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.

Secrecy Outage Analysis of Buffer-Aided Cooperative MIMO Relaying Systems

Abstract: This paper investigates the secrecy outage performance of buffer-aided multirelay multiple-input multiple-output cooperative systems in the presence of a passive eavesdropper. Due to the unavailability of the channel state information of eavesdropper's channel, a buffer-aided joint transmit antenna and relay selection scheme based on the main channel is proposed to enhance the secrecy performance. Specifically, we model the evolution of the relay buffers as a Markov chain and derive new exact and asymptotic closed-form expressions for the secrecy outage probability, which provides an efficient way to assess the effect of system parameters on the secrecy outage probability. Moreover, simple asymptotic results are further exploited under two special scenarios, i.e., $L \rightarrow \infty$ and $L ot\rightarrow \infty$ (where $L$ denotes the size of the relay buffers), for characterizing the achievable secrecy diversity gain, the secrecy coding gain, and the secrecy diversity-multiplexing tradeoff. Our results reveal that: 1) a secrecy diversity gain of $N_RM{\rm min}(N_S,N_D)$ is achieved when $L ot\rightarrow \infty$ , however, when $L \rightarrow \infty$ , the secrecy diversity gain increases to $N_RM(N_S+N_D)$ , where $N_S$ , $N_R$ , and $N_D$ represent the number of antennas at the source, each of $M$ relays and the destination, respectively. 2) The eavesdropper's channel does not affect the secrecy diversity gain but only the secrecy coding gain in both the two scenarios.

Distributed Demand-Aware Channel-Slot Selection for Multi-UAV Networks: A Game-Theoretic Learning Approach

Abstract: Over the past decades, the unmanned aerial vehicle (UAV) has received unprecedented surge of scientific and military interest worldwide. This paper investigates the problem of opportunistic spectrum access for multi-UAV networks from a game-theoretic perspective. Due to the topology of the multi-UAV networks, the interference may be classified into two parts, i.e., the intra-cluster and the inter-cluster interference. Moreover, since the UAVs in the network have different tasks, the communication demand of each UAV should be taken into account. First, we formulate the demand-aware joint channel-slot selection problem as a weighted interference mitigation game, and then, design the utility function considering features of multi-UAV network, e.g., some rewards due to the channel and slots selection. We prove that the formulated game is an exact potential game with at least one pure-strategy Nash equilibrium. Next, we apply the distributed log-linear algorithm to achieve the desired optimization and overcome the constraint of dynamic communication demand of each UAV. To speed up the convergence, we also propose a low-complexity and realistic channel and slot initialization scheme for UAVs. Finally, the simulation results validate the effectiveness of the formulated game.

CARM: Crowd-Sensing Accurate Outdoor RSS Maps with Error-Prone Smartphone Measurements

Abstract: Received Signal Strength (RSS) maps provide fundamental information for mobile users, aiding the development of conflict graph and improving communication quality to cope with the complex and unstable wireless channels. In this paper, we present CARM: a scheme that exploits crowd-sensing to construct outdoor RSS maps using smartphone measurements. An alternative yet impractical approach in literature is to appeal to professionals with customized devices. Our work distinguishes itself from previous studies by supporting off-the-shelf smartphone devices, and more importantly, by mitigating the error-prone nature and inaccuracies of these devices to build RSS maps through crowd-sensing. The main challenges are that, we need to calibrate error-prone smartphone measurements with “inaccurate” and “incomplete” data. To address these challenges, we build the measurement error model of smartphone based on the experimental observations and analyses. Moreover, we propose an iterative method based on Davidon-Fletcher-Powell (DFP) algorithm, to estimate the parameters for the error models of each smartphone and the signal propagation models of each AP simultaneously. The key intuition is that, the calibrated measurements based on the error model are constrained by the physics of the signal propagation model. Finally, a model-driven RSS map construction scheme is built upon these two models with these estimated parameters. The theoretical analyses prove the optimality and convergence of this iterative method. Also, the crowd-sensing experiments show that, CARM can achieve an accurate RSS map, decreasing the average error from 19.8 to 8.5 dBm.

Weak and Occluded Vehicle Detection in Complex Infrared Environment Based on Improved YOLOv4

Abstract: Infrared small target detection is still a challenge in the field of object detection. At present, although there are many related research achievements, it surely needs further improvement. This paper introduced a new application of severely occluded vehicle detection in the complex wild background of weak infrared camera aerial images, in which more than 50% area of the vehicles are occluded. We used YOLOv4 as the detection model. By applying secondary transfer learning from visible dataset to infrared dataset, the model could gain a good average precision (AP). Firstly, we trained the model in the UCAS_AOD visible dataset, then, we transferred it to the VIVID visible dataset, finally we transferred the model to the VIVID infrared dataset for a second training. Meanwhile, added the hard negative example mining block to the YOLOv4 model, which could depress the disturbance of complex background thus further decrease the false detecting rate. Through experiments the average precision improved from90.34% to 91.92%, the F1 score improved from 87.5% to 87.98%, which demonstrated that the proposed algorithm generated satisfactory and competitive vehicle detection results.