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    Dual Band and Dual Diversity Four-Element MIMO Dipole for 5G Handsets
    (Mdpi, 2021) Jamshed, Muhammad Ali; Ur-Rehman, Masood; Frnda, Jaroslav; Althuwayb, Ayman A.; Nauman, Ali; Cengiz, Korhan
    The increasing popularity of using wireless devices to handle routine tasks has increased the demand for incorporating multiple-input-multiple-output (MIMO) technology to utilize limited bandwidth efficiently. The presence of comparatively large space at the base station (BS) makes it straightforward to exploit the MIMO technology's useful properties. From a mobile handset point of view, and limited space at the mobile handset, complex procedures are required to increase the number of active antenna elements. In this paper, to address such type of issues, a four-element MIMO dual band, dual diversity, dipole antenna has been proposed for 5G-enabled handsets. The proposed antenna design relies on space diversity as well as pattern diversity to provide an acceptable MIMO performance. The proposed dipole antenna simultaneously operates at 3.6 and 4.7 sub-6 GHz bands. The usefulness of the proposed 4x4 MIMO dipole antenna has been verified by comparing the simulated and measured results using a fabricated version of the proposed antenna. A specific absorption rate (SAR) analysis has been carried out using CST Voxel (a heterogeneous biological human head) model, which shows maximum SAR value for 10 g of head tissue is well below the permitted value of 2.0 W/kg. The total efficiency of each antenna element in this structure is -2.88, -3.12, -1.92 and -2.45 dB at 3.6 GHz, while at 4.7 GHz are -1.61, -2.19, -1.72 and -1.18 dB respectively. The isolation, envelope correlation coefficient (ECC) between the adjacent ports and the loss in capacity is below the standard margin, making the structure appropriate for MIMO applications. The effect of handgrip and the housing box on the total antenna efficiency is analyzed, and only 5% variation is observed, which results from careful placement of antenna elements.
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    Reinforcement learning-enabled Intelligent Device-to-Device (I-D2D) communication in Narrowband Internet of Things (NB-IoT)
    (Elsevier, 2021) Nauman, Ali; Jamshed, Muhammad Ali; Ali, Rashid; Cengiz, Korhan; Zulqarnain; Kim, Sung Won
    The 5th Generation (5G) and Beyond 5G (B5G) are expected to be the enabling technologies for Internet-of-Everything (IoE). The quality-of-service (QoS) for IoE in the context of uplink data delivery of the content is of prime importance. The 3rd Generation Partnership Project (3GPP) standardizes the Narrowband Internet-of-Things (NB-IoT) in 5G, which is Low Power Wide Area (LPWA) technology to enhance the coverage and to optimize the power consumption for the IoT devices. Repetitions of control and data signals between NB-IoT User Equipment (UE) and the evolved NodeB/Base Station (eNB/BS), is one of the most prominent characteristics in NB-IoT. These repetitions ensure high reliability in the context of data delivery of time-sensitive applications, e.g., healthcare applications. However, these repetitions degrade the performance of the resource-constrained IoT network in terms of energy consumption. Device-to-Device (D2D) communication standardized in Long Term Evolution-Advanced (LTE-A) offers a key solution for NB-IoT UE to transmit in two hops route instead of direct uplink, which augments the efficiency of the system. In an effort to improve the data packet delivery, this study investigates D2D communication for NB-IoT delay-sensitive applications, such as healthcare-IoT services. This study formulates the selection of D2D communication relay as Multi-Armed Bandit (MAB) problem and incorporates Upper Confidence Bound (UCB) based Reinforcement Learning (RL) to solve MAB problem. The proposed Intelligent-D2D (I-D2D) communication methodology selects the optimum relay with a maximum Packet Delivery Ratio (PDR) with minimum End-to-End Delay (EED), which ultimately augments energy efficiency.