Efficient Design of Precoded Multicarrier System using Software Defined Radio

Orthogonal frequency division multiplexing (OFDM) is crucial for 5G and beyond because of its excellent spectral efficiency and resilience to multipath fading in contemporary communication systems. The high Peak-to-Average Power Ratio (PAPR), which impairs system performance by resulting in power inefficiency and nonlinear distortion in the power amplifier, is one of the main issues with OFDM systems. To lower PAPR in OFDM systems, this work uses cutting-edge methods such as Zadoff-Chu Matrix Transform (ZCT) and Singular Value Decomposition (SVD). By applying ZCT precoding for improved power distribution and SVD for signal breakdown into orthogonal components, the proposed model seeks to enhance the conventional OFDM system while achieving a significant PAPR reduction. In addition to increasing power efficiency, SVD and ZCT together maintain a low bit error rate (BER) and enhance system performance. This proposed work uses Python module blocks for simulation and analysis to integrate Octave scripts into GNU Radio. Initially, the proposed model is implemented in the Octave environment to evaluate the effectiveness of the PAPR reduction strategies. Next, using Python, the Octave code is incorporated into software-defined radio (SDR) software like GNU Radio to enable dynamic testing and real-time evaluation. In addition to improving flexibility, this hybrid method provides a scalable framework for wireless systems in the future. In comparison to conventional OFDM systems, the proposed system shows a noticeable decrease, reaching a PAPR of about 5 dB difference, improving overall system performance.