<span>Due to the evaluation of mobile devices and applications in the current decade, a new direction for wireless networks has emerged. The general consensus about the future 5G network is that the following should be taken into account; the purpose of thousand-fold system capacity, hundredfold energy efficiency, lower latency, and smooth connectivity. The massive multiple-input multiple-output (MIMO), as well as the Millimeter wave (mm Wave) have been considered in the ultra-dense cellular network (UDN), because they are viewed as the emergent solution for the next generations of communication. This article focuses on evaluating and discussing the performance of mm Wave massive MIMO for ultra-dense network, which is one of the major technologies for the 5G wireless network. More so, the energy efficiencies of two kinds of architectures for wireless backhaul networks were investigated and compared in this article. The results of the simulation revealed some points that should be considered during the deployment of small cells in the two architectures UDN with backhaul network capacity and backhaul energy efficiency, that the changing the frequency bands in Distribution approach gives the same energy efficiency reached to 600 Mb/s at 15 nodes while the Conventional approach results reached less than 100 Mb/s at the same number of nodes.</span>

Using multiple-Input Multiple-Output (MIMO) configuration is not new in the field of wireless communication to increase the capacity of the system. This configuration is still valid to use nowadays with the modern wireless configuration such as the Fifth generation (5G). Massive MIMO is the key resource of the 5G systems due to its huge ability to increase the capacity of the network and on the other hand its ability to enhance both spectral and transmit-energy efficiency. The need for using Massive MIMO comes from the increase in using smartphones, tablets, and the rise of the Internet of Things. This increasing demand for the use of wireless applications requires networking and Internet infrastructures to meet the needs of current and future multimedia applications which massive MIMO satisfies. The key limitation of using massive MIMO is the cost of installation of these antennas and how to multiplex between them. In addition to this, the Radio Frequency (RF) links are also increased where this increase leads to high system complexity and hardware energy consumption. Because of this, reducing the required number of RF chains is essential to use by performing antenna selection which this paper aims to evaluate without significant performance loss which can be performed by employing low-resolution Analog-to-Digital Converter (ADC) to select an antenna with the best tradeoff between the additional channel gain and increase in quantization error. In this paper, Quantization-Aware Greedy Antenna Selection (QAGAS) algorithm has been proposed and compared with other antenna selection algorithms especially simple algorithms like random selection and Fast Antenna Selection (FAS) algorithm. The achieved capacity is compared with that of a very simple scheme that selects the antennas with the highest received power. The system capacity obtained from QAGAS is evaluated related to the transmit power of the Base Station (BS) and the quantization bits used in the lowresolution ADC. The simulation is also performed for a different numbers of users served by the BS and with the number of antennas at the BS. The simulation results show that the proposed algorithm indicates a potential for significant reductions of massive MIMO implementation complexity, by reducing the number of RF links and performing antenna selection using simple algorithms.

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In the current scenario, Intelligent Transportation Systems play a significant role in smart city platform. Automatic moving vehicle detection from video sequences is the core component of the automated traffic management system. Humans can easily detect and recognize objects from complex scenes in a flash. Translating that thought process to a machine, however, requires us to learn the art of object detection using computer vision algorithms. This paper solves the traffic issues of the urban areas with an intelligent automatic transportation system. This paper includes automatic vehicle counting with the help of blob analysis, background subtraction with the use of a dynamic autoregressive moving average model, identify the moving objects with the help of a Boundary block detection algorithm, and tracking the vehicle. This paper analyses the procedure of a video-based traffic congestion system and divides it into greying, binarisation, de-nosing, and moving target detection. The investigational results show that the planned system can provide useful information for traffic surveillance.

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