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Massive MIMO is recognized as an important technology for advancing the deployment of 5th generation and beyond (B5G) networks. In a multi-cell large-scale multi-input multi-output (MIMO) non-orthogonal multi-access (NOMA) network, a base station (BS) with multiple antennas delivers radio frequency energy downlink and Internet of Things (IoT) devices. Uses the collected energy Supports the transmission of data about uplinks. This approach uses wireless power transfer (WPT) to investigate energy efficiency (EE) issues in large multi-cell MIMO NOMA networks. To maximize the EE of the network, we propose a new joint power, time, antenna selection, and subcarrier resource allocation scheme that can appropriately allocate time for power collection and data transmission. Both complete channel state information and incomplete channel state information (CSI) are considered and the corresponding EE performance is analyzed. The EE maximization problem, which is not trivial due to non-convexity, is formulated. First, we apply fractional nonlinear programming to transform the problem into a convex problem, and then we develop an ADMM-based approach (variance alternating direction method of multipliers) to solve this problem. This ADMM is combined with error reduction on the receiving side. Since these data errors are assumed with different channel estimation parameters and their relationship to energy efficiency has not yet been evaluated, a livestock grazing optimization algorithm (CGPO) is used to specify the data size capacity range for each RF link. Introduce error reduction. The proposed CGPO algorithm keeps tracking the error correction and detection process of the receiver for each radio frequency link. Finally, the numerical and analytical results show that the maximal Energy efficiency is achieved by a massive MIMO Systems using BIB-PA approach in the MATLAB platform.

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