On the Capacity and Transmission Techniques of Massive MIMO Systems

Abstract

A massive MIMO wireless system is a multiuser MISO system where base stations consist of a large number of antennas with respect to number of user devices, each equipped with a single antenna. Massive MIMO is seen as the way forward in enhancing the transmission rate and user capacity in 5G wireless. The potential of massive MIMO system lies in the ability to almost always realize multiuser channels with near zero mutual coupling. Coupling factor reduces by 1/2 for each doubling of transmit antennas. In a high bit rate massive MIMO system with base station antennas and users, downlink capacity increases as log 2 bps/Hz, and the capacity per user reduces as log 2 bps/Hz. This capacity can be achieved by power sharing and using signal weighting vectors aligned to respective 1 × channels of the users. For low bit rate transmission, time sharing achieves the capacity as much as power sharing does. System capacity reduces as channel coupling factor increases. Interference avoidance or minimization strategies can be used to achieve the available capacity in such scenarios. Probability distribution of channel coupling factor is a convenient tool to predict the number of antennas needed to qualify a system as massive MIMO.

A stochastic geometric programming approach for power allocation in wireless networks

“…The first model we consider allows maximizing the worst user signal-tointerference noise ratio for a MaMIMO system where each receiver has perfect channel state information [37]. Hereafter, we assume that the MaMIMO network is located inside a single cell area and it is composed of a set of K = {1, .…”

“…The second model we consider maximizes the total capacity of the network in presence of signal-to-interference noise ratio for a MaMIMO system where each receiver has a perfect channel state information [37]. In this case, the non-linear optimization problem we consider can be stated as…”

Spectral Efficiency Improvement of 5G Massive MIMO Systems for High-Altitude Platform Stations by Using Triangular Lattice Arrays

“…The received signal-to-interference-plus-noise ratio (SINR) for the n th user in case of a multi-user scenario can be written as [ 40 , 50 , 51 ]: where G ( θ n , ϕ n ) represents the HAPS array’s gain toward the n th user located at ( θ n , ϕ n ); δ represents the ratio between the transmitted power ( P t ) and the noise power ( σ 0 ); h n ϵ 1× M ( n = 1,2,.., K ) corresponds to the channel propagation between the n th user and the HAPS antenna array; and w n ϵ M ×1 consists of the precoding vector that, in general, depends on the selected beamforming method. It is worth noting that, differently from Reference [ 37 ], the simulated matching efficiency ( η Γ ) was included in (9) in order to achieve a more accurate SINR estimate.…”

Optimization of a T-Shaped MIMO Antenna for Reduction of EMI

“…Tian et al [9] and Liu et al [10] discussed channel capacity to enhance antenna efficiency by reducing the mutual coupling and effects of spatial correlation under the Rayleigh fading channel assumption. Abdul et al [11] and Khalighi et al [12] proposed a method to identify the number of antennas at the asymmetric base station as well as in mobile units in order to increase the effectiveness of the station. Du et al [13] addressed the need to optimize overall MIMO system capacity, which includes the unequal costs of antennas at both channels.…”