••01 Jan 2022
TL;DR: In this article, the BER performance and outage capacity of MIMO and massive mIMO on Rayleigh, Rician and Nakagami channels are compared. And the performance analysis between MIMI and Massive MIMOs is analyzed with different channels corresponding to the output and result obtained.
Abstract: Multiple Input Multiple Output (MIMO) provides an environment in wireless communication that offers simple antennas and resource allocation is simplified utilizing Time Division multiplexing. MIMO overcomes bandwidth restrictions with its channel state information and multiple antennas. Massive MIMO uses large-scale antenna systems and are capable of improving the channel capability. Extra antennas improve the throughput and energy efficiency. The performance analysis between MIMO and Massive MIMO is analyzed with different channels corresponding to the output and result obtained. In this paper, the BER performance and outage capacity of MIMO and Massive MIMO on Rayleigh, Rician and Nakagami channels are compared.
TL;DR: In this paper, an antenna selection algorithm based on maximum flow minimum cut theorem is proposed which captures the optimal antennas based on the aggregate capacity of the possible antenna combinations, and the remaining capacity present in antennas to compute the maximum flow in a network.
Abstract: The performance of Massive MIMO can be enhanced by utilizing a large number of antennas than used and signifying its enormous capabilities in upgrading spectral efficiency. Antenna Selection is a low-priced complexity that decreases the number of radiofrequency chains with the contemplation of augmenting channel capacity. In this paper, an Augment Antenna Selection algorithm based on maximum flow minimum cut theorem is proposed which captures the optimal antennas based on the aggregate capacity of the possible antenna combinations. The first step in this algorithm proposes a subset of antennas to compute the augment paths and the second step selection considers the remaining capacity present in antennas to compute the maximum flow in a network. Thus, this algorithm that selects optimal antennas with better channel conditions aims at improving spectrum and energy efficiency. Simulation results show the outage capacity and the performance of BER with 64*64 and 128*128 Massive MIMO versus SNR has been analyzed.
TL;DR: In this paper , the linear precoding schemes were proposed to mitigate interference between the base stations (intercell), which are categorized into linear and non-linear; these schemes are grounded into three types, namely, Zero Forcing (ZF), Block Diagonalization (BD), and Signal Leakage Noise Ratio (SLNR).
Abstract: In Mobile Communication Systems, inter-cell interference becomes one of the challenges that degrade the system’s performance, especially in the region with massive mobile users. The linear precoding schemes were proposed to mitigate interferences between the base stations (inter-cell). These schemes are categorized into linear and non-linear; this study focused on linear precoding schemes, which are grounded into three types, namely Zero Forcing (ZF), Block Diagonalization (BD), and Signal Leakage Noise Ratio (SLNR). The study included the Cooperative Multi-cell Multi Input Multi Output (MIMO) System, whereby each Base Station serves more than one mobile station and all Base Stations on the system are assisted by each other by shared the Channel State Information (CSI). Based on the Multi-Cell Multiuser MIMO system, each Base Station on the cell is intended to maximize the data transmission rate by its mobile users by increasing the Signal Interference to Noise Ratio after the interference has been mitigated due to the usefully of linear precoding schemes on the transmitter. Moreover, these schemes used different approaches to mitigate interference. This study mainly concentrates on evaluating the performance of these schemes through the channel distribution models such as Ray-leigh and Rician included in the presence of noise errors. The results show that the SLNR scheme outperforms ZF and BD schemes overall scenario. This implied that when the value of SNR increased the performance of SLNR increased by 21.4% and 45.7% for ZF and BD respectively.