A design of a fast and accurate optical Gaussian noise generator is proposed and demonstrated. The noise sample generation is based on the Box-Muller algorithm. The functions implementation was performed on a high-speed Altera Stratix EP1S25 field-programmable gate array (FPGA) development kit. It enabled the generation of 150 million 16-bit noise samples per second. The Gaussian noise generator required only 7.4% of the FPGA logic elements, 1.2% of the RAM memory, 0.04% of the ROM memory, and a laser source. The optical pulses were generated by a laser source externally modulated by the data bit samples using the frequency-shift keying technique. The accuracy of the noise samples was evaluated for different sequences size and confidence intervals. The noise sample pattern was validated by the Bhattacharyya distance (Bd) and the autocorrelation function. The results showed that the proposed design of the optical Gaussian noise generator is very promising to evaluate the performance of optical communications channels with very low bit-error-rate values.
An optical packet compression for data samples with different bit patterns and scaling degrees is proposed and demonstrated. The compression stages are composed of optical modulators, 3-dB couplers and fiber delay lines. Erbium-doped fiber amplifiers are applied only between pairs of compression stages. An experiment of 16-bit packet size with compression from 150 MHz to 2.5 GHz is also reported. The proposed scheme achieved low costs in terms of optical components while preserving the signal pattern and scaling degree integrity. It is also shown that the scaling monitoring is an important performance issue for optical communications systems.
Call admission control (CAC) of time-dependent video connections is an important issue for network traffic engineering. The impact of this traffic dependence on video call acceptance region is examined in this article. We considered two different CAC mechanisms; (1) a descriptor- based CAC mechanism and (2) a measurement-based CAC (MBCAC) mechanism. The proposed MBCAC is a hybrid measurement scheme that includes a Kalman filter and a real-time Hurst estimation. We investigated several buffer sizes and video sequences with different dependence degrees. For the accuracy of the Hurst estimation, we developed a Hurst parameter package. The package consists of three different estimators, R/S, Higuchi and Abry-Veitch (wavelet). An important result shows that long-range dependence and short- range dependence connections have similar admission regions.