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.

Self-Organizing Slot Access for Neighboring Cooperation in UAV Swarms

Abstract: This article focuses on the slot access problem for neighboring cooperation in unmanned aerial vehicle (UAV) swarms. To avoid the slot access process being hindered by unavailable topology information or information exchanges, a self-organized collision discovery mechanism is proposed. Each broadcaster can know whether its transmission is successful through the mechanism which provides the basic knowledge for finding the slot access strategy. Considering the distributed feature, the slot access problem is formulated as two game models. Both games are proved to have at least one Nash Equilibrum (NE) and the best NE is the optimum of the problem. Two distributed and synchronous algorithms are proposed to reach the NE. The first algorithm converges fast which satisfies the dynamic feature of UAV swarms and the second one converges to the optimum asymptotically. Moreover, to enhance the time efficiency of UAV swarms, the total number of required slots is investigated in some typical topologies and then conjectured to general ones. Simulation results verify that the proposed method is effective and the conjecture is true in almost all topologies.

QoE and Energy Aware Resource Allocation in Small Cell Networks With Power Selection, Load Management, and Channel Allocation

Abstract: With the ever-growing number of mobile users and the rapid growth of wireless data service requirement, quality of experience (QoE) has emerged as an essential indicator for users, service providers, and operators. Meanwhile, to improve coverage and serve users, a lot of small cell base stations (SBSs) must be installed, and a great amount of energy is consumed. However, as far as is known, there are few works that have studied the combinatorial problem of QoE and energy aware SBS management, which jointly implements power selection, load management (SU allocation), and channel allocation. This paper investigates the problem of QoE and energy aware SBS management, which consists of power selection, load management, and channel allocation. In this paper, we resort to cloud technologies to solve such a complicated combinatorial problem and employ an iterative approach in which two subproblems are alternatively assigned and optimized at each iteration, i.e., 1) transmission power and load joint management and 2) channel allocation. We propose a two-dimensional-action extended weakly acyclic game theoretical scheme to optimize the two subproblems distributedly and iteratively. We define a novel two-dimensional-action pure strategy Nash equilibrium (2D-NE) and prove that at least one 2D-NE exists in the proposed game. With the help of cloud, we propose two kinds of better response algorithms to achieve 2D-NE of the proposed game $G_w$ . Moreover, simulation results show that the proposed approach could achieve a good QoE-energy utility performance and a high QoE energy efficiency.

Anchor-Assisted Channel Estimation for Intelligent Reflecting Surface Aided Multiuser Communication

Abstract: Channel estimation is a practical challenge for intelligent reflecting surface (IRS) aided wireless communication. As the number of IRS reflecting elements or IRS-aided users increases, the channel training overhead becomes excessively high, which results in long delay and low throughput in data transmission. To tackle this challenge, we propose in this paper a new anchor-assisted channel estimation approach, where two anchor nodes, namely A1 and A2, are deployed near the IRS for facilitating its aided base station (BS) in acquiring the cascaded BS-IRS-user channels required for data transmission. Specifically, in the first scheme, the partial channel state information (CSI) on the element-wise channel gain square of the common BS-IRS link for all users is first obtained at the BS via the anchor-assisted training and feedback. Then, by leveraging such partial CSI, the cascaded BS-IRS-user channels are efficiently resolved at the BS with additional training by the users. While in the second scheme, the BS-IRS-A1 and A1-IRS-A2 channels are first estimated via the training by A1. Then, with additional training by A2, all users estimate their individual cascaded A2-IRS-user channels simultaneously. Based on the CSI fed back from A2 and all users, the BS resolves the cascaded BS-IRS-user channels efficiently. In both schemes, the channels among the fixed BS, IRS, and two anchors are estimated in a large timescale, which greatly reduces the real-time training overhead. Simulation results demonstrate that our proposed anchor-assisted channel estimation schemes achieve superior performance as compared to existing IRS channel estimation schemes, under various practical setups. In addition, the first proposed scheme outperforms the second one when the number of antennas at the BS is sufficiently large, and vice versa.

On the design of relay selection strategy for two-way amplify-and-forward mobile relaying

Abstract: Opportunistic relay selection (RS) is an efficient method to obtain diversity gain in analogue network coding (ANC) protocol with multiple relays. However, in networks with mobile relays, the channel state information (CSI) used in the RS procedure becomes outdated because of the time-varying nature of fading channels, which severely deteriorates the system performance. In this study, the authors study the RS strategy which aims to optimise the outage performance of the ANC protocol with multiple mobile relays. RS schemes for two different cases are designed, that is, (i) the scheme with only the outdated CSI during the RS procedure, and (ii) the scheme with both the outdated CSI and the statistical knowledge of channels during the RS procedure. The closed-form expressions of the outage probabilities as well as the asymptotic expressions are analytically derived for the proposed schemes, and moreover, the achievable diversities are analysed based on the asymptotic expressions. Simulation results are presented to evaluate the performances while validate the theoretical analyses for the proposed schemes.

SE and EE Optimization for Cognitive UAV Network Based on Location Information

Abstract: Unmanned Aerial Vehicle (UAV) aided communication has the potential to provide on-demand wireless services and improve the outdoor link throughput. Applications for UAVs are rapidly growing with the development of Internet of Things. Because of limited battery energy, the UAVs need time-limited spectrum access to complete data transmission. Hence there are two challenges for the UAV-based communication: 1) Spectrum-efficient design; 2) Energy-efficient design. In this paper, we investigate the optimization of spectrum efficiency (SE) and energy efficiency (EE) for cognitive UAV network based on location information. Because of high mobility, the cognitive radio (CR) based UAVs operate on different frequency bands that vary with time and space. Thus, one spectrum band that is available in one region may not be necessarily available in another region. Based on location information of the primary transmitter and the UAV, we propose a hybrid mode in which the sensing performance and UAV’s transmit power can be adjusted simultaneously to satisfy the outage constraint of the primary user. The multi-objective optimization theory is used to solve the tradeoff between SE and EE. The UAV’s transmit power, sensing time and sensing threshold are optimized jointly to solve the tradeoff problem. To further improve the SE and EE performance, we propose a multi-frame combined sensing scheme, in which multiple frames are bundled together. Simulation results are provided to show the SE-EE tradeoff design, to validate the effectiveness of the proposed hybrid mode, and to show the advantages of the multi-frame combined sensing scheme in EE performance.