locally optimal solutions rapidly. In [41], the work of [40] is extended. The authors designed a static RRH selection scheme for load balancing among RRHs. Furthermore, based on this, a dynamic RRH switching mechanism considering a fairness index to measure the imbalance degree of traffic load is presented. The RRH selection is performed using an efficient local search procedure in conjunction with a user association algorithm, considering the bandwidth and power budgets of the RRH and energy consumption of the system. They designed a bandwidth and power allocation algorithm for determining the minimum power consumption of the RRH while satisfying the rate requirements of users. Subsequently, the fairness index was defined by introducing three local improvement operations: \"open,\" \"close,\" and \"exchange.\" The RRH switching mechanism is designed such that when the bandwidth and power requirements of the user are not satisfied and the fairness index is larger than a predefined threshold, the switching is triggered for load rebalancing while the signaling overhead of the system is controlled. However, the selection of the fairness index threshold is critical because a signaling overhead may be generated and decrease the system performance because of the handover of users, during RRH switching. [PERSON] et al. [42] proposed a strategy for switching a BBU on/off according to the traffic load in the associated RRH by using a host server (HS) in the BBU pool, which assigns a newly arrived user to a certain BBU. They formulated an optimization problem of reducing the number of BBUs with low loads by transferring the load to neighboring BBUs with the available capacity. They proposed a combined bin packing method (BPM) and modified the best fit decreasing (MFBD) algorithm to solve this problem. Here, each BBU is considered as a bin for traffic load balancing. In [43], [PERSON] et al. investigated the power consumption tradeoff between BBUs and RRHs. They formulated a theoretical model for BBU aggregation to determine the optimal traffic threshold for RRH switching to minimize the total power consumption. The authors considered an architecture involving (1) subareas, with three types of RRHs, based on the coverage area of RRHs and (2) two modes for the subareas (LC mode and SC mode) based on the states and types of RRHs in that area. First, the expected number of LC mode and SC mode RRHs are calculated. Next, the traffic loads carried by the active RRHs are calculated.