Abstract-In this paper, we relate the error vector magnitude (EVM) bit error rate (BER) and signal to noise ratio (SNR). We also present the fact that with such relationship it would be possible to predict or in cases substitute EVM in places of BER or even SNR. In doing so, we first define EVM with normalization so that the definition stands for multi-modulation systems, viz. binary phas shift keying (BPSK), quadrature phase shift keying (QPSK) etc. We also compare among the different performance metrics and show that EVM can be equivalently useful as signal to noise ratio and bit error rate. The relationships are based on stream based communication systems. A few Monte Carlo simulations are carried out to illustrate the performance of EVM based on these relationships.
The Green and cost-effective nature of microbial desalination cell (MDC) make it a promising alternative for future sustainable desalination. However, MDC suffers from a low desalination rate to be commercialized. External resistance (Rext) is one of the factors that significantly affect the desalination rate in MDCs, which is yet under debate. This research, for the first time, investigated the impact of Rext on MDCs having different internal resistance (Rint) of the system to discover the optimal range of Rext for efficient MDC performance. The results showed that the effect of Rext on desalination rate (2.52 mg/h) was quite low when Rint of MDC was high (200 Ω). However, operating MDC with low Rint (67 Ω) significantly improved the desalination rate (9.85 mg/h) and current generation. When MDC was operated with low Rint the effect of variable Rext on desalination and current generation was noticeable. Therefore, low Rint (67 Ω) MDC was used to select optimum Rext when the optimal range was found as Rext ≪ Rint, Rext < Rint, Rext ≈ Rint (ranging from 1–69 Ω) to achieve the highest desalination rates (10.41–8.59 mg/h). The results showed the superior effect of Rint on desalination rate before selecting the optimal range of Rext in the outer circuit.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.