Comparative Analysis of Estimation Techniques for IQ Imbalance for OFDM Based WiMAX System: A Review

Wireless communication is becoming very popular now a days. Worldwide Interoperability for Microwave Access (WiMAX) system in wireless communication plays an important role and has been attracting the researchers in the last decade. WiMAX system has provided anefficient solution for ‘last mile’ access technology to provide high data rate internet access for residential as well as small business purposes. Taking the advantages of Orthogonal Frequency Division Multiplexing (OFDM) technique, the WiMAX system is able toprovide robust broadband service in hostile wireless channel.However the most fundamental issuewith WiMAX system is imbalance quadrature (IQ) imbalance. The system performance may be degraded in WiMAX systemdue to in-phase quadrature imbalance (i.e. IQ imbalance) at the transmitter (Tx) and receiver (Rx). Therefore, it is necessary to estimate the IQ imbalance parameters and accordingly compensate for the IQ imbalance.There are many estimation techniques schemes available but least square estimation technique is better to choose. WiMAX have many applications like Digital Audio Broadcast (DAB), Digital Video Broadcast (DVB) and many more. So it becomes very important to estimate and compensate IQ imbalance to improve the performance of WiMAX systems. That’s why the role of estimation and compensation technique becomes very important. This survey aims to provide useful guidance for system designers on how to estimate and compensate the IQ imbalance for OFDM based systems. This paper describes comprehensive review on some popular and efficient estimation and compensation techniques like Least Square estimation (LSE), Expectation Maximization (EM) algorithm. The most representative estimation and compensation techniques are described, discussed and qualitatively compared. Keywords—Worldwide Interoperability for Microwave access (WiMAX), Least square estimation (LSE), In-phase quadrature imbalance (IQI), signal to noise ratio (SNR), Bit error rate