In this paper, a monitoring and controlling process of the mobile base station shelter has been implemented. We have proposed a model that is based on a firebase cloud service and the principle of the internet of things (IoT) to carry out the process of automation. In this model, we have used Raspberry Pi 4 as the main microcontroller of our system that has interacted with a DHT11 Humidity-Temperature sensor and a PIR motion sensor. It's found that the Pi4 module provides efficient analysis, low consumption of power, and effective control of the operation. It turns ON/OFF the electrical appliances automatically inside the shelter. The main advantage of our proposed model is to maintain the temperature and humidity degrees inside the shelter within the required range of operation. Another important advantage is to diminish the tall human exertion level behind the monitoring process throughout the day. The model has been tested through a localhost server via an HTML page. The last one was created with the assistance of HTML and CSS languages to be used as a local user interface. Moreover, the Raspberry Pi 4 was programmed by Python Language to catch up on the reading of the sensors, processes the data, and sends it to the cloud service. Finally, those data will be shown in real-time to the authenticated user on the database of the firebase cloud service.
Hardware problems are the most detrimental issues to channel estimates in wireless communication systems. Because of the enormous number of antennas at the base station (BS) in cellular massive multiple-input multiple-output (MIMO) systems and because one radio frequency (RF) chain per antenna is required, hardware impairments in such systems will be quite severe. Many research publications have used a quality-cost tradeoff to adjust for RF unit hardware issues. In this study, we have taken a different approach by reducing the error floor caused by impairments in the predicted channels. Here are two steps to remedy the problem. In phase 1, a single active user channel in a single cell was calculated statistically rather than parametrically. In phase 2, a convex optimization approach was used to regularize the estimated channel in phase 1 to reduce error and provide a robust channel estimate. The results of our proposed procedure are measured by the normalized minimum mean squared error (NMSE) versus a range from the effective signal-to-noise ratio, and it shows a significant reduction (nearly one order of magnitude) in the error floor as compared with the conventional one, especially at high signal-to-noise ratio (SNR) in the range of (20 dB-30 dB). Simulation results were extracted in MATLAB R2020a.
<span lang="EN-US">Motivated by the fact that the complexity of the computations is one of the main challenges in large multiple input multiple output systems, known as massive multiple-input multiple-output (MIMO) systems, this article proposes a low-complex minimum mean squared error (MMSE) Bayesian channel estimator for uplink channels of such systems. First, we have discussed the necessity of the covariance information for the MMSE estimator and how their imperfection knowledge can affect its accuracy. Then, two reduction phases in dimension and floating-point operations have been suggested to reduce its complexity: in phase 1, eigenstructure reduction for channel covariance matrices is implemented based on some truncation rules, while in phase 2, arithmetic operations reduction for matrix multiplications in the MMSE equation is followed. The proposed procedure has significantly reduced the complexity of the MMSE estimator to the first order O(M), which is less than that required for the conventional MMSE with O(M<sup>3</sup>) in terms of matrix dimension. It has been shown that the estimated channels using our proposed procedure are asymptotically aligned and serve the same quality as the full-rank estimated channels. Our results are validated by averaging the normalized mean squared error (NMSE) over a length of 500 sample realizations through a Monte Carlo simulation using MATLAB R2020a.</span>
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