6G White Paper on Machine Learning in Wireless Communication Networks

Ali, Samad; Saad, Walid; Rajatheva, Nandana; Chang, Kapseok; Steinbach, Daniel; Sliwa, Benjamin; Wietfeld, Christian; Kai, Masahiro; Shiri, Hamid; Zepernick, Hans‐Jürgen; Chu, Thi My Chinh; Ahmad, Ijaz; Huusko, Jyrki; Suutala, Jaakko; Bhadauria, Shubhangi; Bhatia, Vimal; Mitra, Rangeet; Amuru, SaiDhiraj; Abbas, Robert; Shao, Baohua; Capobianco, Michele; Yu, Guanlong; Claes, Maelick; Karvonen, Teemu; Chen, Mingzhe; Girnyk, Maksym A.; Malik, Hassan

doi:10.48550/arxiv.2004.13875

Cited by 36 publications

(50 citation statements)

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“…As research on THz technology and 6G networks is rising [25], [26], a few tutorials on 6G systems have already been published [27], [28]. The 6G literature covers topics such as the role of machine-learning methods [29], broadband connectivity [30], and integrated localization and sensing [8]. For THz systems, signal processing techniques [26], defining features [31], and MAC protocols [32] are also addressed.…”

Section: The Motivation and Structure Of This Workmentioning

confidence: 99%

“…However, a large volume of data from wideband MIMO systems needs to be collected for training purposes, and the trained models have to be updated to adapt to environmental variations. Typical ML algorithms and techniques such as random forest [40], reinforcement learning [41], and deep neural networks [42] are potential solutions to maintain good KPI network service levels [29].…”

Section: B Localization Techniques 1) Geometry-based and Learning-bas...mentioning

confidence: 99%

“…The transmitted signal can be chosen randomly or obtained using a directional beamforming matrix with positioning reference signals (PRS) as discussed in [43], [44], [55], [128]. We use (29) as the benchmark MIMO signal model for mmWave systems. Next, we detail the AOSA-based MIMO signal model for THz systems.…”

Section: Received Signal Model 1) Mimo Architecturesmentioning

confidence: 99%

“…2) Position and Orientation Error Bounds: Given the signal model in (29), both direct and multi-stage approaches will have the same CRLB for the state vector if all available measurements (variables in Γ) are considered [128]. The CRLBs for the measurement vector can also be calculated, which is useful for system design and optimization.…”

Section: B Crlb 1)mentioning

confidence: 99%

“…However, this approach is impractical considering the non-convexity of the cost function. We discuss two practical categories of optimization algorithms: convergent iterative methods and heuristic methods [29], [159].…”

Section: Geometry-based Algorithmsmentioning

confidence: 99%

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A Tutorial on Terahertz-Band Localization for 6G Communication Systems

Chen¹,

Sarieddeen²,

Ballal³

et al. 2021

Preprint

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Terahertz (THz) communications are celebrated as key enablers for converged localization and sensing in future sixth-generation (6G) wireless communication systems and beyond. Instead of being a byproduct of the communication system, localization in 6G is indispensable for location-aware communications. Towards this end, we aim to identify the prospects, challenges, and requirements of THz localization techniques. We first review the history and trends of localization methods and discuss their objectives, constraints, and applications in contemporary communication systems. We then detail the latest advances in THz communications and introduce the THzspecific channel and system models. Afterward, we formulate THz-band localization as a 3D position/orientation estimation problem, detailing geometry-based localization techniques and describing potential THz localization and sensing extensions. We further formulate the offline design and online optimization of THz localization systems, provide numerical simulation results, and conclude by providing insight into interdisciplinary future research directions. Preliminary results illustrate that under the same total transmission power and time, THz-based localization is ∼5 (∼20) times more accurate than mmWave-based localization without (with) prior position information.

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Section: The Motivation and Structure Of This Workmentioning

confidence: 99%

Section: B Localization Techniques 1) Geometry-based and Learning-bas...mentioning

confidence: 99%

Section: Received Signal Model 1) Mimo Architecturesmentioning

confidence: 99%

Section: B Crlb 1)mentioning

confidence: 99%

Section: Geometry-based Algorithmsmentioning

confidence: 99%

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A Tutorial on Terahertz-Band Localization for 6G Communication Systems

Chen¹,

Sarieddeen²,

Ballal³

et al. 2021

Preprint

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Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology

Abd Elaziz,

Al‐qaness,

Dahou

et al. 2023

WIREs Data Min & Knowl

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The sixth generation (6G) represents the next evolution in wireless communication technology and is currently under research and development. It is expected to deliver faster speeds, reduced latency, and greater capacity compared to the current 5G wireless technology. 6G is envisioned as a technology capable of establishing a fully data‐driven network, proficient in analyzing and optimizing end‐to‐end behavior and handling massive volumes of real‐time data at rates of up to terabits per second (Tb/s). Moreover, 6G is designed to accommodate an average of 1000+ substantial connections per person over the course of a decade. The concept of a data‐driven network introduces a new service paradigm, which offers fresh opportunities for applications within 6G wireless communication and network design in the future. This paper aims to provide a survey of existing applications of 6G that are based on deep learning techniques. It also explores the potential, essential technologies, scenarios, challenges, and related topics associated with 6G. These aspects are crucial for meeting the requirements for the development of future intelligent networks. Furthermore, this work delves into various research gaps between deep learning and 6G that remain unexplored. Different potential deep learning applications for 6G networks, including privacy, security, environmentally friendly communication, sustainability, and various wireless applications, are discussed. Additionally, we shed light on the challenges and future trends in this field.This article is categorized under: Technologies > Computational Intelligence Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

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