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Channel Estimation and Optimal Power Allocation using Adaptive Optimizer in Cell Free Massive MIMO-NOMA

International Journal of Electrical and Electronics Engineering
© 2026 by SSRG - IJEEE Journal
Volume 13 Issue 1
Year of Publication : 2026
Authors : V Satya Kumar Kudipudi, S. Neeraja
10.14445/23488379/IJEEE-V13I1P114
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How to Cite?

V Satya Kumar Kudipudi, S. Neeraja, "Channel Estimation and Optimal Power Allocation using Adaptive Optimizer in Cell Free Massive MIMO-NOMA," SSRG International Journal of Electrical and Electronics Engineering, vol. 13,  no. 1, pp. 142-153, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I1P114

Abstract:

Non-Orthogonal Multiple Access (NOMA) is considered as one of the emerging multi-access technologies for 5G communication, and also can enhance the system’s performance. It is integrated with Cell-Free Massive Multiple Input Multiple Output (CF-MA-MIMO) to support multiple users, producing a high gain. Optimizing spectral and energy efficiencies is a challenging process due to the non-linear programming involved. The proposed model considered the downlink transmission of the Cell Free-Massive-Multiple Input Multiple Output- Non-Orthogonal Multiple Access (CF- MA-MIMO-NOMA). This work presents an enhanced approach for channel estimation and optimal power allocation in CF- MA-MIMO-NOMA. Initially, the user equipment estimates the channels for every user and then provides them to the MA-MIMO. Then, the Expectation Maximization (EM) is utilized for Channel Estimation (CE), and the metaheuristic algorithm Adaptive Squirrel Search Optimization (ASSO) is utilized for optimal power allocation. The proposed ASSO attained better spectral efficiency, energy efficiency, and sum rate by the power allocation at the different users and SNR values.

Keywords:

Cell Free-Massive-Multiple-Input Multiple-Output, Non-Orthogonal Multiple Access, Expectation Maximization, Adaptive Squirrel Search Optimization.

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