An Effective Investigation on Performance Analysis of LS and MMSE Channel Estimation Methods in 5G OFDM Wireless Communication Systems

Abstract:-The exponential growth in wireless throughput, driven by diverse user demands for high Quality of Service (QoS), is expected to persist in the coming years. To address this, fifthgeneration (5G) and beyond wireless communication systems have integrated several disruptive technologies—such as Massive MIMO and Reconfigurable Intelligent Surfaces (RIS)—to accommodate increasing wireless data traffic and ensure reliable communication. Among the enabling technologies, Orthogonal Frequency Division Multiplexing (OFDM) has played a crucial role due to its proven success in wideband communications. In fact, OFDM remains a foundational component of 5G systems, as it effectively mitigates frequency-selective fading, thereby maintaining high communication quality in multipath environments. It also significantly improves spectral efficiency compared to single-carrier schemes. However, when signals propagate through wireless multipath channels, they encounter several impairments, including large physical obstructions, multipath fading, local scattering, and mutual interference from shared timefrequency resources. To accurately decode the transmitted signal, the receiver must estimate and compensate for the effects of the channel, which requires precise channel state information (CSI). This estimation is performed using pilot signals, which are pre-known to both the transmitter and receiver. In 5G systems, the structure and placement of these pilot symbols can vary across different use cases. Among traditional channel estimation techniques, the Least Squares (LS) method is widely recognized for its low computational complexity, as it does not require prior knowledge of channel statistics. However, LS often yields higher estimation errors in practical scenarios, especially in environments with severe multipath effects. As an alternative, the Minimum Mean Square Error (MMSE) method offers improved estimation accuracy.

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