The paper analyzes the performance of two forms of orthogonal frequency division multiplexing (OFDM) techniques called as DC biased Optical OFDM (DCO-OFDM) and Flip-OFDM for intensity modulated direct detection (IM/DD) system. The aforementioned OFDM schemes are compared in terms of peak-to-average power ratio (PAPR) and bit error rate (BER) as a function of electrical energy per bit to noise power ratio (E b(elec) /N 0 ) when the signal is affected by additive white Gaussian noise (AWGN). In particular, 7-dB and 13-dB DC bias are used for DCO-OFDM in BER analysis and finally compared with flip-OFDM for the different data formats such as 4-,16-,64-,256-QAMs. The results show that flip-OFDM performs better in BER analysis with reduced value of (E b(elec) /N 0 ) and thus, it can be considered as a power efficient unipolar modulation format. However, Flip-OFDM signal provides a high PAPR value that can deteriorate the overall system performance. Finally, OFDM signal performance tradeoff is measured as a function of transmitted optical power, QAM size, and spectral efficiency.
This paper proposes a new hybrid orthogonal frequency division multiplexing (OFDM) form termed as DC-biased pulse amplitude modulated optical OFDM (DPO-OFDM) by combining the ideas of the existing DC-biased optical OFDM (DCO-OFDM) and pulse amplitude modulated discrete multitone (PAM-DMT). The analysis indicates that the required DC-bias for DPO-OFDM-based light fidelity (LiFi) depends on the dimming level and the components of the DPO-OFDM. The bit error rate (BER) performance and dimming flexibility of the DPO-OFDM and existing OFDM schemes are evaluated using MATLAB tools. The results show that the proposed DPO-OFDM is power efficient and has a wide dimming range. Furthermore, a switching algorithm is introduced for LiFi, where the individual components of the hybrid OFDM are switched according to a target dimming level. Next, machine learning algorithms are used for the first time to find the appropriate proportions of the hybrid OFDM components. It is shown that polynomial regression of degree 4 can reliably predict the constellation size of the DCO-OFDM component of DPO-OFDM for a given constellation size of PAM-DMT. With the component switching and the machine learning algorithms, DPO-OFDM-based LiFi is power efficient at a wide dimming range.
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