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The 3GPP release for 5G (R15) assigns each User Equipment (UE) a radio beam by employing Massive Multi-User MU-Multiple-Input-Multiple-Output (MIMO) technology. Each beam carries, at the downlink, a data rate according to the modulation and coding scheme (MCS) assigned by the base station (BS). For the limited existence of active UEs and during vacant traffic or standby UEs, the assigned beams will be transmitted, but not to any UE. This paper proposes a new scheme that consolidates vacant beams of inactive UEs, to the adjacent beam of the active UE or UE at the cell edge to duplicate the bandwidth of the new beam. The proposed scheme increases the level of desired modulation and coding scheme (MCS) to a higher scheme and hence enhances the spectral efficiency (SE) of the 5G mobile networks. Specifically, the BS consolidates (combines) multiple radio beams along with the assigned beam during vacant traffic. More than two beams are consolidated in particular to the active UE to increase the bit rate by assigning higher MCS. The simulation evaluation depicted that the performance of beams consolidation provides a gain of 3.5 dB above than the state before beams consolidation. Moreover, more than 40 % improvement in UE throughput is achieved.

One of the most serious problems facing the community around the world is car accidents. These accidents occur mainly due to the high-speed of vehicles. Thus, the paper aims to capture, track, and control high-speed vehicles using LTE-A mobile networks to avoid high-speed situations as well as decrease the number of accidents. The paper assumes that all vehicle drivers are now days carrying their mobiles that can be considered as mobile network user equipment (UE). This paper presents an innovative tracking and controlling high-speed vehicles in the LTE-A system that taking the advantages of Channel Quality Indicator (CQI) value mapped to the UE speed. The method can be accomplished by uploading the CQI index to the base station (BS), at the uplink, then the evolved node base station (eNB) sends an extra warning message at the downlink to initiate the radio frequency identifier (RFID) component fixed on the vehicle. The proposed scheme design assumes that the LTE networks have the all traffic speed for the covered area and to be activated when the speed is beyond the maximum speed. In that case, the RFID is activated and an alarm is switched on. Under now response, the RFID will activate the vehicle's traction control (TC), Engine Control Unit (ECU) and automatic brake system (ABS) to decrease the speed gradually. The proposed scheme was simulated using the system level-simulator (SLS) and the performance is depicted. The evaluations show that the CQI values are decreased meaningfully to 2 when the UE movement in the high-way increases to 150 km/h. Consequently, with the obtainability of CQI values at the LTE-A system, an immediate activity is completed to control the vehicle speed and warn the driver.

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The world witnessed a pandemic that needs to be limited. COVID-19 is a disease that spreads among people when an infected person is in close contact with another. To decrease the virus spreading, World Health Organization (WHO) imposed precautionary measures and suggested some rules to be followed such as social distancing and quarantining the infected people. We propose a model, using D2D and IoT technology, for tracking infected persons with COVID-19 and its proximity. If a person (mobile device) gets close to an infected person, he will also get infected, so by continuous moving, the infection will be transmitted. Thus, identifying the infected persons and their contacts will limit the spread of the disease. In each scenario, it is possible to distinguish the number of infected people and know from whom they are infected, and the location of the infection. The simulation shows the tracking of a mobile device when proximate infected person at a distance of 3 meters. As a result, our proposed D2D model is effective, especially in the scenario which found the infected person with COVID-19, tracks them, determines minimum distances, and recognizes the source of the infection. Thus, the model can limit the rapid spread of COVID-19 as it determines the 3meters distance from infected person and send precaution messages to the network.

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