Evaluation of Joint Technique Iterative Clipping Filtering (ICF) and Neural Network Predistortion on SDR-based MIMO-OFDM System

Abstract

Multiple-input, multiple-output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a communications technology that powers numerous modern communication systems, including 5G and WiFi-6. This technology is utilized in current communication systems due to its high performance and extensive channel capacity. MIMO-OFDM does have disadvantages, such as large Peak-to-Average Power Ratio (PAPR) values. If the signal is processed by a nonlinear Power Amplifier (PA) device, a high PAPR value signal can result in both in-band and out-of-band signal distortion. To combat high PAPR values, PAPR reduction strategies such as Iterative Clipping Filtering (ICF) are utilized. From this study, using ICF with iteration 2 and Clipping Ratios (CR) 3 and 4 can improve the system's minimum Bit Error Rate (BER) by about 22.8% and 91.1%, respectively. Choosing the correct CR will improve the system, but using the lower CR will make it worse than a system without ICF. This occurs in systems using ICF with iterations two and CR 2 and at the same SNR conditions as systems without ICF; using ICF with iterations two and CR 2 results in higher BER values. The use of Predistortion Neural Network (PDNN) can overcome this problem. By using PDNN, there is an improvement in the system where the minimum BER value can reach 0.1 × 10-5. The percentage decrease in BER from using PDNN for ICF with iterations two and CR 2, 3, and 4 is 99.88%, 99.86%, and 98.807%, respectively. Thus, the joint techniques of ICF and PDNN can significantly enhance the performance of MIMO-OFDM systems with nonlinear PA. Importantly, the experiment was conducted on an SDR device, ensuring the real-world applicability of the results.