PMID- 35808457
OWN - NLM
STAT- MEDLINE
DCOM- 20220712
LR  - 20220716
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 22
IP  - 13
DP  - 2022 Jun 30
TI  - Comparative Analysis of Major Machine-Learning-Based Path Loss Models for 
      Enclosed Indoor Channels.
LID - 10.3390/s22134967 [doi]
LID - 4967
AB  - Unlimited access to information and data sharing wherever and at any time for 
      anyone and anything is a fundamental component of fifth-generation (5G) wireless 
      communication and beyond. Therefore, it has become inevitable to exploit the 
      super-high frequency (SHF) and millimeter-wave (mmWave) frequency bands for 
      future wireless networks due to their attractive ability to provide extremely 
      high data rates because of the availability of vast amounts of bandwidth. 
      However, due to the characteristics and sensitivity of wireless signals to the 
      propagation effects in these frequency bands, more accurate path loss prediction 
      models are vital for the planning, evaluating, and optimizing future wireless 
      communication networks. This paper presents and evaluates the performance of 
      several well-known machine learning methods, including multiple linear regression 
      (MLR), polynomial regression (PR), support vector regression (SVR), as well as 
      the methods using decision trees (DT), random forests (RF), K-nearest neighbors 
      (KNN), artificial neural networks (ANN), and artificial recurrent neural networks 
      (RNN). RNNs are mainly based on long short-term memory (LSTM). The models are 
      compared based on measurement data to provide the best fitting 
      machine-learning-based path loss prediction models. The main results obtained 
      from this study show that the best root-mean-square error (RMSE) performance is 
      given by the ANN and RNN-LSTM methods, while the worst is for the MLR method. All 
      the RMSE values for the given learning techniques are in the range of 0.0216 to 
      2.9008 dB. Furthermore, this work shows that the models (except for the MLR 
      model) perform excellently in fitting actual measurement data for wireless 
      communications in enclosed indoor environments since they provide R-squared and 
      correlation values higher than 0.91 and 0.96, respectively. The paper shows that 
      these learning methods could be used as accurate and stable models for predicting 
      path loss in the mmWave frequency regime.
FAU - Elmezughi, Mohamed K
AU  - Elmezughi MK
AD  - The Discipline of Electrical, Electronic and Computer Engineering, University of 
      KwaZulu-Natal, Durban 4041, South Africa.
FAU - Salih, Omran
AU  - Salih O
AUID- ORCID: 0000-0002-7861-5502
AD  - Institute of Systems Science, Durban University of Technology, Durban 4000, South 
      Africa.
FAU - Afullo, Thomas J
AU  - Afullo TJ
AUID- ORCID: 0000-0002-2710-4577
AD  - The Discipline of Electrical, Electronic and Computer Engineering, University of 
      KwaZulu-Natal, Durban 4041, South Africa.
FAU - Duffy, Kevin J
AU  - Duffy KJ
AD  - Institute of Systems Science, Durban University of Technology, Durban 4000, South 
      Africa.
LA  - eng
PT  - Journal Article
DEP - 20220630
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Algorithms
MH  - Forecasting
MH  - Linear Models
MH  - *Machine Learning
MH  - *Neural Networks, Computer
PMC - PMC9269839
OTO - NOTNLM
OT  - 5G
OT  - 6G
OT  - channel modeling
OT  - machine learning
OT  - neural network
OT  - path loss
OT  - propagation characteristics
OT  - random forest
OT  - regression
OT  - wireless communications
COIS- The authors declare no conflict of interest.
EDAT- 2022/07/10 06:00
MHDA- 2022/07/14 06:00
PMCR- 2022/06/30
CRDT- 2022/07/09 01:24
PHST- 2022/04/13 00:00 [received]
PHST- 2022/05/14 00:00 [revised]
PHST- 2022/06/07 00:00 [accepted]
PHST- 2022/07/09 01:24 [entrez]
PHST- 2022/07/10 06:00 [pubmed]
PHST- 2022/07/14 06:00 [medline]
PHST- 2022/06/30 00:00 [pmc-release]
AID - s22134967 [pii]
AID - sensors-22-04967 [pii]
AID - 10.3390/s22134967 [doi]
PST - epublish
SO  - Sensors (Basel). 2022 Jun 30;22(13):4967. doi: 10.3390/s22134967.