## What is/are Mimo Networks?

Mimo Networks - Cell-Free massive MIMO networks are recognized as possible solution in the future wireless communication.^{[1]}This power control algorithm leads to scalable CF Massive MIMO networks in which the amount of computations conducted by each access point (AP) does not depend on the number of network APs.

^{[2]}However, there are still unsolved problems before such commercial MIMO networks are rolled out.

^{[3]}To promote energy conservation and improve utilization of system resources, we propose a joint antenna selection and power allocation (JASPA) strategy for CCFD massive MIMO networks.

^{[4]}In this paper, we design a deep learning framework for the power allocation problems in massive MIMO networks.

^{[5]}The proposed method greatly improves the accuracy of modulation classification in MIMO networks.

^{[6]}While prior work indicates that mMIMO networks employing time division duplexing have a significant capacity growth potential, deploying mMIMO in frequency division duplexing (FDD) networks remains problematic.

^{[7]}The major challenges in massive MIMO networks are to increase the system throughput and capacity with low complexity and reliability of the wireless communication system.

^{[8]}We then tackle massive MIMO networks, and exploit our asymptotic analysis in the number of antennas to derive a low-complexity, yet highly efficient, operational mode and transmit power allocation scheme for a finite-size scenario.

^{[9]}The RIS is deployed to help conventional massive MIMO networks serve the users in the dead zone.

^{[10]}Although many precoding techniques are essentially proposed for a small-scale MIMO, they have been exploited in massive MIMO networks.

^{[11]}The cellular mMIMO networks can provide high data rate for users, however their performance is not satisfied for the cell-edge users and shadowed users.

^{[12]}When network slicing is applied to CF-mMIMO networks in order to serve multiple tenants, the deployment flexibility of CF-mMIMO networks could be further improved.

^{[13]}In this paper, a resource allocation problem is studied for downlink cell-free massive MIMO networks, where each access point (AP) serves a cluster of user equipment (UE).

^{[14]}The results demonstrate that ARDI is capable of accurately reconstructing full downlink channels when the signal-to-noise ratio is higher than 15dB, thereby expanding the channel capacity of Massive MIMO networks.

^{[15]}We then apply our proposed technique to channel estimation in massive MIMO networks.

^{[16]}The results show that 5G Massive MIMO networks require about 50% less power consumption than the 4G ones, and the NB-IoT in-band deployment requires about 10% less power than guard-band deployment.

^{[17]}Massive MIMO networks).

^{[18]}We present a modification of conjugate beamforming for the forward link of cell-free massive MIMO networks.

^{[19]}To minimize the total power consumption, we further propose an improved coalition game approach to effectively optimize MU clustering for the large-scale MIMO networks, in which the size of a cluster is flexible.

^{[20]}In particular, we focus on the downlink performance of massive MIMO networks with the Maximum Ratio Transmission (MRT) precoder at the base stations.

^{[21]}This study investigates the channel correlation and the power delay profile (PDP) of the propagation channels in wideband massive MIMO networks through measurements on prototypes under certain circumstance.

^{[22]}Specifically, a novel cooperative uplink transmission and detection scheme is first proposed for massive MIMO networks, where each uplink frame is divided into a number of data blocks with independent coding schemes and the following blocks are decoded based on previously detected data blocks in both service and neighboring cells.

^{[23]}In particular, for a WPT system with coverage radius

^{[24]}Such an adaptive subarray architecture offers a tradeoff between hardware complexity and spectral efficiency in massive MIMO networks at mmWave frequencies.

^{[25]}To fill this gap, in this paper we conduct a comparative analysis of several most viable advanced jamming schemes in the widely-used MIMO networks.

^{[26]}This method was applied to MIMO networks that operate over Rayleigh fading channels with different antenna nodes.

^{[27]}In this study, a CSI feedback scheme using random scalar quantization (RSQ) for MU-MIMO networks is proposed.

^{[28]}Based on the provided results, we conclude that our proposed scheme increases the sum SE of multi-cell massive MIMO networks.

^{[29]}To further improve the spectrum efficiency of 5G massive MIMO networks, bi-directional training (BiT) was developed for TDD systems to maximize the downlink weighted sum rate.

^{[30]}Such that MIMO networks give output exactly matching with the information sent by the transmitter.

^{[31]}However, in practice MU-MIMO networks are far from their full potential due to the poor scalability problem, including high computational complexity at PHY layer and large-overhead channel contention at MAC layer.

^{[32]}This paper deals with the coverage probability analysis of Massive MIMO networks with random topology.

^{[33]}This article investigates the using of this new SM technique in Multi-user MIMO networks, denoted here as MU-QSM.

^{[34]}Degree-of-Freedom (DoF) based models have been widely used to study MIMO networks.

^{[35]}This paper aims at highlighting the different trade-offs affecting various performance metrics in CF-M-MIMO networks.

^{[36]}The results demonstrate that ARDI is capable of accurately reconstructing full downlink channels when the signal-to-noise ratio is higher than 15dB, thereby expanding the channel capacity of Massive MIMO networks.

^{[37]}Unlike previous related works, hardware impairment, transmission efficiency, and energy consumption at the circuit and antennas are involved in massive MIMO networks.

^{[38]}The IoT $K$ -User MIMO networks described improve upon prior multi-user capacity results through the application of a new beamformer modality.

^{[39]}We propose a linear precoding scheme that relaxes such infeasibility in overloaded MU-MIMO networks.

^{[40]}

## Massive Mimo Networks

Cell-Free massive MIMO networks are recognized as possible solution in the future wireless communication.^{[1]}This power control algorithm leads to scalable CF Massive MIMO networks in which the amount of computations conducted by each access point (AP) does not depend on the number of network APs.

^{[2]}To promote energy conservation and improve utilization of system resources, we propose a joint antenna selection and power allocation (JASPA) strategy for CCFD massive MIMO networks.

^{[3]}In this paper, we design a deep learning framework for the power allocation problems in massive MIMO networks.

^{[4]}The major challenges in massive MIMO networks are to increase the system throughput and capacity with low complexity and reliability of the wireless communication system.

^{[5]}We then tackle massive MIMO networks, and exploit our asymptotic analysis in the number of antennas to derive a low-complexity, yet highly efficient, operational mode and transmit power allocation scheme for a finite-size scenario.

^{[6]}The RIS is deployed to help conventional massive MIMO networks serve the users in the dead zone.

^{[7]}Although many precoding techniques are essentially proposed for a small-scale MIMO, they have been exploited in massive MIMO networks.

^{[8]}In this paper, a resource allocation problem is studied for downlink cell-free massive MIMO networks, where each access point (AP) serves a cluster of user equipment (UE).

^{[9]}The results demonstrate that ARDI is capable of accurately reconstructing full downlink channels when the signal-to-noise ratio is higher than 15dB, thereby expanding the channel capacity of Massive MIMO networks.

^{[10]}We then apply our proposed technique to channel estimation in massive MIMO networks.

^{[11]}The results show that 5G Massive MIMO networks require about 50% less power consumption than the 4G ones, and the NB-IoT in-band deployment requires about 10% less power than guard-band deployment.

^{[12]}Massive MIMO networks).

^{[13]}We present a modification of conjugate beamforming for the forward link of cell-free massive MIMO networks.

^{[14]}In particular, we focus on the downlink performance of massive MIMO networks with the Maximum Ratio Transmission (MRT) precoder at the base stations.

^{[15]}This study investigates the channel correlation and the power delay profile (PDP) of the propagation channels in wideband massive MIMO networks through measurements on prototypes under certain circumstance.

^{[16]}Specifically, a novel cooperative uplink transmission and detection scheme is first proposed for massive MIMO networks, where each uplink frame is divided into a number of data blocks with independent coding schemes and the following blocks are decoded based on previously detected data blocks in both service and neighboring cells.

^{[17]}In particular, for a WPT system with coverage radius

^{[18]}Such an adaptive subarray architecture offers a tradeoff between hardware complexity and spectral efficiency in massive MIMO networks at mmWave frequencies.

^{[19]}Based on the provided results, we conclude that our proposed scheme increases the sum SE of multi-cell massive MIMO networks.

^{[20]}To further improve the spectrum efficiency of 5G massive MIMO networks, bi-directional training (BiT) was developed for TDD systems to maximize the downlink weighted sum rate.

^{[21]}This paper deals with the coverage probability analysis of Massive MIMO networks with random topology.

^{[22]}The results demonstrate that ARDI is capable of accurately reconstructing full downlink channels when the signal-to-noise ratio is higher than 15dB, thereby expanding the channel capacity of Massive MIMO networks.

^{[23]}Unlike previous related works, hardware impairment, transmission efficiency, and energy consumption at the circuit and antennas are involved in massive MIMO networks.

^{[24]}

## User Mimo Networks

This article investigates the using of this new SM technique in Multi-user MIMO networks, denoted here as MU-QSM.^{[1]}The IoT $K$ -User MIMO networks described improve upon prior multi-user capacity results through the application of a new beamformer modality.

^{[2]}