The Multiple-Input Multiple-Output (MIMO) system can provide improved spectral efficiency and energy performance. However, the computational demand faced by conventional signal recognition techniques has significantly increased due to the growing number of antennas and higher-order modulations. To overcome these challenges, deep learning approaches are adopted as they offer versatility, nonlinear modelling capabilities, and parallel computation efficiency for large-scale MIMO detection. Therefore, a deep network for channel estimation and massive MIMO detection is developed to reduce computational complexity issues. Initially, a channel estimation scheme is developed to enhance the channel capacity of the MIMO system. It correlates the transmitted and received signals using a confusion matrix. The proposed Modified Squid Game Optimizer (MSGO) is employed for channel state estimation. Based on the obtained channel state information, MIMO detection is performed within the communication system. Here, Multiuser Interference Cancellation (MIC)-based iterative sequential detection is initially conducted. Then, massive MIMO detection is performed using the Dilated Adaptive Recurrent Neural Network with Attention Mechanism (DARNN-AM) through learnable parameters. Moreover, to further optimize the detection performance by fine-tuning the attributes of DARNN-AM, the MSGO is utilized. The proposed network performs multi-segment mapping across multiple constellation points with different modulation schemes. The effectiveness of the proposed deep learning-based MIMO detection system is evaluated by comparing it with existing techniques and algorithms to validate its superior performance.

The proliferation of 5G technology requires the installation of new radio frequency equipment (antennas, base stations) next to the existing 4G network elements. New frequency bands have been allocated and opened for 5G services in the lower and higher GHz bands. Due to the lack of reliable and conclusive experiments and the uncontrolled spread of false information via media and social networks on the internet, a never seen before resistance has arisen from the public. Although many papers have recently been published suggesting adverse health effects, most of them were inadequately designed, conducted, and evaluated in order to be conclusive. Nevertheless, there is a need for proper experiments. This paper highlights current results, emerging critics, and presents a recommendation for further investigation.

Conventional drones are using ISM (Industrial, Scientific, and Medical) frequency bands during operation that can limit the range of the device. Limitations depend on the applied technologies in these frequency bands. With the help of cellular networks, BVLOS (Beyond Visual Line of Sight) UAV operations can be realized with increased safety. Unfortunately, base stations used in cellular networks target the lower, near-earth layers of air space. In case of drones, coverage in higher layers must be planned. Test procedures used in residential mobile network measurements record the quality parameters of the network measured on foot or by vehicle. Furthermore, the device carrying the measurement setup can cause serious interference with the measurement results. This paper presents current measurement methods and a concept for a solution to apply the measurement devices to an UAV. This new measurement method is correspondingly fit in the conception of IoD conference. We are executed the measurements by our own newly developed UAV system which can be a substitute solution of the walking measurements. Furthermore it provides opportunity to measurement some of that new layers of the air that have not had a chance before. Also, this innovation gives us the opportunity to perform measurements that have been difficult and dangerous so far, much easier and safer than before, without the need for human contact. In addition, measurements with drones have given a new perspective to the world of measurement methods.