System-Level Analysis of Joint Sensing and Communication Based on 5G New Radio

Pucci, Lorenzo; Giorgetti, Andrea

doi:10.1109/jsac.2022.3155522

Cited by 71 publications

(45 citation statements)

References 43 publications

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“…For each transmission, a subarray is randomly activated, and the receiver obtains the target information from randomness incurred grading lobes. In [89], Pucci et al evaluated the sensing performance of the 5G new radio. Instead of directly using communication signals, communication and sensing beams are spatially separated and the power of orthogonal frequency division multiplexing (OFDM) signals is split for these two functions.…”

Section: B Communication and Sensing Integrationmentioning

confidence: 99%

Joint Communication and Sensing: Models and Potential of Using MIMO

Fang¹,

Feng²,

Chen³

et al. 2022

Preprint

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The sixth-generation (6G) network is envisioned to integrate communication and sensing functions, so as to improve the spectrum efficiency (SE) and support explosive novel applications. Although the similarities of wireless communication and radio sensing lay the foundation for their combinations, their different requirements for electromagnetic signals make the joint system design a hard task. To simultaneously guarantee sensing accuracy and communication capacity, the multipleinput and multiple-output (MIMO) technique plays an important role, due to its unique capability of spatial beamforming and waveform shaping. However, the configuration of MIMO also brings high hardware cost, high power consumption, and high signal processing complexity. How to efficiently apply MIMO in the joint communication and sensing (JCAS) system is still open. In this survey, we discuss JCAS in the context of MIMO configurations. We first outline the roles of MIMO in the progress of communication and radar sensing. Then, we review current advances in both communication and sensing coexistence and integration in detail. Three novel JCAS MIMO models are subsequently discussed by introducing the promising 6G enablers, i.e., the unmanned aerial vehicle (UAV) and the reconfigurable intelligent surface (RIS). With the aim of building a compatible dual-function system, the benefits and challenges of MIMO in JCAS are summarized in each subsection. Promising solutions are also discussed from the system perspective with simple, intelligent and robust principles. In the end, open issues are outlined to envisage a comprehensive JCAS network in the near future.

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Section: B Communication and Sensing Integrationmentioning

confidence: 99%

Joint Communication and Sensing: Models and Potential of Using MIMO

Fang¹,

Feng²,

Chen³

et al. 2022

Preprint

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show abstract

“…Specifically, the (n t N r + n r )-th element in i[z, m] serves as the m-th symbol received at the (n t N r + n r )-th virtual array antenna. In particular, we note that the virtual array formed in (7) does not correspond to a uniform linear array (ULA) since the i[z, m] is calculated with symbols on various subcarriers with different propagation delays, i.e., the first term in the Hadamard product in (7). Likewise, by substituting P in (6) Here, each column in Ṕ (:, 1 : N t ) has only one non-zero element, which means that each transmit antenna possesses an exclusive subcarrier.…”

Section: Joint Mimo Sensing and Communication Systemmentioning

confidence: 99%

“…Likewise, by substituting P in (6) Here, each column in Ṕ (:, 1 : N t ) has only one non-zero element, which means that each transmit antenna possesses an exclusive subcarrier. Hence, the formulation of virtual array is available in the same way as (7) when z = 0.…”

Section: Joint Mimo Sensing and Communication Systemmentioning

confidence: 99%

“…However, in the considered fully nonorthogonal signal model as defined in (1) and ( 2), there are no exclusive subcarriers for each transmit antenna. As a result, the symbols transmitted by different antennas are mixed together in d[k, m], such that the construction of virtual array in (7) is no longer valid.…”

Section: Joint Mimo Sensing and Communication Systemmentioning

confidence: 99%

“…is the steering vector corresponding to the q-th noncooperative target. Unlike the virtual array formed in (7), the augmented virtual array S corresponds to a single subcarrier, leading to the same propagation delay of all the virtual array antennas. Thus, the augmented virtual array S is a ULA.…”

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confidence: 99%

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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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Hardware softwareSoCco‐design analysis and implementation ofMIMO‐OFDMfor4G/5G/6G eNodeBapplications

Dessai,

Patidar

2024

Trans Emerging Tel Tech

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With the emerging challenges for the data rate requirements of 5G/6G applications and reusing the 4G infrastructure for 5G, it is necessary to understand the System‐on‐Chip (SoC) platform‐based embedded co‐design and implementation of the programmable and reconfigurable MIMO‐OFDM system. For both uplink and downlink data transmissions, these applications require a larger data throughput as well as reduced bit error rates, latency, and increased spectral efficiency. This work describes the co‐design and development of hardware and software for the MIMO‐OFDM algorithms for 5G and 6G eNodeBs. An efficient design through computer architecture based on pipeline and parallelization using systolic and CORDIC has been applied for the IP development of the sub‐components of the MIMO‐OFDM systems. A Zynq platform with computing resources including PS, Mali GPU‐400, and PL is utilized to increase the data rate for MIMO‐OFDM system architecture co‐design and implementation. The architecture approach used in this work enabled a data rate of 10–50 Gbps and beyond reaching Tbps based on the system's programmability and reconfigurability with an efficient SoC platform design. The design platform provides a programming feature such as MIMO‐OFDM, OFDM, and MIMO without OFDM through software programming for the range of applications of the desired data rates. With 64‐QAM modulation, the three channels' observed performance in the predicted multipath channel velocity of 15 km/h for pedestrians, vehicles, and AWGN is seen in simulation. To reach the application clock frequencies, the device's PLL (ZUI7EG) upscales and downscales clock frequencies from 750 to 1600 MHz using a configurable register. When the system is configured to operate as MIMO‐OFDM or OFDM in order to get an execution throughput of 300 msec and a data throughput ranging from 71 Gbps to 1749 Gbps using 2 × 2/4 × 4 configurations. The device scalability depends on at present devices of advanced embedded reconfigurable architecture platform. Massive MIMO and multi‐user MIMO will be used in the future to increase throughput and data rates. Additionally, future work will focus on creating a MIMO‐OFDM hardware‐software embedded architecture and testbed to enhance implementation and verification of the vehicle and pedestrian.

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System-Level Analysis of Joint Sensing and Communication Based on 5G New Radio

Cited by 71 publications

References 43 publications

Joint Communication and Sensing: Models and Potential of Using MIMO

Joint Communication and Sensing: Models and Potential of Using MIMO

Nonorthogonal waveform assisted DOA estimation for joint MIMO sensing and communication

Hardware softwareSoCco‐design analysis and implementation ofMIMO‐OFDMfor4G/5G/6G eNodeBapplications

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