Integration of Communication and Sensing in 6G: a Joint Industrial and Academic Perspective

Wymeersch, Henk; Shrestha, Deep; Lima, Carlos H. M. de; Yajnanarayana, Vijaya; Richerzhagen, Björn; Keskin, Musa Furkan; Schindhelm, Kim; Ramirez, Alejandro; Wolfgang, Andreas; Guzman, Mar Francis de; Haneda, Katsuyuki; Svensson, Tommy; Baldemair, Robert; Parkvall, Stefan

doi:10.1109/pimrc50174.2021.9569364

Cited by 76 publications

(42 citation statements)

References 50 publications

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“…Hence, it is worthy to explore the natural synergy of sensing, localization, and communication by observing the fact that they share similar signal processing operations (e.g., carrier modulation/demodulation and synchronization) and hardware implementation. Very recently, in the 6G research community, there has been a strong voice to integrate these three into one converged RF system for higher spectrum and energy efficiencies, lower hardware cost/storage, and mutually enhanced functionalities [24], [25].…”

Section: H Integrated Sensing Localization and Communicationmentioning

confidence: 99%

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

et al. 2022

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Section: H Integrated Sensing Localization and Communicationmentioning

confidence: 99%

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

et al. 2022

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“…C. MCRB Derivation for RIS-aided Localization 1) Determining the Pseudo-True Parameter: To derive the MCRB for estimating the UE position under mismatch between the amplitude models for the RIS elements, we should first calculate the η 0 parameter in (10) for the system model described in Section II; that is, we should find the value of η that minimizes the KL divergence between p(y) in ( 6) and p(y|η) in (8). The following lemma characterizes η 0 for the considered system model.…”

Section: B Mcrb Definitionmentioning

confidence: 99%

“…Based on the definition of the KL divergence and the system model in Section II, (10) where the second equality is due to the independence of p(y) from η, and the last equality is obtained from (8). Then, it can be shown that the following equations hold: p(y) y − μ(η)…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 99%

RIS-aided Near-Field Localization under Phase-Dependent Amplitude Variations

Ozturk¹,

Keskin²,

Wymeersch³

et al. 2022

Preprint

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We investigate the problem of reconfigurable intelligent surface (RIS)-aided near-field localization of a user equipment (UE) served by a base station (BS) under phasedependent amplitude variations at each RIS element. Through a misspecified Cramér-Rao bound (MCRB) analysis and a resulting lower bound (LB) on localization, we show that when the UE is unaware of amplitude variations (i.e., assumes unit-amplitude responses), severe performance penalties can arise, especially at high signal-to-noise ratios (SNRs). Leveraging Jacobi-Anger expansion to decouple range-azimuth-elevation dimensions, we develop a low-complexity approximated mismatched maximum likelihood (AMML) estimator, which is asymptotically tight to the LB. To mitigate performance loss due to model mismatch, we propose to jointly estimate the UE location and the RIS amplitude model parameters. The corresponding Cramér-Rao bound (CRB) is derived, as well as an iterative refinement algorithm, which employs the AMML method as a subroutine and alternatingly updates individual parameters of the RIS amplitude model. Simulation results indicate fast convergence and performance close to the CRB. The proposed method can successfully recover the performance loss of the AMML under a wide range of RIS parameters and effectively calibrate the RIS amplitude model online with the help of a user that has an a-priori unknown location.

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“…Besides, as a key technology of 6G network, the integrated sensing and communication (ISAC) technology will enhance the communication performance through sensing information [20], [21]. Due to the higher frequency bands, wider bandwidth and denser distribution of antenna arrays used in 6G, the integration of wireless signal sensing and communication will be possible in a single system.…”

Section: Green V2x Communicationsmentioning

confidence: 99%

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

Wang¹,

Zhu²,

Hossain³

2021

Preprint

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As one of the most promising applications in future Internet of Things, Internet of Vehicles (IoV) has been acknowledged as a fundamental technology for developing the Intelligent Transportation Systems in smart cities. With the emergence of the sixth generation (6G) communications technologies, massive network infrastructures will be densely deployed and the number of network nodes will increase exponentially, leading to extremely high energy consumption. There has been an upsurge of interest to develop the green IoV towards sustainable vehicular communication and networking in the 6G era. However, as a special mobile ad-hoc network, the energy cost in an IoV system involves the communication and computation energy in addition to the fuel consumption and the electricity cost of moving vehicles. Moreover, the energy harvesting technology, which is likely to be adopted widely in 6G systems, will complicate the optimization of energy efficiency in the entire system. Current studies focus only on part of the energy issues in IoV systems without a comprehensive discussion of the state-of-the-art energy-efficient approaches and the influence of the development of 6G networks on green IoV. In this paper, we present the main considerations for green IoV from five different scenarios, including the communication, computation, traffic, Electric Vehicles (EVs), and energy harvesting management. The literatures relevant to each of the scenarios are compared from the perspective of energy optimization (e.g., with respect to resource allocation, workload scheduling, routing design, traffic control, charging management, energy harvesting and sharing, etc.) and the related factors affecting energy efficiency (e.g., resource limitation, channel state, network topology, traffic condition, etc.). In addition, we introduce the potential challenges and the emerging technologies in 6G for developing green IoV systems. Finally, we discuss the research trends in designing energy-efficient IoV systems.

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Integration of Communication and Sensing in 6G: a Joint Industrial and Academic Perspective

Cited by 76 publications

References 50 publications

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

RIS-aided Near-Field Localization under Phase-Dependent Amplitude Variations

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

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