Penn State College of Communications

About: Penn State College of Communications is a based out in . It is known for research contribution in the topics: Relay & Cognitive radio. The organization has 2106 authors who have published 2119 publications receiving 24693 citations.

Peak-to-Average Power Ratio of Multicarrier Faster-Than-Nyquist Signals: Distribution, Optimization and Reduction

Abstract: Multicarrier faster-than-Nyquist (MFTN) is a spectral efficient transmission scheme for future communication systems. However, as one of the most important drawbacks of multicarrier transmission systems, the peak-to-average power ratio (PAPR) of MFTN signals is still not clear yet. In this paper, we investigate the PAPR of MFTN signals for nonlinear satellite communication systems. First of all, the PAPR distribution of MFTN signals under various practical situations is evaluated, and we show that the PAPR of MFTN signals is closely related to the number of subcarriers, the shaping pulse, and the time–frequency packing factors. Moreover, different from conventional Nyquist multicarrier signals, certain time–frequency packing in MFTN will however, improve the PAPR performance. Then, the PAPR of MFTN signals under the given spectral efficiency is considered. By jointly optimizing the time–frequency spacing to minimize the PAPR, we show that the MFTN combined with low-order modulation could be more energy efficient than the corresponding Nyquist high-order modulation signals. Finally, we investigate the PAPR reduction for MFTN signals. Specifically, a selective mapping-based alternative-signal scheme, which mainly intended for the conventional Nyquist multicarrier signals, is extended to the MFTN signaling system. It is shown that the PAPR reduction performance of the considered scheme is robust to the time–frequency packing, and even approaches the conventional orthogonal frequency-division multiplexing system. Our numerical results validate that besides the high spectral efficiency, the MFTN may be also an energy efficient transmission scheme for next-generation satellite communication systems.

ARNC Multicasting of HDCP Data for Cooperative Mobile Devices With Dual Interfaces

Abstract: This letter focuses on the hard deadline constrained prioritized data multicasting scene, in which each user device (UD) that equips with dual wireless interfaces can receive data from the base station and transmit or receive data from near-by UDs simultaneously. In order to maximize the sum throughput, we aim to find the optimal and suboptimal solutions using the backward induction algorithm (BIA) and greedy algorithm (GA) based on full feedback. To decrease the large feedback resources caused by full feedback, we propose a limited feedback strategy, which requires very few channel occupation. We find that the optimal and suboptimal solutions under this limited feedback strategy can also be solved using BIA and GA, but with high complexity. Hence, a heuristic algorithm under this strategy is proposed, which has very low complexity and can keep considerable performance at the same time.

Predictability Analysis of Spectrum State Evolution: Performance Bounds and Real-World Data Analytics

Abstract: Predictability in spectrum prediction refers to the degree to which a correct prediction of the radio spectrum state (RSS) can be made quantitatively. It is obvious that the possibility that the future RSS is accurately predicted will be different when using different spectrum prediction algorithms. However, the fundamental limits on the accuracy of various spectrum prediction algorithms should exist and be worthwhile to be paid attention to. In this paper, we define these fundamental limits as the performance bounds of predictability, which can be the important indexes when evaluating the performance of different spectrum prediction algorithms. Real-world spectrum data is involved to present comprehensive and profound analysis of the predictability. We first transform large amount of spectrum data into symbol sequences by sampling and quantization, to calculate the entropy of the symbol sequence, which represents the randomness of the RSS evolution. Then, we derive the upper bound and the lower bound of the predictability mainly from entropies of the symbol sequences. Further, we conduct the detailed analysis on the performance bounds of the predictability of the RSS. Based on real-world data analytics, the key insights among others include: 1) entropies almost have no relationship with selection of sampling intervals in the data preprocessing; 2) the upper and the lower bounds of the predictability will both decrease as the quantization level rises and tend to be stable around a value at last; and 3) two kinds of lower bounds of the predictability are proposed, and one of the lower bounds, the regularity $R$ , can reveal the tidal effect of the evolution of the RSS.

Auction-Based Secondary Relay Selection on Overlay Spectrum Sharing in Hybrid Satellite-Terrestrial Sensor Networks.

Abstract: In this paper, we investigate the auction-based secondary relay selection on overlay spectrum sharing in hybrid satellite–terrestrial sensor networks (HSTSNs), where both the decode-and-forward (DF) and amplify-and-forward (AF) relay protocols are analyzed based on time division multiple access (TDMA). As both the primary and secondary networks are rational, honest but with incomplete network information, they prefer to obtain maximum possibility payoffs by the cooperation between the primary and secondary networks, and the competition among secondary networks. Hence, Vickery auction is introduced to achieve the effective and efficient secondary relay selection by distinct sub-time slot allocation for one shot in terms of a distributed manner. Finally, numerical simulations are provided to validate the effectiveness of the auction mechanism on cooperative spectrum sharing in HSTSNs for secondary relay selection. Besides, the effect of key factors on the performance of the auction mechanism are analyzed in details.

Icing performance of superhydrophobic silicone rubber surfaces by laser texturing

Abstract: In this paper, the superhydrophobic surfaces of silicone rubber with different microstructure were directly prepared by texturing with a nanosecond fibre laser. The superhydrophobic surfaces have excellent anti-icing performance. Even at 0 ° C, the superhydrophobic surface has a contact angle of ~150° and a rolling-off angle of ~2.5°. The superhydrophobic silicone rubber surfaces with different microstructures have obvious differences in contact behaviours with water droplets at low temperatures. The surface textured with a laser fluence of 10 J cm has a larger particle size and more abundant micro-nano particles, which results in a smaller contact area with the water droplet due to greater roughness and root mean square slope. The deeper the small gaps on the superhydrophobic surface, the more time it takes for the change in contact state between the surface and the water droplets. The adhesion strength of the superhydrophobic rubber surfaces with the ice layer were smaller due to the air stored between the surfaces and the ice layer. In particular, the laser textured surface with an laser fluence of 10 J cm has the lowest ice adhesion strength due to its layered micro-nano composite structure. After 30 cycles of icing and de-icing, the processed silicone rubber surface still retains excellent hydrophobicity. The superhydrophobic silicone rubber surface has important value in anti-icing and anti-pollution applications.