Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges

Wang, Cheng-Xiang; Renzo, Marco Di; Stańczak, Sławomir; Wang, Sen; Larsson, Erik G.

doi:10.1109/mwc.001.1900292

Cited by 196 publications

(112 citation statements)

References 15 publications

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“…ML techniques, particularly supervisory supervision methods, are used to predict the path loss of the wireless channel, a key component of channel modeling. While in [7] demonstrates how artificial intelligence and machine learning combined is applied in the design of 5G networks to solve unstructured problems and how these techniques can influence channel estimation for network optimization.…”

Section: Introductionmentioning

confidence: 99%

Empirical Path Loss Model in City-forest Environment for Mobile Communications

Carvalho¹,

Batalha²,

Neto³

et al. 2021

JCIS

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The feasible choice of a propagation model for a given wireless system depends on environment type among other factors. Thus, it is a crucial decision on radio network planning. This current proposal is a new methodology applied for LTE systems that includes: to find optimal parameters of a propagation model that minimizes Root Mean Square Error (RMSE) and maximizes Grey Relation Grade and Mean Absolute Percentage Error, (GRG-MAPE) in a city-forest environment through the use of metaheuristic optimization such as Cuckoo Search (CS). The results, quantitatively analyzed by RMSE and GRG-MAPE, show a better accuracy of optimized model in comparison with the original version and even with Stanford University Interim (SUI) model.

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Section: Introductionmentioning

confidence: 99%

Empirical Path Loss Model in City-forest Environment for Mobile Communications

Carvalho¹,

Batalha²,

Neto³

et al. 2021

JCIS

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“…Some preliminary studies have shown the potential of AI and ML based wireless channel measurements and models, for example, MPCs clustering, scenario classification, and channel prediction, by using clustering, classification, and regression algorithms [335]. Different ML algorithms, such as artificial neural network (ANN), convolutional neural network (CNN), and generative adversarial network can be applied to wireless channel modeling [336]. One of the benefits of applying AI and ML over traditional channel modeling methods is that they can partly predict wireless channel properties for unknown scenarios, unknown frequency bands, and future time instants.…”

Section: Channel Measurements and Models For 5g And Beyondmentioning

confidence: 99%

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

You

Wang

Huang

et al. 2020

Sci. China Inf. Sci.

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1,195

495

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The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

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“…The reconfigurability should include system hardware re-uses and mode-switching. Meanwhile, with the rapid development of artificial intelligence [57], [58], such as machine learning [59], a 6G system is also required to be intelligent for providing better services, including the adaptation to environments and changes of functionality.…”

Section: B Potential System Architecture For 6g Communicationsmentioning

confidence: 99%