Pilot power optimization in scalable user-centric HC-RANs for future IoT and IIoT applications

In this paper, a scalable user-centric HC-RAN is taken into consideration, where each remote radio head (RRH) serves user equipments (UEs) over the same time/frequency resources by using time division duplex (TDD) mode. Network scalability results in the front-haul load and computational complexity at the baseband unit (BBU) pool remaining constant irrespective of the number of UEs in the network. During the channel estimation phase, each RRH will acquire the channel state information (CSI) based on the received pilot signals from the UEs. With the available CSI, each RRH will decode/precode the desired UE information in uplink and downlink, respectively. However, in ultra-dense networks, pilot contamination is a major limitation that hugely impacts the system’s performance. To address this, we proposed an uplink pilot power optimization algorithm by considering the inter-user interference due to pilot sharing and RRH selection. In this algorithm, the pilot power coefficients are designed in such a way as to decrease the mean square error (MSE) of the channel estimates. To achieve this, we used the successive convex approximation method. Moreover, we derived a closed-form expression for achievable spectral efficiency (SE) per UE, which will be valid for any pilot/data power optimization and RRH selection scheme. The results show that the proposed algorithm significantly improves the system performance in the channel estimation phase and will be more suitable for urban environments.