Improved Nyquist-I Pulses to Enhance the Performance of OFDM-Based Systems

Pulse shaping is used in orthogonal frequency division multiplexing (OFDM) based systems to reduce inter-carrier interference (ICI) power and peak-to-average power ratio (PAPR), which are considered the major weaknesses in OFDM-based systems. A novel family of Nyquist-I pulses called sinc exponential pulse (SEP) is proposed, and it is characterized by two new design parameters that provide extra degrees of freedom for a certain roll-off factor, alpha. SEP effectively decreases the relative magnitude of the two largest side lobes of the SEP frequency function, which minimizes the ICI power and reduces the PAPR in OFDM systems. Furthermore, the SEP possesses a broader main lobe, which provides sufficient improvement in bit-error-rate (BER). The behavior of the SEP is examined in the time and frequency domain by tuning its design parameters to obtain the sub-optimum SEP. Theoretical and simulation results show that the sub-optimum SEP performs better than other existing pulses in terms of ICI power, signal-to-interference ratio (SIR) power, BER, and PAPR in OFDM-based systems

Multi-Sensor-Based Blind-Spot Reduction Technology and a Data-Logging Method Using a Gesture Recognition Algorithm Based on Micro E-Mobility in an IoT Environment

Autonomous driving is evolving through the convergence of object recognition using multiple sensors in the fourth industrial revolution. In this paper, we propose a system that utilizes data logging to control the functions of micro e-mobility vehicles (MEVs) and to build a database for autonomous driving with a gesture recognition algorithm for use in an IoT environment. The proposed system uses multiple sensors installed in an MEV to log driving data as the vehicle operates and to recognize objects surrounding the MEV to remove blind spots. In addition, the proposed system is capable of multi-sensor control and data logging for the MEV based on a gesture recognition algorithm, and it can provide safety information to allow the system to address blind spots or unexpected situations by recognizing the appearances or gestures of pedestrians around the MEV. The proposed system can be applied and extended in various fields, such as 5G communication, autonomous driving, and AI, which are the core technologies of the fourth industrial revolution

Low-PAPR Hybrid Filter for SC-FDMA

We propose a hybrid filter to reduce the peak-to-average power ratio (PAPR) on the transmitter side in a long-term evolution uplink scheme. The design of the proposed filter is based on two key components: a finite impulse response filter and a Nyquist-I pulse. We consider an envelope-constrained filter design to optimize the impulse response of the proposed filter in terms of PAPR reduction. Moreover, we propose a new family of Nyquist-I pulses, the exponential linear pulse, which has a new design parameter that helps reduce PAPR for a given roll-off factor and transmission scheme. Theoretical and numerical results show that the proposed filter outperforms existing filters in terms of PAPR and symbol error rate, and it has a less computationally complex impulse response expression than existing filters for the interleaved subcarrier mode of single-carrier frequency-division multiple access