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.

Joint Beamforming and Power Allocation for Satellite-Terrestrial Integrated Networks With Non-Orthogonal Multiple Access

Abstract: In this paper, we propose a joint optimization design for a non-orthogonal multiple access (NOMA)-based satellite-terrestrial integrated network (STIN), where a satellite multicast communication network shares the millimeter wave spectrum with a cellular network employing NOMA technology. By assuming that the satellite uses multibeam antenna array and the base station employs uniform planar array, we first formulate a constrained optimization problem to maximize the sum rate of the STIN while satisfying the constraint of per-antenna transmit power and quality-of-service requirements of both satellite and cellular users. Since the formulated optimization problem is NP-hard and mathematically intractable, we develop a novel user pairing scheme so that more than two users can be grouped in a cluster to exploit the NOMA technique. Based on the user clustering, we further propose to transform the non-convex problem into an equivalent convex one, and present an iterative penalty function-based beamforming (BF) scheme to obtain the BF weight vectors and power coefficients with fast convergence. Simulation results confirm the effectiveness and superiority of the proposed approach in comparison with the existing works.

Secure Transmission in Cognitive Satellite Terrestrial Networks

Abstract: This paper investigates the physical layer security of a satellite network, whose downlink spectral resource is shared with a terrestrial cellular network. We propose to employ a multi-antenna base station (BS) as a source of green interference to enhance secure transmission in the satellite network. By taking the mutual interference between these two networks into account, we first formulate a constrained optimization problem to maximize the instantaneous rate of the terrestrial user while satisfying the interference probability constraint of the satellite user. Then, with the assumption that imperfect channel state information (CSI) and statistical CSI of the link between the BS and satellite user are available at the BS, we present two beamforming (BF) schemes, namely, hybrid zero-forcing and partial zero-forcing to solve the optimization problem and obtain the BF weight vectors in a closed form. Moreover, we analyze the secrecy performance of primary satellite network by considering two practical scenarios, namely: Scenario I, the eavesdroppers CSI is unknown at the satellite and Scenario II, the eavesdroppers CSI is known at the satellite. Specifically, we derive the analytical expressions for the secrecy outage probability for Scenario I and the average secrecy rate for Scenario II. Finally, numerical results are provided to confirm the superiority of the proposed BF schemes and the validity of the performance analysis, as well as demonstrate the impacts of various parameters on the secrecy performance of the satellite network.

News cues: Information scent and cognitive heuristics

Abstract: Google News and other newsbots have automated the process of news selection, providing Internet users with a virtually limitless array of news and public information dynamically culled from thousands of news organizations all over the world. In order to help users cope with the resultant overload of information, news leads are typically accompanied by three cues: (a) the name of the primary source from which the headline and lead were borrowed, (b) the time elapsed since the story broke, and (c) the number of related articles written about this story by other news organizations tracked by the newsbot. This article investigates the psychological significance of these cues by positing that the information scent transmitted by each cue triggers a distinct heuristic (mental shortcut) that tends to influence online users' perceptions of a given news item, with implications for their assessment of the item's relevance to their information needs and interests. A large 2 × 3 × 6 within-subjects online experiment (N = 523) systematically varied two levels of the source credibility cue, three levels of the upload recency cue and six levels of the number-of-related-articles cue in an effort to investigate their effects upon perceived message credibility, newsworthiness, and likelihood of clicking on the news lead. Results showed evidence for source primacy effect, and some indication of a cue-cumulation effect when source credibility is low. Findings are discussed in the context of machine and bandwagon heuristics.

Dynamic Computation Offloading for Mobile Cloud Computing: A Stochastic Game-Theoretic Approach

Abstract: Driven by the growing popularity of mobile applications, mobile cloud computing has been envisioned as a promising approach to enhance computation capability of mobile devices and reduce the energy consumptions. In this paper, we investigate the problem of multi-user computation offloading for mobile cloud computing under dynamic environment, wherein mobile users become active or inactive dynamically, and the wireless channels for mobile users to offload computation vary randomly. As mobile users are self-interested and selfish in offloading computation tasks to the mobile cloud, we formulate the mobile users’ offloading decision process under dynamic environment as a stochastic game. We prove that the formulated stochastic game is equivalent to a weighted potential game which has at least one Nash Equilibrium (NE). We quantify the efficiency of the NE, and further propose a multi-agent stochastic learning algorithm to reach the NE with a guaranteed convergence rate (which is also analytically derived). Finally, we conduct simulations to validate the effectiveness of the proposed algorithm and evaluate its performance under dynamic environment.

Intelligent Reflecting Surface Assisted Secrecy Communication: Is Artificial Noise Helpful or Not?

Abstract: In this letter, we investigate whether the use of artificial noise (AN) is helpful to enhance the secrecy rate of an intelligent reflecting surface (IRS) assisted wireless communication system. Specifically, an IRS is deployed nearby a single-antenna receiver to assist in the transmission from a multi-antenna transmitter, in the presence of multiple single-antenna eavesdroppers. Aiming to maximize the achievable secrecy rate, a design problem for jointly optimizing transmit beamforming with AN or jamming and IRS reflect beamforming is formulated, which is however difficult to solve due to its non-convexity and coupled variables. We thus propose an efficient algorithm based on alternating optimization to solve the problem sub-optimally. Simulation results show that incorporating AN in transmit beamforming is beneficial under the new setup with IRS reflect beamforming. In particular, it is unveiled that the IRS-aided design without AN even performs worse than the AN-aided design without IRS as the number of eavesdroppers near the IRS increases.