Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    A 6 GHz Integrated High-Efficiency Class-F−1 Power Amplifier in 65 nm CMOS Achieving 47.8% Peak PAE
    (MDPI (Basel, Switzerland), 2021-10-09) Ali SMA; Hasan SMR; Ebrahimi A
    This paper reports a “single-transistor” Class-F−1 power amplifier (PA) in 65 nm CMOS, which operates at the microwave center frequency of 6 GHz. The PA is loaded with a Class-F−1 harmonic control network, employing a new “parasitic-aware” topology deduced using a novel iterative algorithm. A dual-purpose output matching network is designed, which not only serves the purpose of output impedance matching, but also reinforces the harmonic control of the Class-F−1 harmonic network. This proposed PA yields a peak power-added efficiency (PAE) of 47.8%, which is one of the highest when compared to previously reported integrated microwave/millimeter-wave PAs in CMOS and SiGe technologies. The amplifier shows a saturated output power of 14.4 dBm along with an overall gain of 13.8 dB.
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    A Novel Weighted Clustering Algorithm Supported by a Distributed Architecture for D2D Enabled Content-Centric Networks
    (MDPI (Basel, Switzerland), 25/09/2020) Aslam S; Alam F; Hasan S; Rashid M
    Next generation cellular systems need efficient content-distribution schemes. Content-sharing via Device-to-Device (D2D) clustered networks has emerged as a popular approach for alleviating the burden on the cellular network. In this article, we utilize Content-Centric Networking and Network Virtualization to propose a distributed architecture, that supports efficient content delivery. We propose to use clustering at the user level for content-distribution. A weighted multifactor clustering algorithm is proposed for grouping the D2D User Equipment (DUEs) sharing a common interest. The proposed algorithm is evaluated in terms of energy efficiency, area spectral efficiency, and throughput. The effect of the number of clusters on these performance parameters is also discussed. The proposed algorithm has been further modified to allow for a tradeoff between fairness and other performance parameters. A comprehensive simulation study demonstrates that the proposed clustering algorithm is more flexible and outperforms several classical and state-of-the-art algorithms.