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A peerreviewed article of this preprint also exists.
This version is not peerreviewed
Challenges  Algorithm  Highlight (pros)  Limitations (cons)  Key contribution  Ref. 

Beam selection and blockage prediction  Kernelbased KNN  Employs sub6 GHz CSI to predict vehicle’s positions and, consequently, preactivate the target BS as a way to speed up handovers preemptively. 


[135] 
Handover success prediction  XGBoost 



[134] 
Throughput estimation  AROW 


Estimates mmWave throughput using depth images and the AROW algorithm.  [129] 
DRL  Uses received power signals and video from depth cameras to train a DRL agent to overcome the computational complexity of learning the optimal handover policy, decreasing handover time. 

Shows that blockage prediction is improved by augmenting the input to the DRL agent with video from depth cameras.  [130]  
Blockage prediction and preemptive handover  DRL  Improves blockage prediction and handover reaction time by using depth images from multiple cameras.  Blockage caused by pedestrians being out of the camera’s coverage is hard to be avoided, requiring a greater number of cameras to be solved.  Employs DRL with received signal powers and images from multiple cameras as states to predict blockage and proactively initiate handovers.  [133] 
GRU 


Presents a blockage prediction and proactive handover solution that reduces latency and increases the system reliability in highmobile applications without requiring high cooperation overhead of coordinated transmission.  [126]  
Load balancing handover  DDPG  Maximizes the sum rate of all UEs moving along different trajectories while minimizing the number of handover and outage events. 

Maximizes the sum data rate of all users and minimizes the number of handovers and outage events using the DDPG algorithm.  [140] 
Beam gain maximization  CMAB 


The handover mobility optimization considers current 5G deployment aspects and uses current 5G signaling.  [141] 
Joint handover and beamforming optimization  QLearning 


Beamforming can be performed using a low number of pilots due to the use of path skeletons. Handover optimization uses Qlearning to determine the best backup BS for handover based on each UE location and trajectory.  [142] 
MAB 



[128]  
Minimization of handovers  DRL 


Reduces the number of handovers and maintains the user’s QoS.  [143] 
DRL  RHandoF and RHandoS adapt their policies to the channel fading characteristics, providing robustness of the proposed framework. 

Reduces the number of handovers, and increases the average network throughput.  [144]  
Handover success rate maximization and power allocation  DRL 


Employs a fully cooperative multiagent DRL approach to optimize handover success and power allocation jointly.  [136] 
Maximization of handover success rate and user localization  DL 


Usage of DL with users’ RSRP signals as input to implement a handover and localization mechanism.  [138] 
Maximization of handover success rate  XGBoost 


Usage of XGBoost and CSI to implement a handover mechanism.  [139] 
Handover prediction  DRL 


Multiagent DRL approach that employs imagelike states as input and takes the maximization of the system’s throughput into consideration as well.  [137] 
QLearning 


Usage of pedestrians’ locations and velocities to maximize their throughput by predicting the necessity of handovers.  [132]  
Proactive handovers  DRL  Employs DRL to map images into handover decisions, improving the QoS perceived by users, since handovers are proactively triggered. 

Usage of camera images to proactively trigger handovers.  [131] 
Challenges  Algorithm  Highlight (pros)  Limitations (cons)  Key contribution  Ref. 

Hardware and deployment awareness 




[145] 
Limited Feedback  Kmeans 

Kmeans clustering will suffer with dimensionality.  reduces the codebook design problem to an unattended clustering problem in a Grassmann collector.  [146] 
Limited Feedback 



Reduce the dimension of the full space and the feedback overhead.  [147] 
Environment awareness 




[148] 
Exhaustive search algorithm (ESA) 




[149] 
Large codebook sizes 




[150] 
Maximize the achievable rate  Kmeans  proposed codebook design can recognize and adapt to arbitrary propagation environment.  large amounts of channel state information (CSI) is stored as the input data.  characteristics extracted from the clustering centroids are used as the key channel information.  [151] 
Optimal precoding policy for complex (MIMO) 




[152] 
Limited Feedback 



proposed method is able to update the codebook adaptively according to the instantaneous channel state information.  [153] 
Limited Feedback 


when the Rician factor is small, the impact of the NLOS components is greater. As a result, the average quantization distortion increases. 

[154] 
CSI Feedback 




[155] 
Balanced MRTZF combined optimization 




[156] 
Interference mitigation (SI & CCI) 




[157] 
SINR balancing and power minimization 




[158] 
Challenges  Algorithm  Highlight (pros.)  Limitations (cons.)  Key Contributions  Ref. 

Channel Estimation  DL  Solves two problems with a similar approach. 

A comparison between a DL compressed sensing channel estimation for MIMO and deep learning quantized phase hybrid precoding.  [160] 
DL 

Needs large training dataset to provide robustness. 

[161]  
DL  Good results with lower computational complexity if compared to SVD and GMDbased methods.  The simulated communications environment is poorly described. 

[162]  
DL  The proposed solution can be generalized to unseen environments.  The training time was not discussed to assess the feasibility of the proposed solution. 

[163]  
Deep Learning Integrated Reinforcement Learning (DLIRL)  The hybrid beamforming method spectral efficiency that surpluses the fully digital precoding  As it is a new ML scheme, it lacks a complexity assessment to fairly compare it to the other algorithms  The authors propose a new way of combining DL and RL for beamforming leveraging high spectral efficiency and overall beamforming efectiveness  [174]  
Dynamic subarrays  AHC  Proposed hybrid precoding, which can efficiently avoid mutually correlated metrics. 


[165] 
Twostage precoding  DL  Proposed an MLbased approach to finding optimal dimensions with good accuracy and closer to the bruteforce solution. 


[166] 
Hybrid, analog, and Digital Precoding  DL 

Missing some ML algorithm details. 

[167] 
BFbased on IRS  DL 



[169] 
Locationbased  DL  A method capable of handling LoS and NLoS propagation. 


[170] 
Complexity reduction  DL  The proposed method has low computation complexity when compared with CNNs.  The computational complexity relies on the learning technology design (CNN or ELM). 

[171] 
DL  Using PSO combined with DNN, the authors reduced computational cost in managing antenna arrays.  Does not present accuracy, which hinders the performance assessment. 

[172]  
DRL 


A hybrid ML approach for precoding policy for complex MIMO systems.  [152]  
DL 

Leveraging prior knowledge with DL has an underlying training cost to collect information about the endtoend channel and network training. 

[173]  
Channel estimation and Power consumption  DL 

Might not be as precise as CSItrained DL models. 

[155] 
Database  Date of Search  Search Strings  Number of Selected Papers 

Google Scholar  March 2021  “machine learning”, “beam selection”  36 
April 2021  “machine learning”, “codebook”, “mimo”  21  
April 2022  “beamforming”, “machine learning”  7  
“beamforming”, “artificial intelligence”  5  
“beam selection”, “machine learning”  6  
“machine learning”, “beam selection”, “mmwave”  16  
“machine learning”, “handover”, “mmwave”  29  
December 2022  “Beamforming”, “Beamselection”, “machine learning”, “artificial intelligence”  25  
IEEE Explore  April 2021  “Beam selection”, “machine learning”, “artificial intelligence”  36 
Challenges  Algorithm  Highlight (pros)  Limitations (cons)  Key contribution  Ref. 

Situational Awareness 

Leverages situational awareness, such as vehicle coordinates, type, and speed.  Requires the neighboring vehicles to be connected to the network for best accuracy.  This paper evaluates different coordinate systems and several levels of available side information.  [66] 


The lack of information on trucks’ positions has a large impact on the method’s performance.  This work proposes predicting the received power with different beam power quantizations using regression models through situational awareness.  [74]  

The classification models have smaller feedback and better overall performance.  The regression models require feedback.  This work proposes optimal access point and beam pair predictions for establishing communication by exploiting UE’s localization and machine learning tools.  [73]  
CSML 

Only permanent blockage is considered.  This paper brings a doublelayer online learning algorithm based on user context and social preference information.  [71]  
RL  Using only GNSS data, the ML algorithm has a good beam prediction accuracy.  Although the beam prediction with LIDAR data is more accurate, it is computationally demanding.  This work investigated the use of GNSS and GNSS + LIDAR data for beam selection with NN using Raymobtime datasets.  [92]  
FML 

The algorithm relies on GPS coordinates, which can be inaccurate in domestic devices.  An online learning algorithm based on CMAB is proposed, enabling mmWave BS to learn from the context autonomously, and it provides a scalable solution to increase the deployment density of mmWave BS.  [75]  
MAB 



[69]  
Position aided  DNN 


The results vary with the number of obstacles for training and test datasets, highlighting the robustness of traintest mismatch.  [67] 
MAB 

The paper lacks a discussion on practical implementations and the algorithm’s computational complexity.  Proposes an online method for beam selection to speed up the process.  [77]  
LtR 


Authors use context information and past beam measurements to determine potential beam pointing directions.  [76]  

The algorithm presents high accuracy for lowresolution images. 

Proposes a CNN algorithm for beam selection and switching using lowresolution images as input.  [82]  
Angle of Arrival Aided 

Evaluates the impact of using imperfect and realistic information for the AoA and received power estimation by using Capon and MUSIC estimation methods.  The BS performance degrades for a low number of UEs compared to the available antennas.  Proposes the use of AoA and received power as input of a DNN to select the best beamformer on a codebook rather than the complete channel matrix, which is a realistic approach.  [68] 
Vehicular Networks  SVM  Higher sum rate and lower complexity than channel estimationbased method.  The training depends on the link density, which is hard to estimate and may vary substantially in real scenarios.  Proposes a tailored SVM/SMO algorithm for beam training.  [70] 
3D scenebased  DNN  The 3Dscene reconstruction achieves better accuracy than LIDAR, which is more expensive. 

In this paper, a 3D scene reconstruction is used to identify the best beams.  [78] 
Beam Domain Image Reconstruction 

Reduced beam selection overhead without degrading the beamforming performance.  The training is based on historical data.  This paper treats the beam selection as an image reconstruction problem without requiring channel knowledge.  [80] 
Low overhead  LSTM  The proposed scheme finds the narrow best beam based only on wide beam measurements reducing the beam training overhead.  Only DFT codebook is tested as both high and lowresolution codebook.  This paper proposes a DLbased low overhead analog beam selection scheme.  [81] 
DNN 

Lacks comparisons with other algorithms using the same scenario (i.e., DeepMIMO O1).  This paper relies on sub6GHz channel information to deduce the resources in the mmWave band.  [83]  
Sub6GHz channel information.  DNN 


A dualband scheme to predict beam and blockage from the sub6GHz band to aid in the mmWave band.  [85] 
DNN  Presents a prototype validation of an indoor scenario, which shows that the raytracing and the beam selection method are very close to the real scenario. 

The PDP of the sub6 GHz channel, which is highly available and does not demand beamforming, was used as input of a DNN for beam selection estimation in indoor and outdoor scenarios.  [84]  
Blockage prediction  CNN  The use of RGB images reduces beam selection and blockage prediction overhead. 

The paper joints images and sub6GHz channel information to identify mmWave blocked users.  [86] 
Intercarrier Interference (ICI) Mitigation  DNN  Low computation time yet high spectral efficiency algorithm.  The paper lacks profound analysis for more users and if the grouping is effective.  This paper proposes an optimal user group beam selection scheme aiming the spectral efficiency maximization.  [117] 
Small cell networks  SVM  Reduced complexity with quick and high ASR in the BS.  Though the paper assumes analog beamforming, the sidelobe interference is ignored.  This paper aims to maximize Average SumRate (ASR) for concurrent transmission on an analog beamforming mmWave network by analyzing the BS spatial distribution.  [121] 
LIDAR data  DNN  High accuracy for topM beamselection classification.  The one LIDAR per vehicle premise is not feasible due to LIDAR cost.  Proposes using LIDAR information to select beams in vehicular applications using deep learning, comparing centralized and distributed LIDAR.  [90] 
CNN  The use of LiDAR data reduced beamselection overhead for LOS situations.  Overhead increases on NLOS occasions to maintain a tolerable throughput ratio.  The use of LIDAR data with CNN reduces the beam selection overhead for Vehicle to Infrastructure (V2I) communications.  [91]  
DL 

The measurement setup is complex and hard to be reproduced  The authors establish guidelines for beamselection dataset generation and release a real experiment dataset with the paper results  [93]  
IRSAssisted Beam Selection  DL 

The algorithm depends on high BSUE and UE computational capabilities to provide full mobility awareness.  This work presents an IRSassisted mmWave network to improve coverage, handover, and beamswitching.  [122] 
Channel Data Generation and Position aided Beamforming 

Beam selection performance is simulated for several classification methods.  The paper is focused on data generation and classification methods for beam selection.  It describes a methodology for generating mmWave channel data, including realistic traffic simulation.  [124] 
SVM  The computational complexity of the proposed datadriven approach is significantly lower than the suboptimization method.  The number of analog beams considered is too small.  The authors propose a novel method, called biasedSVM, that determines the optimal parameter of the Gaussian kernel function to achieve optimal beam selection with low computational complexity.  [96]  
RFC  The model complexity decreases as the number of users increases and is lower than the other compared methods, which is an advantage for delaysensitive applications.  The simulation tool is not mentioned, which inhibits the results’ reproductivity. 

[97]  
Low complexity 
DL  The authors propose a sampling method, reducing the number of seeped beams, and the DL predicts all beams, increasing the search space for the beam selection  The beam combination method cannot be generalized, so in practice, each scenario may require a different combination  A method for sampling a fraction of the beam pairs is proposed, combined with a DL for predicting the RSRP of all beams from the samples  [99] 
RBF  Reduced complexity by several orders of magnitude, with nearoptimal performance compared with conventional methods.. 


[98]  
Qlearning  The performance is close to the optimal solution but takes fewer iterations.  Depends on knowledge of the channel matrix.  The paper minimizes the training time for beam selection using Qlearning to find the bestquantized analog precoders.  [94]  
DNN  This approach is appropriate for practical massive MIMO systems due to the complexity of the algorithm, which is not proportional to the number of beamforming vectors, using only one pilot signal. 

This work proposes a novel algorithm (named Deep Scanning) based on deep Qlearning.  [100]  
CNN 

The paper assumes a perfect complex channel matrix as input, which can be hard to obtain in a real scenario.  Authors propose a novel modeldriven technique based on CNN, which calculates only essential and passes it through a lowcomplex beamforming recovery algorithm.  [101]  
Body area network  GAN  Authors generated a dataset for WBAN based on a human pose dataset used for computer vision.  Does not address how the beam prediction would be made without an external camera, and only one set of sensor’s location is provided  This work proposes employing a nonintrusive beamforming method in the WBAN with the use of GAN method for mmWave beam predictions using human pose images.  [107] 
Highly mobile systems  DNN  Authors develop lowcomplexity mmWave coordination strategies for coverage coordination and latency reduction using omnidirectional + directional beams in the offline training phase and only omnidirectional transmission in the testing phase. 

To reduce the overhead, the BSs use DNN to determine the best beams using quasi omnidirectional patterns during the online test phase.  [108] 
Outofband information  CNN 


The authors created an experimental setup with mmWave hardware, obstacles, and cameras, which originated a dataset of images and beam pairs. Furthermore, the dataset was used for imagebased beam prediction.  [79] 
Large Scale MIMO  Qlearning  Outperformed stateoftheart in terms of capacity.  Only assumes Rayleigh fading channel.  Beam scheduling method for enhancing the RF spectrum utilization by subleasing RF slices.  [88] 
Limited Feedback  DNN  The method achieves high sum rates in the low SNR regime and Rician fading. 


[104] 
Interference Rejection  CNN 

Needs large training datasets and offline training.  The CNN is employed for space and spacetime processing, evaluated in two scenarios with different interference and DOA configurations.  [118] 
Power restrictions  CNN  The intensive computational training phase is done offline.  Considers perfect CSIonly.  The goal of this paper is to maximize the downlink SINR based on power restrictions per antenna at the base station and improve the performance complexity tradeoff.  [116] 
Cloud Assisted 
ConvLSTM  The proposed solution improves positioning prediction accuracy while reducing storage costs by using Cloud and Edge collaboratively.  The load caused in the backhaul and the Cloud service is not taken into account.  This paper proposes a collaborative cloudedge architecture. The BS uses ConvLSTM to predict the user distribution and, through this, decide on a better set of beams.  [109] 
Scheduling  RL 


Its used CAVIAR methodology for communication systems combined with the AI models, and the virtual world components for terrestrial and aerial beam selection.  [87] 
Dataset generation  GRNN  Provides a baseline solution that predicts future beams based on the sequence of previous ones.  The baseline solution does not take the generated images into account.  This work used computer vision with AI algorithms to predict blockage through image classificationaided beam selection.  [125] 
Beam Alignment  KSBLLTS 


The Authors developed a KBSL algorithm for mmWave beam alignment and beam selection policy to validate which policy would result in the most efficient beamformer: the linear Thompsom sampling, the omnidirectional, random, and greedy policies.  [120] 
No Reference Signal  NN  Does not depend on prior knowledge.  The proposed technique only works in LOS conditions. 

[102] 
DL  More efficient and accurate than MUSIC but with comparable performance. 


[103]  
Dual Connectivity  SVM 

Training time significantly increases with the dataset size. 

[110] 
Nonideal Channel conditions  NN  Reduced overhead compared to the exhaustive search and modelbased approaches. 


[95] 
Beam tracking and rate adaptation  MAB 


Proposal of a novel restless MAB framework for beamtracking for mmWave cellular systems using ACK/NACK messages instead of explicit control signaling. The method implements an online RL technique called adaptive Thompson sampling, which selects the best beam and MCS pair.  [105] 
Data Augmentation  SMOTE 

Lack of comparison of the SMOTEbased method with other algorithms found in the literature.  A method to augment datasets with synthetic data.  [111] 
Angle Estimation and User Selection  DL 


A computervisionbased method to estimate the beam angle, consequently selecting the beam and user.  [112] 
CVbased UAV localization  CNN 


A CVaided joint optimization scheme of flight trajectory and power allocation for mmWave UAV communication systems.  [113] 
Power control and beam alignment  LSTM 


Proposal of a DL framework for beam selection and power control in massive MIMO  mmWave communications to optimize transmit power and beam selection for users with unknown channel state information.  [114] 
Beam change prediction  LSTM 


The LSTMbased beam change prediction scheme can achieve over 58% power reduction regarding beam management compared to deployed commercial schemes.  [106] 
Beam alignment  DNN 

The solution presents high computational complexity.  This approach proposes using contextual information (position and orientation of user) for the initial beam alignment procedure through deep learning techniques.  [115] 
Challenges  Algorithm  Highlight (pros.)  Limitations (cons.)  Key Contributions  Ref. 

Beam prediction under adversarial attacks  DL  The proposed counterattack can be used against a variety of different adversarial ML attacks.  To be effective, the attacker must have access to the gradient of the loss function for a given input instance, which in turn implies having access to the model’s weights, which is often unfeasible.  Proposes a mitigation method that uses the gradients of the victim’s model to retrain it with adversarial samples and their respective labels and mitigate adversarial attacks, consequently improving the security.  [183] 
Proposes two methods for counterattacking adversarial attacks: adversarial training and defensive distillation.  [184]  

[182] 
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. 
Submitted:
15 March 2023
Posted:
15 March 2023
You are already at the latest version
A peerreviewed article of this preprint also exists.
This version is not peerreviewed
Submitted:
15 March 2023
Posted:
15 March 2023
You are already at the latest version
Challenges  Algorithm  Highlight (pros)  Limitations (cons)  Key contribution  Ref. 

Beam selection and blockage prediction  Kernelbased KNN  Employs sub6 GHz CSI to predict vehicle’s positions and, consequently, preactivate the target BS as a way to speed up handovers preemptively. 


[135] 
Handover success prediction  XGBoost 



[134] 
Throughput estimation  AROW 


Estimates mmWave throughput using depth images and the AROW algorithm.  [129] 
DRL  Uses received power signals and video from depth cameras to train a DRL agent to overcome the computational complexity of learning the optimal handover policy, decreasing handover time. 

Shows that blockage prediction is improved by augmenting the input to the DRL agent with video from depth cameras.  [130]  
Blockage prediction and preemptive handover  DRL  Improves blockage prediction and handover reaction time by using depth images from multiple cameras.  Blockage caused by pedestrians being out of the camera’s coverage is hard to be avoided, requiring a greater number of cameras to be solved.  Employs DRL with received signal powers and images from multiple cameras as states to predict blockage and proactively initiate handovers.  [133] 
GRU 


Presents a blockage prediction and proactive handover solution that reduces latency and increases the system reliability in highmobile applications without requiring high cooperation overhead of coordinated transmission.  [126]  
Load balancing handover  DDPG  Maximizes the sum rate of all UEs moving along different trajectories while minimizing the number of handover and outage events. 

Maximizes the sum data rate of all users and minimizes the number of handovers and outage events using the DDPG algorithm.  [140] 
Beam gain maximization  CMAB 


The handover mobility optimization considers current 5G deployment aspects and uses current 5G signaling.  [141] 
Joint handover and beamforming optimization  QLearning 


Beamforming can be performed using a low number of pilots due to the use of path skeletons. Handover optimization uses Qlearning to determine the best backup BS for handover based on each UE location and trajectory.  [142] 
MAB 



[128]  
Minimization of handovers  DRL 


Reduces the number of handovers and maintains the user’s QoS.  [143] 
DRL  RHandoF and RHandoS adapt their policies to the channel fading characteristics, providing robustness of the proposed framework. 

Reduces the number of handovers, and increases the average network throughput.  [144]  
Handover success rate maximization and power allocation  DRL 


Employs a fully cooperative multiagent DRL approach to optimize handover success and power allocation jointly.  [136] 
Maximization of handover success rate and user localization  DL 


Usage of DL with users’ RSRP signals as input to implement a handover and localization mechanism.  [138] 
Maximization of handover success rate  XGBoost 


Usage of XGBoost and CSI to implement a handover mechanism.  [139] 
Handover prediction  DRL 


Multiagent DRL approach that employs imagelike states as input and takes the maximization of the system’s throughput into consideration as well.  [137] 
QLearning 


Usage of pedestrians’ locations and velocities to maximize their throughput by predicting the necessity of handovers.  [132]  
Proactive handovers  DRL  Employs DRL to map images into handover decisions, improving the QoS perceived by users, since handovers are proactively triggered. 

Usage of camera images to proactively trigger handovers.  [131] 
Challenges  Algorithm  Highlight (pros)  Limitations (cons)  Key contribution  Ref. 

Hardware and deployment awareness 




[145] 
Limited Feedback  Kmeans 

Kmeans clustering will suffer with dimensionality.  reduces the codebook design problem to an unattended clustering problem in a Grassmann collector.  [146] 
Limited Feedback 



Reduce the dimension of the full space and the feedback overhead.  [147] 
Environment awareness 




[148] 
Exhaustive search algorithm (ESA) 




[149] 
Large codebook sizes 




[150] 
Maximize the achievable rate  Kmeans  proposed codebook design can recognize and adapt to arbitrary propagation environment.  large amounts of channel state information (CSI) is stored as the input data.  characteristics extracted from the clustering centroids are used as the key channel information.  [151] 
Optimal precoding policy for complex (MIMO) 




[152] 
Limited Feedback 



proposed method is able to update the codebook adaptively according to the instantaneous channel state information.  [153] 
Limited Feedback 


when the Rician factor is small, the impact of the NLOS components is greater. As a result, the average quantization distortion increases. 

[154] 
CSI Feedback 




[155] 
Balanced MRTZF combined optimization 




[156] 
Interference mitigation (SI & CCI) 




[157] 
SINR balancing and power minimization 




[158] 
Challenges  Algorithm  Highlight (pros.)  Limitations (cons.)  Key Contributions  Ref. 

Channel Estimation  DL  Solves two problems with a similar approach. 

A comparison between a DL compressed sensing channel estimation for MIMO and deep learning quantized phase hybrid precoding.  [160] 
DL 

Needs large training dataset to provide robustness. 

[161]  
DL  Good results with lower computational complexity if compared to SVD and GMDbased methods.  The simulated communications environment is poorly described. 

[162]  
DL  The proposed solution can be generalized to unseen environments.  The training time was not discussed to assess the feasibility of the proposed solution. 

[163]  
Deep Learning Integrated Reinforcement Learning (DLIRL)  The hybrid beamforming method spectral efficiency that surpluses the fully digital precoding  As it is a new ML scheme, it lacks a complexity assessment to fairly compare it to the other algorithms  The authors propose a new way of combining DL and RL for beamforming leveraging high spectral efficiency and overall beamforming efectiveness  [174]  
Dynamic subarrays  AHC  Proposed hybrid precoding, which can efficiently avoid mutually correlated metrics. 


[165] 
Twostage precoding  DL  Proposed an MLbased approach to finding optimal dimensions with good accuracy and closer to the bruteforce solution. 


[166] 
Hybrid, analog, and Digital Precoding  DL 

Missing some ML algorithm details. 

[167] 
BFbased on IRS  DL 



[169] 
Locationbased  DL  A method capable of handling LoS and NLoS propagation. 


[170] 
Complexity reduction  DL  The proposed method has low computation complexity when compared with CNNs.  The computational complexity relies on the learning technology design (CNN or ELM). 

[171] 
DL  Using PSO combined with DNN, the authors reduced computational cost in managing antenna arrays.  Does not present accuracy, which hinders the performance assessment. 

[172]  
DRL 


A hybrid ML approach for precoding policy for complex MIMO systems.  [152]  
DL 

Leveraging prior knowledge with DL has an underlying training cost to collect information about the endtoend channel and network training. 

[173]  
Channel estimation and Power consumption  DL 

Might not be as precise as CSItrained DL models. 

[155] 