Enhanced development of communication between the network and the end user by eliminating the interference signals inMIMOchannel

Abstract

Though the key technology for 5G communication is the massive Multiple input and multiple output (MIMO), it has a major drawback when the user generated inference signals are to be handled by several other users. When these interference signals are used in various users, issues concerning system ability, power management issues and QoS related issues arises with the involvement of MIMO channel. Though internet of things (IoT) making use of MIMO is still under development, the requirements of the IoT is quite different when compared to several other connections. Thus, taking into account the demands, the solutions were framed with the help of the proposed system. One such solution is the Partition Square and Cross-Processing method that can possess several antennas at the given destination. Based on this solution obtained the data rate can be drastically increased correspondingly reducing the power consumed thus producing the most desirable result with high signal to noise ratio (SNR). Best victory is another such solution that solves the issues relating to power reduction thereby providing the best solution with minimum SNR. These solutions produced by the proposed system still has some disadvantages with respect to the following case: when the number of destination antennas is less than the number of source antennas. This case is considered as most common in the model proposed. For the above-mentioned case, near optimal solution is produced by both the algorithms. In this article, the transmitters are represented by the source antenna and the receiver is represented by the destination antenna. Thus, with the help of IoT connectivity, major benefits can be bought to the massive MIMO channel.

Low-Power Communication Signal Enhancement Method of Internet of Things Based on Nonlocal Mean Denoising

“…When processing each pixel in the noisy image, the distribution around the pixel will be evaluated and compared, and the difference similarity of the distribution will be used to calculate the weight ω. The performance of nonlocal mean denoising algorithm is better than other traditional image denoising algorithms, and the denoising effect is better [19][20][21].…”

Spatial channel correlation for local scattering with linear MMSE‐based estimator and detector in multi‐cell large scale MU‐MIMO networks

“…However, the performance of LS-MU-MIMO has been analyzed in the literature for uncorrelated channel model. [5][6][7] In practice and due to irregularity in the propagation environment, higher power levels are received from some spatial directions more than others. This phenomenon is known as spatial correlation and has a significant influence on the performance of the wireless system.…”

“…These aspects provide insights into the system behavior and can be captured by different tractable models. However, the performance of LS‐MU‐MIMO has been analyzed in the literature for uncorrelated channel model 5‐7 …”

An interference alignment scheme based on limited feedback with differentiation in heterogeneous networks

“…Therefore, it is very necessary to solve the complex problems of interference in the communication system. A non‐repetitive algorithm is proposed in Reference 6 to combine both the transmitters and the receivers, which assists in the process of converting the cases of the users using various antennas. It can minimize the interference under a given MIMO channel and ultimately improve throughput.…”