In general, the MIMO technology is used to transfer data from a protocol such as Wi-Fi in 5G networks. This is due to the increased bandwidth and capacity. The 802.11n protocol, which, using the technology described in it, allows you to reach speeds of up to 350 megabits/second. The quality of data transmission has improved even in areas where the reception signal is low. An external access point with a MIMO antenna is a well-known one. The WiMAX network can now transmit information at speeds of up to 40 megabits/second, using MIMO. It uses MIMO technology up to 
 
 8
 ×
 8
 
 . Thanks to this, a higher transfer rate is achieved—over 35 megabits/second. In addition, reliable and high quality connection of the best quality is guaranteed. In this paper, we continue to work and enhance technical configurations of MIMO in 5G networks. The proposed model will improve spectrum performance, improve network capability, and speed up data rates. In a saturation point, the proposed method achieves that the signal to noise interference ratio is just 42.4%, the receiver signal strength is 92.94%, the downstream traffic is 48.76%, the upstream traffic is 45.62%, the bandwidth utilization is 97.43%, the speed is 95.79%, the connectivity between the access point is 90.6%, and the network security is 96.42%.

Massive MIMO Wireless Solutions in Backhaul for the 5G Networks

Interference alignment (IA) is a promising technology to effectively eliminate interference and improve the wireless communication network capacity. However, the low complexity IA solution with high degrees of freedom (DoF) for the multi-cell multi-user cellular network is still an open problem that needs to be solved. To address this problem, in this paper, the two-stage IA scheme for the multi-cell multi-user multiple-input multiple-output (MIMO) interfering broadcast channel (IFBC) model is investigated. Since a multi-cell IFBC is a complex interfering channel model, it is simplified into several simple models. The complex MIMO IFBC model can be first transformed into a simple K-user MIMO interference channel model, by designing the first stage precoding matrix to eliminate inter-cell interference (ICI). Based on the designed first stage precoding matrix, the MIMO IFBC becomes several single-cell MIMO IFBCs, and the second stage precoding matrix can be designed to eliminate inter-user interference (IUI). The high system sum-rate performance and low computational complexity of the proposed IA design scheme are verified through a series of simulations.

Low Complexity IA Design for the Multi-Cell MIMO Downlink Cellular Network

Interference alignment (IA) is a promising technology to effectively eliminate interference and improve the wireless communication network capacity. However, the low complexity IA solution with high degrees of freedom (DoF) for the multi-cell multi-user cellular network is still an open problem that needs to be solved. To address this problem, in this paper, the two-stage IA scheme for the multi-cell multi-user multiple-input multiple-output (MIMO) interfering broadcast channel (IFBC) model is investigated. Since a multi-cell IFBC is a complex interfering channel model, it is simplified into several simple models. The complex MIMO IFBC model can be first transformed into a simple K-user MIMO interference channel model, by designing the first stage precoding matrix to eliminate inter-cell interference (ICI). Based on the designed first stage precoding matrix, the MIMO IFBC becomes several single-cell MIMO IFBCs, and the second stage precoding matrix can be designed to eliminate inter-user interference (IUI). The high system sum-rate performance and low computational complexity of the proposed IA design scheme are verified through a series of simulations. 

In MIMO X channel (XC) there are two multiple transmitter and receivers antenna pairs, where every transmitters communicate to every receivers. The MIMO Z channel (ZC) [1] is the special case of MIMO X channel [1] that is obtained by elimination of one link and message corresponding to it. MIMO Z has been derived and then compare to existing system. After MIMO X channel will be derived and then propose the new upper bound for MIMO ZC. Next we will consider worst noise covariance at receiver to derive another upper bound called MIMO MAC. After utilization of the MIMO MAC Channel we calculate better upper bound by utilization of tighter sum rate named Turbo MIMO X Channel. Finally, we use Turbo MIMO X equations. These numerical equations give the better proposed upper bounds of sum rate capacity which is tighter than existing bounds.

Reducing interference of Gaussian MIMO Z channel and Gaussian MIMO X Channel

We study the performance of Space Shift Keying modulation which is based on the concept of Spatial modulation for Multiple Input Multiple Output (MIMO) system. SSK is operated as Orthogonal SSK (OSSK) and Bi-Orthogonal SSK (BiSSK) and the Bit Error probability (BEP) is evaluated for performance measurement. In the proposed system, Spatial Modulation is used in the low-complexity MIMO Systems over the Rayleigh fading Channel to consider the Multipath propagation that exist in MIMO Systems when the Transmitter antenna diversity is increased and it is shown that we get better results as compared to the existing method using Rician fading Channel.

Paras Krishnatre

Papers

Reducing interference of Gaussian MIMO Z channel and Gaussian MIMO X Channel

LOS estimation in MIMO system using Space Shift Keying