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Development of Multilayer Partially Reflective Surfaces for Highly Directive Cavity Antennas: A Study
This paper proposes a novel triple-layer partially reflecting surface (PRS) for designing a highly directive antenna. The proposed PRS design has 54% improvement in impedance bandwidth with existing design. The design has multiple cavities, optimized for improved gain and bandwidth performance. The PRS arrays are printed on the dielectric substrate, placed above the ground plane at a height of approximately half wavelength. The reference square microstrip patch antenna operating at a frequency of 5.8 GHz with a gain of 3.77 dBi is enhanced to 13.54 dBi. The measured S11 of the fabricated prototype is −22.12 dB with a VSWR of 1.17 : 1. Measured 3 dB gain bandwidth of the antenna is 390 MHz which is an improvement of 50% compared with the reference antenna. This highly directive antenna is suitable for WiMAX wireless application.
Design of Wavelet-Encoded Symbol Constellations for Cyclostationary Spectrum Sensing
In mobile communication systems, the signals propagate through multipath over time-varying channels, which are subject to distortion caused by fading and Doppler shifts. In order to minimize such distortions, coding techniques and transmission diversity can be employed, e.g., wavelet coding. In this work, the wavelet coding is investigated in scenarios of cognitive radio systems with dynamic spectrum access. Cognitive radio systems with dynamic spectrum access should be able to sense unoccupied frequency bands for opportunistic transmissions, as well as detect the presence of primary users when they occupy their licensed spectrum. Therefore, an essential element for the accurate operation of cognitive radio systems encoded by wavelet coding is the ability to sense the signals encoded by this technique. It is effectively demonstrated that the possibility of sensing such signals is associated with a suitable design of the signal constellation used in the modulation of the coded symbols. The constellation design of these is performed via genetic algorithms using a multiobjective optimization approach. The developed system is evaluated according to the robustness to time-varying flat fading through a bit error probability (BER) versus Eb/N0 analysis. The spectral sensing ability is also addressed employing the cyclostationary analysis. The results denote the feasibility of using wavelet coding in radio scenarios with dynamic spectrum access, with good performance in terms of BER and signal detection rates.
Security and Privacy in Vehicular Ad Hoc Network and Vehicle Cloud Computing: A Survey
Vehicular networks are becoming a prominent research field in the intelligent transportation system (ITS) due to the nature and characteristics of providing high-level road safety and optimized traffic management. Vehicles are equipped with the heavy communication equipment which requires a high power supply, on-board computing device, and data storage devices. Many wireless communication technologies are deployed to maintain and enhance the traffic management system. The ITS is capable of providing services to the traffic authorities and precautionary measures to the drivers and passengers. Several methods have been proposed for discussing the security and privacy issues for the vehicular ad hoc networks (VANETs) and vehicular cloud computing (VCC). They receive a great deal of attention from researchers around the world since they are new technologies, and they can improve road safety and enhance traffic flow by utilizing the vehicles resources and communication system. Firstly, the VANETs are presented, including the basic overview, characteristics, threats, and attacks. The location privacy methodologies are elaborated, which can protect the confidential information of the vehicle, such as the location detail and driver information. Secondly, the trust management models in the VANETs are comprehensively discussed, followed by the comparison of the cryptography and trust models in terms of different kinds of attacks. Then, the simulation tools and applications of the VANETs are discussed, and the evolution is presented from the VANETs to VCC in the vehicular network. Thirdly, the VCC is discussed from its architecture and the security and privacy issues. Finally, several research challenges on the VANETs and VCC are presented. In sum, this survey comprehensively covers the location privacy and trust management models of the VANETs and discusses the security and privacy issues in the VCC, which fills the gap of existing surveys. Also, it indicates the research challenges in the VANETs and VCC.
Three-Dimensional Coprime Array for Massive MIMO: Array Configuration Design and 2D DOA Estimation
In massive multiple-input multiple-output (MIMO) systems, it is critical to obtain the accurate direction of arrival (DOA) estimation. Conventional three-dimensional array mainly focuses on the uniform array. Due to the dense arrangement of the sensors, the array aperture is limited and severe mutual coupling effects arise. In this paper, a coprime cubic array (CCA) configuration design is presented, which is composed of two uniform cubic subarrays and can extend the interelement spacing with a selection of three pairs of coprime integers. Compared with uniform cubic array (UCA), CCA achieves the larger array aperture and less MC effects. And the analytical expression of Cramer–Rao Bound (CRB) for CCA is derived which verifies that the proposed CCA geometry outperforms the conventional UCA in two-dimensional (2D) DOA estimation performance in massive MIMO systems. Meanwhile, we propose a computationally efficient 2D DOA estimation algorithm with high accuracy for CCA. Specifically, we utilize array mapping to extract two uniform arrays from the nonuniform array by exploiting the relation derived from the signal subspace and the two directional matrices. Then, we operate a reduced dimension process on the uniform arrays and convert the 2D spectrum peak searching (SPS) problem into one-dimensional (1D) one, which significantly reduces the computational complexity. In addition, we employ the polynomial root finding technique with a lower complexity instead of 1D SPS to further relieve the computational complexity. Simultaneously, with coprime property, the phase ambiguity problem is solved, which results from the large interelement spacing. Numerical simulation results demonstrate that the proposed algorithm is very computationally efficient without degradation of DOA estimation performance.
Measurement of Objective Video Quality in Social Cloud Based on Reference Metric
This paper explores the objective of the present video quality analysis (VQA) and measures the full reference metrics keeping in view the quality degradation. During the research work, we conduct experiments on different social clouds (SCs) and low-quality videos. Selected videos are uploaded to SC to assess differences in video service and quality. WeChat shows that the average of all videos (Avg = 100), peak signal-to-noise ratio (PSNR), has no impact on other indicators. Therefore, we believe that WeChat provides the best video quality and multimedia services to their users to meet Quality of Service (QoS)/Quality of Experience (QoE).
Software-Defined Multilayered Admission Control for Quality of Service Assurance in Mobile Ad-hoc Networks
Mobile Ad-hoc Network has emerged as a key technology for next-generation networks. Though its rapid growth inspires numerous applications, it is difficult to assure Quality of Service because of its immense scaling caused due to node’s mobility, fading radio signals, and unreliable nature of the wireless channel. To efficiently utilize network resources and accomplish guaranteed Quality of Service, a novel Software-Defined Multilayered Admission Control model that embeds an intelligent Neurofuzzy Inference-based Admission Control service engine is proposed in this paper. Each node makes use of the Neurofuzzy Inference-based Admission Control service to learn, manage, prioritize, and admit data traffic according to user requirement. The service engine exploits fuzzy inference-based admission control process to assess node’s current status using Quality of Service parameters, namely, bandwidth, queue load, and Received Signal Strength Indicator to evaluate the prediction index. The prediction index not only helps in determining the strongly connected neighbors during reliable path selection process but also solely decides whether the admission control session can be admitted or rejected. Moreover, the Neuro-Multilayered Learning process of the service engine helps to self-organize and make the complete network intelligent for instantaneous decision making. The proposed mechanism not only improves the session admission between nodes but also reduces the packet drops assuring successful session completion. Performance analysis using the simulation model proves that the proposed system shows promising gains with assured throughput and low end-to-end delay and has the potential to be applied in real-world scenarios.