Performance Analysis of Joint Sensing and Communication based on 5G New Radio

Pucci, Lorenzo; Matricardi, Elisabetta; Xu, Wen; Giorgetti, Andrea

doi:10.1109/gcwkshps52748.2021.9682124

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…. The rank deficiency of invalids the eigen-decomposition-based DOA estimation in (21). Hence, we propose the Toeplitz reconstruction to compensate the rank of the equivalent signal matrix .…”

Section: Proofmentioning

confidence: 99%

“…In [20], DOA estimation of non-cooperative targets is achieved by incorporating an additional sensing beam, which consists a duplication of the current communication symbols to avoid interfering the communication process. Moreover, the DOA estimation performance within the multibeam framework with different power allocation fraction is analyzed in [21,22]. Different from the above methods which employ the echo of downlink signals from the base station, the uplink signals from a specific mobile user with known location are utilized for DOA estimation without the requirement of full-duplex capability for the transceiver [23].…”

Section: Introductionmentioning

confidence: 99%

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Nonorthogonal waveform assisted DOA estimation for joint MIMO sensing and communication

Lin

Zheng

Zhou

et al. 2023

EURASIP J. Adv. Signal Process.

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Conventional direction-of-arrival (DOA) estimation methods for multiple-input multiple-output (MIMO) joint sensing and communication system normally pursue high estimation accuracy and resolution by imposing orthogonal waveforms. However, such operation results in a deterioration of communication performance. In this paper, we propose a nonorthogonal waveform assisted DOA estimation algorithm, where an augmented virtual array is derived by exploiting the nonorthogonal MIMO communication waveforms, while a high communication rate can still be maintained. To estimate the round-trip sensing channels of each subcarrier, we utilize the transmitted symbols as pilot symbols, and obtain all the channel coefficients with a minimum mean square error solver. A virtual channel matrix can be formulated with these channel coefficients, which can be regarded as the samples of an augmented virtual array. Based on that, the subspace processing can be conducted for DOA estimation with fully nonorthogonal waveforms. Furthermore, the rank deficiency property of the equivalent signal matrix of the virtual array is analyzed when the distance of targets are identical. To address the problem, a Toeplitz reconstruction method is proposed to restore the rank of the rank-deficient equivalent signal matrix for DOA estimation. Simulations show that the proposed nonorthogonal waveform assisted DOA estimation algorithm outperforms the conventional methods in terms of resolution and accuracy, while maintaining a satisfactory computational efficiency.

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Section: Proofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonorthogonal waveform assisted DOA estimation for joint MIMO sensing and communication

Lin

Zheng

Zhou

et al. 2023

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…In [17], the DOA estimation of noncooperative targets is realized by incorporating an additional sensing beam, which consists a duplication of the current communication symbols to avoid interfering the communication process. Moreover, the DOA estimation performance within the multibeam framework with different power allocation fraction is analyzed in [18,19]. Different from the above methods which employ the echo of downlink signals from the base station, the uplink signals from a specific mobile user with known location are utilized for DOA estimation without the requirement of a full-duplex capability of the transceiver [20].…”

Section: Introductionmentioning

confidence: 99%

Nonorthogonal waveform assisted DOA estimation for joint MIMO sensing and communication

Lin

Zheng

Zhou³

et al. 2022

Preprint

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Conventional direction-of-arrival (DOA) estimation methods for multiple-input multiple-output (MIMO) joint sensing and communication system normally pursue high estimation accuracy and resolution by imposing orthogonal waveforms, which however, results in a deterioration of communication performance. In this paper, we propose a nonorthogonal waveform assisted DOA estimation algorithm, where an augmented virtual array is derived for DOA estimation exploiting the nonorthogonal MIMO communication waveforms, while a high communication rate can still be maintained. To estimate the round-trip sensing channels on each subcarrier, we utilize the transmitted symbols as pilot symbols, and obtain all the channel coefficients with a minimum mean square error (MMSE) solver. A virtual channel matrix can be formulated with these channel coefficients, which can be regarded as the samples of the augmented virtual array. Further, the subspace framework of DOA estimation for this nonorthogonal scenario is presented, and the rank deficiency property of the equivalent signal matrix of the virtual array is analyzed when the distance of targets are identical. To address the problem, a suitable Toeplitz reconstruction method is proposed for the rank-deficient equivalent signal matrix. Simulations show that the proposed nonorthogonal waveform assisted DOA estimation algorithm outperforms the conventional DOA estimation methods for joint MIMO sensing and communication system in terms of resolution and accuracy, and is capable of locating targets with both identical and non-identical distance.

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Tracking and Data Fusion in Joint Sensing and Communication Networks

Favarelli

Matricardi

Pucci

et al. 2022

2022 IEEE Globecom Workshops (GC Wkshps)

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In this work, we investigate the performance of a joint sensing and communication (JSC) network consisting of multiple base stations (BSs) that cooperate through a fusion center (FC) to exchange information about the sensed environment while concurrently establishing communication links with a set of user equipments (UEs). Each BS within the network operates as a monostatic radar system, enabling comprehensive scanning of the monitored area and generating range-angle maps that provide information regarding the position of a group of heterogeneous objects. The acquired maps are subsequently fused in the FC. Then, a convolutional neural network (CNN) is employed to infer the category of the targets, e.g., pedestrians or vehicles, and such information is exploited by an adaptive clustering algorithm to group the detections originating from the same target more effectively. Finally, two multi-target tracking algorithms, the probability hypothesis density (PHD) filter and multi-Bernoulli mixture (MBM) filter, are applied to estimate the state of the targets. Numerical results demonstrated that our framework could provide remarkable sensing performance, achieving an optimal sub-pattern assignment (OSPA) less than 60 cm, while keeping communication services to UEs with a reduction of the communication capacity in the order of 10% to 20%. The impact of the number of BSs engaged in sensing is also examined, and we show that in the specific case study, 3 BSs ensure a localization error below 1 m.

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Performance Analysis of Joint Sensing and Communication based on 5G New Radio

Cited by 9 publications

References 12 publications

Nonorthogonal waveform assisted DOA estimation for joint MIMO sensing and communication

Nonorthogonal waveform assisted DOA estimation for joint MIMO sensing and communication

Nonorthogonal waveform assisted DOA estimation for joint MIMO sensing and communication

Tracking and Data Fusion in Joint Sensing and Communication Networks

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