An Enhanced Pilot Aided Channel Estimation in XL-MIMO Communication Systems Using Bitterling Swallow Fish Optimization

Abstract

The continuous evolution of 6th Generation (6G) wireless networks places Extremely Large-scale Multiple-Input Multiple-Output (XL-MIMO) schemes as a crucial enabler for ultra-reliable and high data rate communication. Channel estimation in XL-MIMO is crucial here because it allows the system to precisely recognize the wireless channel conditions between the transmitter and receiver, which is vital for enhancing signal processing, and resource allocation. Traditional pilot-aided channel estimation approaches face challenges, such as high error. Hence, this work proposes an innovative model called the Deep Kronecker Network-Bitterling Swallow Fish Optimization Algorithm (DKN-BSwaFOA) for pilot-aided channel estimation in XL-MIMO systems. Initially, the system model of XL-MIMO is contemplated. The pilot insertion is done at the transmitter, and the location of the pilot symbol is optimally selected using BSwaFOA. The signal is propagated over the hybrid field channel, where both farfield and near-field components coexist. At the receiver, the channel is estimated using DKN, which is trained using the proposed BSwaFOA. The experimental outcomes demonstrated that the DKN_BSwaFOA computed the minimum Root Mean Square Error (RMSE), Bit Error Rate (BER), and Mean Square Error (MSE) of 0.030, 0.002, and 0.001.OPEN ACCESS Received: 23/09/2025 Accepted: 11/11/2025

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