This paper presents a multiple-input multiple-output (MIMO) antenna system for millimeter-wave 5G wireless communication services. The proposed MIMO configuration is composed of four antenna elements, where each antenna possesses an HP-shaped configuration that features simple configuration and excellent performance. The proposed MIMO design can operate at a very wideband of 36.83–40.0 GHz (measured). Furthermore, the proposed MIMO antenna attains a peak gain of 6.5 dB with a maximum element-isolation of −45 dB. Apart from this, the MIMO performance metrics such as envelope correlation coefficient (ECC), diversity gain, and channel capacity (CCL) are analyzed, which demonstrate good characteristics across the operating band. The proposed antenna radiates efficiently with a radiation efficiency of above 80% at the desired frequency band which makes it a potential contender for the upcoming communication applications. The proposed design simulations were performed in the computer simulation technology (CST) software, and measured results reveal good agreement with the simulated one.

Because of their simple design structure, end-to-end deep learning (E2E-DL) models have gained a lot of attention for speech enhancement. A number of DL models have achieved excellent results in eliminating the background noise and enhancing the quality as well as the intelligibility of noisy speech. Designing resource-efficient and compact models during real-time processing is still a key challenge. In order to enhance the accomplishment of E2E models, the sequential and local characteristics of speech signal should be efficiently taken into consideration while modeling. In this paper, we present resource-efficient and compact neural models for end-to-end noise-robust waveform-based speech enhancement. Combining the Convolutional Encode-Decoder (CED) and Recurrent Neural Networks (RNNs) in the Convolutional Recurrent Network (CRN) framework, we have aimed at different speech enhancement systems. Different noise types and speakers are used to train and test the proposed models. With LibriSpeech and the DEMAND dataset, the experiments show that the proposed models lead to improved quality and intelligibility with fewer trainable parameters, notably reduced model complexity, and inference time than existing recurrent and convolutional models. The quality and intelligibility are improved by 31.61% and 17.18% over the noisy speech. We further performed cross corpus analysis to demonstrate the generalization of the proposed E2E SE models across different speech datasets.

Protic ionic liquids (PILs) being intrinsic proton conducting ionic species are considered as potential green electrolytes for study of electrocatalytic reactions and for fabrication of IL-based fuel cells (FCs) and batteries. We have prepared a sulfate anion based protic ionic liquid (PIL), triethylammonium sulfate (TEAS) through a reaction involving transfer of proton from H ₂ SO ₄ to triethylamine (TEA). ¹ H NMR and FT-IR spectroscopic techniques were employed for confirmation of the synthesis of TEAS and water content of the PIL was quantified using coulometric Karl–Fischer (KF) titration. ¹ H NMR and FT-IR analysis confirm the synthesis of the PILs and KF-titration analysis shows that TEAS contains 1.43 w/w % water. Electrical conductivity of TEAS was determined at different temperatures showing that the PIL has excellent ionic conductivity that enhances with rise in temperature of the medium. The temperature dependence of the conductivity of the PIL follows the Arrhenius equation as the log σ versus 1/ T plot is linear. The electrochemical windows (EWs) of the electrolyte were found using cyclic voltammetry at Pt and Au working electrodes and found to decrease with increase in temperature of the medium. The data revealed that the surfaces of the electrodes are covered with oxide layers due to oxidation of trace water (1.43 w/w %) present in the PIL. The oxide layers growth increase and their onset potential moves to less positive values as the temperature of the PILs is increased. The data was compared with the literature and would be helpful in understanding of the surface electrochemistry in this neoteric medium for being used as potential electrolyte in industry for various electrochemical applications.

Data caching has emerged as a promising technique to handle growing data traffic and backhaul congestion of wireless networks. However, there is a concern regarding how and where to place contents to optimize data access by the users. Data caching can be exploited close to users by deploying cache entities at Small Base Stations (SBSs). In this approach, SBSs cache contents through the core network during off-peak traffic hours. Then, SBSs provide cached contents to content-demanding users during peak traffic hours with low latency. In this paper, we exploit the potential of data caching at the SBS level to minimize data access delay. We propose an intelligence-based data caching mechanism inspired by an artificial intelligence approach known as Reinforcement Learning (RL). Our proposed RL-based data caching mechanism is adaptive to dynamic learning and tracks network states to capture users’ diverse and varying data demands. Our proposed approach optimizes data caching at the SBS level by observing users’ data demands and locations to efficiently utilize the limited cache resources of SBS. Extensive simulations are performed to evaluate the performance of proposed caching mechanism based on various factors such as caching capacity, data library size, etc. The obtained results demonstrate that our proposed caching mechanism achieves 4% performance gain in terms of delay vs. contents, 3.5% performance gain in terms of delay vs. users, 2.6% performance gain in terms of delay vs. cache capacity, 18% performance gain in terms of percentage traffic offloading vs. popularity skewness ($\gamma$), and 6% performance gain in terms of backhaul saving vs. cache capacity.

User Equipment (UE) is equipped with limited cache resources that can be utilized to offload data traffic through device-to-device (D2D) communications. Data caching at a UE level has the potential to significantly alleviate data traffic burden from the backhaul link. Moreover, in wireless networks, users exhibit mobility that poses serious challenges to successful data transmission via D2D communications due to intermittent connectivity among users. Users’ mobility can be exploited to efficiently cache contents by observing connectivity patterns among users. Therefore, it is crucial to develop an efficient data caching mechanism for UE while taking into account users’ mobility patterns. In this work, we propose a mobility-aware data caching approach to enhance data offloading via D2D communication. First, we model users’ connectivity patterns. Then, contents are cached in UE’ cache resources based on users’ data preferences. In addition, we also take into account signal-to-interference and noise ratio (SINR) requirements of the users. Hence, our proposed caching mechanism exploits connectivity patterns of users to perform data placement based on users’ own demands and neighboring users to enhance data offloading via cache resources. We performed extensive simulations to investigate the performance of our proposed mobility-aware data caching mechanism. The performance of our proposed caching mechanism is compared to most deployed data caching mechanisms, while taking into account the dynamic nature of the wireless channel and the interference experienced by the users. From the obtained results, it is evident that our proposed approach achieves 14%, 16%, and 11% higher data offloading gain than the least frequently used, the Zipf-based probabilistic, and the random caching schemes in case of an increasing number of users, cache capacity, and number of contents, respectively. Moreover, we also analyzed cache hit rates, and our proposed scheme achieves 8% and 5% higher cache hit rate than the least frequently used, the Zipf-based probabilistic, and the random caching schemes in case of an increasing number of contents and cache capacity, respectively. Hence, our proposed caching mechanism brings significant improvement in data sharing via D2D communications.

The limited shelf life of carbon prepreg waste (CPW) from component manufacturing restricts its use as a composite reinforcement fibre on its own. However, CPW can be recycled with glass fibre (GF) reinforcement to develop a unique remediate material. Therefore, this study fabricated (1) a glass fibre-carbon prepreg waste reinforced polymer hybrid composite (GF-CPW-PP), (2) a polypropylene composite (PP), (3) a carbon prepreg waste reinforced composite (CPW-PP), and (4) a glass fibre reinforced composite (GF-PP) and reported their degradation and residual tension properties after immersion in water. The polymer hybrid composites were fabricated via extrusion technique with minimum reinforce glass-carbon prepreg waste content of 10 wt%. The immersion test was conducted at room temperature using distilled water. Moisture content and diffusion coefficient (DC) were determined based on water adsorption values recorded at 24-h intervals over a one-week period. The results indicated that GF-PP reinforced composites retained the most moisture post-168 h of immersion. However, hardness and tensile strength were found to decrease with increased water adsorption. Tensile strength was found to be compromised since pores produced during hydrolysis reduced interfacial bonding between glass fibre and prepreg carbon reinforcements and the PP matrix.