Ergodic Capacity Analysis of Remote Radio Head Associations in Cloud   Radio Access Networks
  Characterizing user to Remote Radio Head (RRH) association strategies in cloud radio access networks (C-RANs) is critical for performance optimization. In this letter, the single nearest and N--nearest RRH association strategies are presented, and the corresponding impact on the ergodic capacity of C-RANs is analyzed, where RRHs are distributed according to a stationary point process. Closed-form expressions for the ergodic capacity of the proposed RRH association strategies are derived. Simulation results demonstrate that the derived uplink closed-form capacity expressions are accurate. Furthermore, the analysis and simulation results show that the ergodic capacity gain is not linear with either the RRH density or the number of antenna per RRH. The ergodic capacity gain from the RRH density is larger than that from the number of antennas per RRH,which indicates that the association number of the RRH should not be bigger than 4 to balance the performance gain and the implementation cost. 
Mugen Peng, Shi Yan and H. Vincent Poor
cs.IT math.IT