Kelvin Anoh
ABSTRACT
Multicarrier modulation demultiplexes a wide bandwidth into many narrowbands increasing the spectral efficiency by up to 50%. The narrowbands results in extended symbol time, which is useful for combating intersymbol interference. Unfortunately, this scheme suffers from peak-to-average power ratio (PAPR) problem. The PAPR problem is particularly prevalent in orthogonal frequency division multiplexing (OFDM) systems and several different approaches for reducing the PAPR problem have been reported in literature. The problem is significantly detrimental to high power amplifiers (HPA) causing the HPA system to consume high power. In addition, HPAs may distort the OFDM signal amplitude leading to diminished signal quality at the receiver. By reducing the PAPR of OFDM systems, high power consumption of HPAs can be reduced. The speech will discuss different multicarrier kernels recently used in the design of OFDMfi?!like systems for reducing the PAPR problem with some tolerable trade-offs. The speech will also present the use of machine learning tools for PAPR reduction.
Index Terms
- Advances in PAPR reduction for OFDM systems with machine learning
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