Increasing demand for multicast transmission necessitates service-specific and precise quality-of-service (QoS) control. Since existing works provided limited methodologies such as best path selection, their ability is restricted by the given topology and the congestion status of the network. This paper proposes a fanout set partition (FSP) scheme to realize QoS-guaranteed multicast transmission. The FSP scheme adjusts the delay of the multicast flow by dividing its fanout set into smaller subsets. Since it is carried out based on the service requirement, service-specific QoS control is implemented. Mathematical analysis investigates the trade-offs, and the performance evaluation results show significant improvements under various traffic conditions.

This paper presents a G-band triple-push voltage controlled oscillator (VCO) operating around 177GHz. The VCO, implemented in a commercial 0.13-µm RFCMOS technology, adopts a triple-push topology that is composed of 3 symmetrically coupled identical Colpitts sub-oscillators. Oscillation frequency can be tuned from 175.9GHz to 178.4GHz with varactor tuning voltage swept from 0 to 1.2V. The calibrated output power ranged from -19.7dBm to -16.6dBm depending on the oscillation frequency. The measured phase noise of the VCO is -80.2dBc/Hz at 1MHz offset. The results clearly demonstrate the possibility of applying triple-push topology for VCOs operating beyond 100GHz, enabling various high frequency applications that require tunable frequency sources.

We propose a novel synthetic-benchmarks generation model using partial time-series regression, called Partial-Regression-Integrated Generic Model (PRIGM). PRIGM abstracts the unique characteristics of the input sensor data into generic time-series data confirming the generation similarity and evaluating the correctness of the synthetic benchmarks. The experimental results obtained by the proposed model with its formula verify that PRIGM preserves the time-series characteristics of empirical data in complex time-series data within 10.4% on an average difference in terms of descriptive statistics accuracy.