Mohammad S. Rahman

On the Extended Relationships Among EVM, BER and SNR as Performance Metrics

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.

On the error vector magnitude as a performance metric and comparative analysis

In this paper, we present the error vector magnitude (EVM) as a figure of merit for assessing the quality of digitally modulated telecommunication signals. We define EVM for a common industry standard and derive the relationships among EVM, signal to noise ratio (SNR) and bit error rate (BER). 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. A few simulation results are presented to illustrate the performance of EVM based on these relationships.

On the Lempel-Ziv-Welch coded speech transmission and markov chain detection

In this paper, we present a novel technique for speech transmission over noisy channels using Lempel-Ziv-Welch (LZW) source coding and Markov chain detection. The basic idea is to make use of the highly dynamic nature and high compression ratio of LZW algorithm that is possible for speech signals due to its correlation. However, the erroneous reception after noise is added to this coded signal poses challenge of dynamic dictionary forming. In this paper, assuming that speech maintains high order memory, this has been non-trivially handled by high order Markov chain (MC) modeling, detection and correction technique. This technique ensures that the decoded speech maintains the approximate correlation normally present in speech. In order to justify the quality of speech we have made use of spectrogram analysis. Few simulations for the whole process have been performed to illustrate the performance. This speech compression technique can be used real-time and imposes no outstanding delay at the reception side except the transmission delay plus the minimal decoding and detection time