Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond

Liu, Fan; Cui, Yuanhao; Masouros, Christos; Xu, Jie; Han, Tony Xiao; Eldar, Yonina C.; Buzzi, Stefano

doi:10.48550/arxiv.2108.07165

Cited by 27 publications

(49 citation statements)

References 165 publications

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“…Then, the resulting vector in a frame is reconstructed as a sensing data matrix Y k ∈ C Nc×M , where N c = T chirp F s . In this matrix, each column vector represents a set of complex-valued baseband samples from a single chirp sampled at the rate F s along the fast-time axis, which can be used to extract range information; each row vector contains complex-valued baseband samples in M different chirps from the same range bin, which can be used to extract Doppler information along the slow-time axis [6].…”

Section: B Signal Preprocessing At Receivermentioning

confidence: 99%

“…On the other hand, wireless sensing has emerged as a new promising sensing technique as it can work under any weather or light conditions and its collected information is less sensitive than commonly used camera sensors [5]. Wireless sensing can also be easily integrated with wireless communications to enable the so-called integrated sensing and communication (ISAC), which can significantly enhance the spectrum and hardware utilization efficiency [6]. By utilizing advanced machine learning approaches, such as support vector machine (SVM) and deep learning (DL), wireless sensing has been successfully applied in applications such as human motion recognition, sleep monitoring, and gait recognition [7].…”

Section: Introductionmentioning

confidence: 99%

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Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication

Liu¹,

Zhu²,

Jiang³

et al. 2022

Preprint

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This letter studies a vertical federated edge learning (FEEL) system for collaborative objects/human motion recognition by exploiting the distributed integrated sensing and communication (ISAC). In this system, distributed edge devices first send wireless signals to sense targeted objects/human, and then exchange intermediate computed vectors (instead of raw sensing data) for collaborative recognition while preserving data privacy. To boost the spectrum and hardware utilization efficiency for FEEL, we exploit ISAC for both target sensing and data exchange, by employing dedicated frequency-modulated continuous-wave (FMCW) signals at each edge device. Under this setup, we propose a vertical FEEL framework for realizing the recognition based on the collected multi-view wireless sensing data. In this framework, each edge device owns an individual local L-model to transform its sensing data into an intermediate vector with relatively low dimensions, which is then transmitted to a coordinating edge device for final output via a common downstream S-model. By considering a human motion recognition task, experimental results show that our vertical FEEL based approach achieves recognition accuracy up to 98% with an improvement up to 8% compared to the benchmarks, including on-device training and horizontal FEEL.

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Section: B Signal Preprocessing At Receivermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication

Liu¹,

Zhu²,

Jiang³

et al. 2022

Preprint

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“…Therefore, in contrast to the past five generations of wireless networks that mainly support wireless communication, the B5G and 6G require a paradigm shift to the integration of multiple functions including communications, sensing, control, and computing [3]. Based on this vision, the concept of integrated sensing and communication (ISAC) 1 has emerged and attracted growing attention in both academia [4], [5] and industries [6], [7]. The goal of ISAC is to integrate the communication and radar sensing via the same platform and the same spectrum, which is capable of increasing resource efficiency and achieving mutual benefits.…”

Section: Introductionmentioning

confidence: 99%

“…The goal of ISAC is to integrate the communication and radar sensing via the same platform and the same spectrum, which is capable of increasing resource efficiency and achieving mutual benefits. On the one hand, the resources for communication and radar sensing are shared in the ISAC, thus improving the utilization efficiency in terms of spectrum, hardware, energy, and costs [4]. On the other hand, the two functions can be interplayed with each other for realizing the win-win operations, such as the sensing-assisted pilot-free communication channel estimation [5] and the communication-assisted high-precision sensing [4].…”

Section: Introductionmentioning

confidence: 99%

NOMA Inspired Integrated Sensing and Communication

Wang¹,

Mu²,

Liu³

et al. 2022

Preprint

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A non-orthogonal multiple access (NOMA)-inspired integrated sensing and communication (ISAC) framework is proposed, where a dual-functional base station (BS) transmits the composite unicast communication signal and sensing signal. In contrast to treating the sensing signal as the harmful interference to communication, in this work, multiple beams of the sensing signal are employed to convey multicast information following the concept of NOMA. Then, each communication user receives multiple multicast streams and one desired unicast stream, which are detected with the aid of successive interference cancellation (SIC). Based on the proposed framework, a multiple-objective optimization problem (MOOP) is formulated for designing the transmit beamforming subject to the total transmit power constraint, which characterizes the trade-off between the communication throughput and sensing beampattern accuracy. For the general multiple-user scenario, the formulated MOOP is firstly converted to a single-objective optimization problem (SOOP) via the -constraint method. Then, a double-layer block coordinate descent (BCD) algorithm is proposed by employing fractional programming (FP) and successive convex approximation (SCA) to find a high-quality suboptimal solution. For the special single-user scenario, the globally optimal solution can be obtained by transforming the MOOP to a convex quadratic semidefinite program (QSDP). Moreover, it is rigorously proved that 1) in the multiple-user scenario, the proposed NOMA-inspired ISAC framework always outperform the state-ofthe-art sensing-interference-cancellation (SenIC) ISAC frameworks by further exploiting sensing signal for delivering information; 2) in the special single-user scenario, the proposed NOMA-inspired ISAC framework achieves the same performance as the existing SenIC ISAC frameworks, which reveals that the coordination of sensing interference is not necessarily required in this case. Numerical results verify

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“…The first is integrated sensing and communication (ISAC), which utilizes the same spectrum bandwidth for both radar sensing and data communication [2]. Prompted by ISAC, the spectrum resources for radar sensing can be exploited for data communication [3]. The second is overthe-air computation (AirComp), which realizes fast wireless data aggregation by simultaneous transmissions and exploiting analog-wave addition in a multi-access channel [4].…”

Section: Introductionmentioning

confidence: 99%

Integrated Sensing, Communication, and Computation Over-the-Air: MIMO Beamforming Design

Li¹,

Liu²,

Zhou³

et al. 2022

Preprint

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To support the unprecedented growth of the Internet of Things (IoT) applications and the access of tremendous IoT devices, two new technologies emerge recently to overcome the shortage of spectrum resources. The first one, known as integrated sensing and communication (ISAC), aims to share the spectrum bandwidth for both radar sensing and data communication. The second one, called over-theair computation (AirComp), enables simultaneous transmission and computation of data from multiple IoT devices in the same frequency. The promising performance of ISAC and AirComp motivates the current work on developing a framework that combines the merits of both called integrated sensing and AirComp (ISAA). Two schemes are designed to support multiple-input-multiple-output (MIMO) ISAA simultaneously, namely the shared and separated schemes. The performance metrics of radar sensing and AirComp are evaluated by the mean square errors of the estimated target response matrix and the received computation results, respectively. The design challenge of MIMO ISAA lies in the joint optimization of radar sensing beamformers and data transmission beamformers at the IoT devices, and data aggregation beamformer at the server, which results in complex non-convex problem. To solve this problem, an algorithmic solution based on the technique of semidefinite relaxation is proposed. The results reveal that the beamformer at each sensor needs to account for supporting dual-functional signals in the shared scheme, while dedicated beamformers for sensing and AirComp are needed to mitigate the mutual interference between the two functionalities in the separated scheme. The use case of target location estimation based on ISAA is demonstrated in simulation to show the performance superiority.

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Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond

Cited by 27 publications

References 165 publications

Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication

Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication

NOMA Inspired Integrated Sensing and Communication

Integrated Sensing, Communication, and Computation Over-the-Air: MIMO Beamforming Design

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