No abstract

Energy Efficiency (EE) plays a significant role in the progress towards the Fifth-Generation (5G) wireless communication networks. Due to the higher Spectral Efficiency (SE) and EE, Massive Multiple-Input Multiple-Output (MIMO) is a promising model for the 5G networks. In this work, a Channel Selection (CS) scheme is proposed by selecting the optimal channel using the Chicken Swarm Optimization (CSO) algorithm. A massive MIMO model is implemented by considering the SE, EE and Resource Efficiency (RE). The main objective is to optimize the beam-forming vectors and power allocation for all the users. The RE metric considering the multi-objective function can be defined to develop an effective and robust design with balanced SE and EE. The objective function for generating the optimal beam forming vectors is satisfying the Signal to Interference-Plus-Noise Ratio (SINR) constraints. The CSO Algorithm is applied to generate the beam-forming vectors and power allocation, based on the channel characteristics. The channel state information is predicted and a projection matrix with channel estimation framework is formed. The selection of the index sets in the iteration process provides the optimized channel. Data transmission is performed through the optimal channel. From the comparative analysis, it is observed that the proposed CS scheme provides better SE and EE than the existing CS schemes.

Summary The massive multiple input multiple output (mMIMO) provides reliable base station (BS) for the mobile users (MUs) with CSI (channel state information) and jointly offers spectral efficiency (SE) and energy efficiency (EE). Conversely, because of the existence of multiple transceivers at both the transmitter and receiver side, the channel estimation (CE) issue is increasingly complex and expensive in terms of hardware and energy utilization. Hence, we have proposed an effective Hybrid Grey Wolf Optimization with Cuckoo Search (GWO‐CS) based optimal channel estimation for developing energy efficient mMIMO. The proposed GWO‐CS selects the optimal channel by jointly optimizing the spectral efficiency and reduces the SINR (signal to interference plus noise ratio). Experimental analysis of the performance of the proposed approach is carried out using existing approaches. The results obtained show that, the EE of the proposed GWO‐CS based CE provides 15–37 Mbits for varying quantization bits, 22.5–25 Mbits for different user equipment (UE) ranges and 7–24 Mbits for various SE. However, the existing approaches fail to provide such EE and this proves the efficiency of proposed approach.