Downlink Performance Enhancement of High-Velocity Users in 5G Networks by Configuring Antenna System

Abstract

A limitation of bandwidth in the wireless network and the exponential rise in the high data rate requirement prompted the development of Massive Multiple-Input-Multiple-Output (MIMO) technique in 5G. Using this method, the ever-rising data rate can be met with the increment of the number of antennas. This comes at the price of energy consumption of higher amounts, complex network setups, and maintenance. Moreover, a high-velocity user experiences unpredictable fluctuations in the channel condition that deteriorates the downlink performance. Therefore, a proper number of antenna selections is of paramount importance. This issue has been addressed using different categories of algorithms but only for static users. In this study, we proffer to implement antenna diversity in closed loop spatial multiplexing MIMO transmission scheme by operating more number of reception antennas than the number of transmission antennas for ameliorating the downlink performance of high-velocity users in case of single user MIMO technology. In general, our results can be interpreted for large scale antenna systems like Massive MIMO even though a 4 × 4 MIMO system has been executed to carry out this study here. Additionally, it shows great prospects for solving practical-life problems like low data rate and call drops during handover to be experienced by cellular users traveling through highspeed transportation systems like Dhaka Metro Rail. The cell edge users are anticipated to get benefits from this method in case of SU-MIMO technology. The proposed

Downlink Performance of Beamforming for High Mobility Users in 5G Cellular Network

“…Regarding fairness in assigning resources to users, PF scheduler triumphs over RR scheduler on every occasion which was not observed in other works where mobility was concerned. CLSM with diversity was proposed in [17] and fairness index was found to perform worse than round robin scheduler. Average UE throughput under PF and RR was less than 0.4 mbps for the velocity higher than 40 kmph for MU-MIMO scheme used in [19] whereas our proposed scheme gave us average UE throughput higher than 0.4 mbps for all combination except PF-2 × 1.…”

“…The average throughput of a UE, T avg can be calculated following the equation given below where k th user have total throughput of T k where total number of users is n [17].…”

An Enhanced Time Series Analysis to Improve the Performance of 5G Communication Systems

“…While 5G networks offer faster speeds than their predecessors, they are more susceptible to interference, which leads to a decrease in the achieved data rate. Additionally, the longer the distance a signal must travel, the more likely it is to experience attenuation, resulting in a decrease in data rate [18]. The vast number of antennas needed to operate 5G networks has greater environmental impacts than earlier generations, leading to expressed concerns from some communities [19].…”

Performance evaluation of precoding system for massive multiple-input multiple-output

“…An further problem for a high-velocity user is that the channel status fluctuates unexpectedly. This results in a decrease in the downlink performance [5]. Precoding schemes, however, require the use of linear algorithms, which are simpler to implement computationally.…”