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One of the main drawbacks of orthogonal frequency division multiplexing (OFDM) is the unpredictable significant increase in the peak-to-average power ratio (PAPR), due to the large dynamic range of the OFDM symbol waveforms. To overcome this problem, we propose a novel PAPR reduction technique in the transmitter DSP of a fiber mobile fronthaul network, named as iterative selective mapping (I-SLM). The proposed algorithm uses orthogonal phase vectors construction. Several alternative sets of the OFDM symbol are iteratively generated by deterministic sets of orthogonal phase vectors and the one which introduces the lowest peak power value is considered for transmission. Simulation results demonstrate a substantial PAPR reduction, comparing to the partial orthogonal selective mapping (POSLM), especially when the number of iterations increases in the presence of different variables, such as the number of OFDM subcarriers/frame and the modulation order.

Universal filtered multi-carrier (UFMC) is a potential multi-carrier system for future cellular networks. UFMC provides low latency, frequency offset robustness, and reduced out-of-band (OOB) emission that results in better spectral efficiency. However, UFMC suffers from the problem of high peak-to-average power ratio (PAPR), which might impact the function of high power amplifiers causing a nonlinear distortion. We propose a comparative probabilistic PAPR reduction technique, called the decomposed selective mapping approach, to alleviate PAPR in UFMC systems. The concept of this proposal depends on decomposing the complex symbol into real and imaginary parts, and then converting each part to a number of different phase vectors prior to the inverse fast Fourier transform (IFFT) operation. The IFFT copy, which introduces the lowest PAPR, is considered for transmission. Results obtained using theoretical analysis and simulations show that the proposed approach can significantly enhance the performance of the UFMC system in terms of PAPR reduction. Besides, it maintains the OOB emission with candidate bit error rate and error vector magnitude performances.

In this paper, we address the problem of modulation format identification (MFI) for few mode fiber (FMF) transmission in elastic optical networks (EONs). The MFI accuracy is studied under different FMF channel conditions including mode coupling (MC), optical signal-to-noise ratio (OSNR), and chromatic dispersion (CD). Artificial neural network, trained using features extracted from the asynchronous in-phase quadrature histogram (IQH), is proposed to investigate the identification accuracy. Extensive simulation results have been conducted to identify six modulation schemes widely used in polarization division multiplexing coherent optical networks. This includes PDM-BPSK, PDM-QPSK, PDM-8QAM, PDM-16QAM, PDM-32QAM, and PDM-64QAM transmitted at 10 Gbaud network transmission speed. The results show that the proposed MFI achieves a successful average identification accuracy exceeding 98% in the presence of low MC when the incoming signal OSNR is greater than 20 dB. However, the effect of high MC and CD = 1100 ps/nm reduces the average accuracy to 90%. Further, the MFI accuracy is investigated under different symbol rates such as 14 and 20 Gbaud. INDEX TERMS Coherent optical communication, few mode fiber, modulation format identification.