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Performance Analysis of a DPSK Modulated Ultra-Dense WDM System at Different Bit Rates

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Abstract

In this paper, we design an ultra-dense wavelength division multiplexing (UD-WDM) system with 400 channels based on the differential phase-shift keying (DPSK) modulation technique and compared the performance of the proposed system with the one not having the DPSK modulation technique. The performance is investigated in terms of quality factor (Q-Factor) and bit error rate (BER) with varying transmission distance from 30 to 250 km and the number of channels from 50 to 400 for bit rates of 10 and 40 Gb/s per channel. The findings showed that the system with the DPSK modulation technique outperformed the system without the DPSK modulation technique for both 10 and 40 Gb/s bit rate per channel. The reported values of BER and Q-factor with transmission distance for DPSK modulated system for 10 Gb/s bit rate are around 10–9 and 14 dB respectively and for 40 Gb/s bit rate, the values are around 10–6 and 13.2 dB respectively, which are better than the values in the system without DPSK modulation technique which reported BER of around 10–3 and Q-factor of 4.2 dB for 10 Gb/s bit rate and BER of around 10–2 and Q-factor of 4.1 dB for 40 Gb/s bit rate. When the number of channels is taken into consideration instead of distance, the values of BER and the quality factor for DPSK modulated system is reported around 10–8 and 13.2 dB for 10 Gb/s bit rate and 10–6 and 9.2 dB for 40 Gb/s bit rate respectively which are better than the values in the system without DPSK modulation which reported BER of around 10–4 and Q-factor of 4.1 dB for 10 Gb/s bit rate and BER of around 10–1 and Q-factor of 4 dB for 40Gb/s bit rate. In the end, the eye diagrams for both the systems are compared and found better eye-opening in DPSK modulated system than the system without the DPSK modulation technique.

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Correspondence to Chakresh Kumar.

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Rajeev, Chakresh Kumar Performance Analysis of a DPSK Modulated Ultra-Dense WDM System at Different Bit Rates. Opt. Mem. Neural Networks 31, 191–205 (2022). https://doi.org/10.3103/S1060992X22020060

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