A cooperative localization-aided tracking algorithm for THz wireless systems

Stratidakis, Giorgos; Boulogeorgos, Alexandros–Apostolos A.; Alexiou, Angeliki

doi:10.1109/wcnc.2019.8885710

Cited by 24 publications

(36 citation statements)

References 20 publications

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“…Furthermore, a tracking approach is developed for time-varying channel modeling in indoor THz communications by using extended Kalman filtering [61]. Cooperation aided localization approaches are also proposed to provide high estimation accuracy and to alleviate the deafness problem in 2D scenarios [62]. In [63], a delay-phase precoding structure is proposed, and a beam zooming mechanism is adopted for THz beam tracking, demonstrating the ability to track multiple users by one RF chain and substantially reduce the beam training overhead.…”

Section: E Terahertz Localizationmentioning

confidence: 99%

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: E Terahertz Localizationmentioning

confidence: 99%

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

Chen¹,

Sarieddeen²,

Ballal³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…This characteristic is expected to influence both beamforming design as well as resource allocation and user association. • The large penetration loss in the THz band, which may surpass 40 or even 50 dB (Kokkoniemi et al, 2016;Stratidakis et al, 2019;Petrov et al, 2020;Stratidakis et al, 2020a;Stratidakis et al, 2020b), renders questionable the establishment of the non-LoS links. As a result, blockage avoidance schemes are needed.…”

Section: Notationsmentioning

confidence: 99%

Machine Learning: A Catalyst for THz Wireless Networks

Boulogeorgos

Yaqub²,

Renzo

et al. 2021

Front. Comms. Net.

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With the vision to transform the current wireless network into a cyber-physical intelligent platform capable of supporting bandwidth-hungry and latency-constrained applications, both academia and industry turned their attention to the development of artificial intelligence (AI) enabled terahertz (THz) wireless networks. In this article, we list the applications of THz wireless systems in the beyond fifth generation era and discuss their enabling technologies and fundamental challenges that can be formulated as AI problems. These problems are related to physical, medium/multiple access control, radio resource management, network and transport layer. For each of them, we report the AI approaches, which have been recognized as possible solutions in the technical literature, emphasizing their principles and limitations. Finally, we provide an insightful discussion concerning research gaps and possible future directions.

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“…Their model relies on a practical user mobility trajectory [24] to develop a time-evolution based framework for the angle of arrival (AoA)/ angle of departure (AoD) of each user. Subsequent contributions in this direction, such as [25] and [26], consider base station (BS) cooperation and a multi-resolution codebook, respectively, for improving the accuracy of channel tracking obtained via the a priori information aided scheme of [12]. However, this improved tracking accuracy is achieved at the cost of inter-BS cooperation, which necessitates additional infrastructure and control overheads.…”

Section: A Related Work and Contributionsmentioning

confidence: 99%

Hybrid Transceiver Design for Tera-Hertz MIMO Systems Relying on Bayesian Learning Aided Sparse Channel Estimation

Srivastava¹,

Tripathi²,

Varshney³

et al. 2021

Preprint

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Hybrid transceiver design in multiple-input multiple-output (MIMO) Tera-Hertz (THz) systems relying on sparse channel state information (CSI) estimation techniques is conceived. To begin with, a practical MIMO channel model is developed for the THz band that incorporates its molecular absorption and reflection losses, as well as its non-line-of-sight (NLoS) rays associated with its diffused components.Subsequently, a novel CSI estimation model is derived by exploiting the angular-sparsity of the THz MIMO channel. Then an orthogonal matching pursuit (OMP)-based framework is conceived, followed by designing a sophisticated Bayesian learning (BL)-based approach for efficient estimation of the sparse THz MIMO channel. The Bayesian Cramer-Rao Lower Bound (BCRLB) is also determined for benchmarking the performance of the CSI estimation techniques developed. Finally, an optimal hybrid transmit precoder and receiver combiner pair is designed, which directly relies on the beamspace domain CSI estimates and only requires limited feedback. Finally, simulation results are provided for quantifying the improved mean square error (MSE), spectral-efficiency (SE) and bit-error rate (BER) performance for transmission on practical THz MIMO channel obtained from the HIgh resolution TRANsmission (HITRAN)-database.

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A cooperative localization-aided tracking algorithm for THz wireless systems

Cited by 24 publications

References 20 publications

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

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

Machine Learning: A Catalyst for THz Wireless Networks

Hybrid Transceiver Design for Tera-Hertz MIMO Systems Relying on Bayesian Learning Aided Sparse Channel Estimation

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