Modeling and Performance of Mine MIMO Channel Based on Geometric Stochastic Characteristics

MIMO technology has become one of the most promising content in mobile wireless communication, and it has shown positive application value in both application and innovation. On the basis of understanding the current development status of MIMO channel technology, this paper analyzes the standardization of MIMO to the future development trend according to the modeling of broadband dryness ratio of GBSM.

Surveying on MIMO Technology for Future Wireless Communication

Massive MIMO is an extension of traditional MIMO with the exception that the BSs in massive MIMO are equipped with large number of antennas, usually hundred or more. This large number of antennas provide several positive advantages towards wireless communication with respect to increasing volume of data traffic. Each antenna is capable of serving multiple users simultaneously leading to reduction in power consumption as well as data rate amplification. Additionally, narrow and more focused beams are pointed to individual user devices located at the cell edge thereby upgrading of downlink signal quality. Using massive MIMO technique also increases reliability of the links, reduces noise effects, and mitigates and interference. With increasing number of users gets service, the throughput of the system also increases.

Survey on Massive MIMO: Technology, Challenges, Opportunities and Benefits

Wireless communication technologies have been studied and explored in response to the global shortage of bandwidth in the field of wireless access. Next-generation networks will be enabled by massive MIMO. Using relatively simple processing, it provides high spectral and energy efficiency by combining antennas at the receiver and transmitter. This paper discusses enabling technologies, benefits, and opportunities associated with massive MIMO, and all the fundamental challenges. Global enterprises, research institutions, and universities have focused on researching the 5G mobile communication network. Massive MIMO technologies will utilize simpler and linear algorithms for beam forming and decoding. As part of future 5G, massive MIMO technology will be used to increase the efficiency of spectrum utilization and channel capacity. The paper then summarizes the technologies that are used in massive MIMO system, including channel estimation, pre-coding, and signal detection.

On the Spectral Efficiency for Distributed Massive MIMO Systems

Distributed MIMO (D-MIMO) systems are expected to play a key role in deployments for future mobile communications. Together with massive MIMO technology, D-MIMO aims to maximize the spectral efficiency and data rate in mobile networks. This paper proposes a deep study on the spectral efficiency of D-MIMO systems for essential channel parameters, such as the channel power balance or the correlation between propagation channels. For that purpose, several propagation channels were acquired in both anechoic and reverberation chambers and were emulated using channel simulators. In addition, several frequency bands were studied, both the sub–6 GHz band and mmWave band. The results of this study revealed the high influence of channel correlation and power balance on the physical channel performance. Low-correlated and high-power balance propagation channels show better performances than high correlated and power unbalance channels in terms of spectral efficiency. Given these results, it will be fundamental to take into account the spectral efficiency of D-MIMO systems when designing criteria to establish multi-connectivity in future mobile network deployments.

Guided MAC to Enhance Quality of Experience of Video Data Transmission over Massive MIMO

Massive MIMO (M-MIMO) system comprises of multiple number of antennas to achieve energy- efficiency and large gains in spectral-efficiency in comparison to existing MIMO technology. High speed and Quality of Experience (QoE) of video data over wireless communication has always been a challenge for the researchers due to scarcity of the bandwidth, fading and interference. The channels with high noise corrupt the transmitted video and results in poor QoE of at the receiver. Therefore, to maintain the quality of transmitted video, it is highly desirable to identify noisy channels and avoid transmission over them. This paper deals with QoE of the transmitted video over Massive MIMO channels. The channels are categorized into two categories: good and bad depending upon the value of Signal to Interference and Noise Ratio (SINR). A channel above the minimum acceptable value (threshold) of SINR is categorized as good channel otherwise bad channel. A Guided MAC layer (GMAC) protocol is designed to transmit the video data over good channels only and to discard the transmission over bad channels.

Analysis of the massive MIMO technology

The article discusses the features of the Massive MIMO technology, the structure of the antenna array, as well as the advantages and example of using the massive MIMO system. The use of Massive MIMO opens up new opportunities and makes a significant contribution to achieving the stated requirements for the further evolution of LTE and 5G.

An improvement to hybrid beamforming precoding scheme for mmWave massive MIMO systems based on channel matrix

<p>The massive multiple-input multiple-output (MIMO) technology has been applied innew generation wireless systems due to growing demand for reliability and high datarate. Hybrid beamforming architectures in both receiver and transmitter, includinganalog and digital precoders, play a significant role in 5G communication networksand have recently attracted a lot of attention. In this paper, we propose a simple andeffective beamforming precoder approach for mmWave massive MIMO systems. Wefirst solve an optimization problem by a simplification subject, and in the second step,we use the covariance channel matrixfCk=Cov(Hk)andBk=HkHHkinstead of chan-nel matrixHk. Simulation results verify that the proposed scheme can enjoy a highersum rate and energy efficiency than previous methods such as spatially sparse method,analog method, and conventional hybrid method even with inaccurate Channel StateInformation (CSI). Percentage difference of the achievable rate ofCk=Cov(Hk)andBk=HkHHkschemes compared to conventional methods are 2.51% and 48.94%, re-spectively.</p>

An Energy Efficient Resource Allocation and Transmit Antenna Selection Scheme in mm-Wave Using Massive MIMO Technology

The massive Multiple-Input Multiple-Output (MIMO) improves the reliability of transmission and capacity of the channel. Resource allocation (RA) and Transmit Antenna Selection (TAS) can minimize the complexity in implementation and hardware costs. In this research, both the RA as well as the TAS of wireless communication in millimetre- wave (mm-wave) with massive MIMO technology is considered. Two different solutions are developed for this research such as the Deep Learning method for efficient resource allocation process and optimization algorithm for Transmit Antenna Selection (TAS) process. Here, the RA process is done with the help of Attention Based Capsule Auto-Encoder (ACAE) architecture which allocates the radio resources like power, space, time and frequency to all the available users in the system. Further, Battle Royale Optimization (BRO) algorithm is utilized to select an efficient antenna from multiple antennas at BS. This optimization algorithm optimally selects an efficient antenna so that, user equipments (UEs) can create high quality links and achieves a reduced power consumption rate of the whole architecture. The overall system performance depends on the selection of optimal antenna which in terms enhances Spectral Efficiency (SE), Energy Efficiency (EE), reliability, and diversity gain of MIMO technology. In this way, both RA and optimal antenna selection schemes are performed to maximize the overall performance of wireless communication with massive MIMO technology for 5G wireless communication applications. The implementation of the proposed methodology is evaluated on MATLAB. Finally, the efficiency of the developed method is improved with respect to the capacity, EE and SE.