Abstract:
The evolution of technologies in data communication system has resulted in generation of large data. With the availability of limited bandwidth and to maintain the energy efficiency of the wireless communication system, data reduction plays a significant amount of role in preserving the lifetime of wireless communication system. Considering the significance of this problem in wireless communication system, in this work, we present data reduction and recovery or reconstruction on multivariate data using principal component analysis (PCA). In general, we send the eigen coefficients from the source and use these coefficients at the destination to recover or reconstruct the original data back in wireless communication system. We employ in our work two different digital modulation techniques such as phase-shift keying (PSK) and quadrature amplitude modulation (QAM) with seven different bit rate to demonstrate our approach. We carry the experiments independently using five different signal ensemble and four different multiple-input, multiple-output (MIMO) configuration along with Rayleigh fading. This work presents an extensive simulated benchmark results based on performance matrix such as root mean square error (RMSE) and bit error rate (BER) at different values of signal-to-noise ratio (SNR). We further validate our approach by presenting synthesis of our proposed model using Xilinx target device Virtex-6 LX240T Field Programmable Gate Array (FPGA). The experimental results reported based on simulation, synthesis, and validation of model demonstrate the applicability of our proposed approach in real-time scenario.
