MIMO-OFDM Joint Radar-Communications: Is ICI Friend or Foe?

Keskin, Musa Furkan; Wymeersch, Henk; Koivunen, Visa

doi:10.1109/jstsp.2021.3109431

Cited by 88 publications

(41 citation statements)

References 42 publications

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“…By properly optimizing the target grouping, this hybrid protocol is expected to outperform Co-ISAC and TDM-ISAC protocols in terms of efficiency and/or cost. In general, the three protocol designs have their pros and cons under different scenarios, depending on various factors such as S&C quality-of-service (QoS), the locations of users/targets, and their mobility [11]. How to optimize the protocol design to enhance the S&C performance still needs further investigation.…”

Section: A Single-uav-enabled Isacmentioning

confidence: 99%

UAV-Enabled Integrated Sensing and Communication: Opportunities and Challenges

Meng¹,

Wu²,

Xu³

et al. 2022

Preprint

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Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) has attracted growing research interests towards sixth-generation (6G) wireless networks, in which UAVs are exploited as aerial wireless platforms to provide better coverage and enhanced sensing and communication (S&C) services. However, due to the UAVs' size, weight, and power (SWAP) constraints, controllable mobility, and strong line-of-sight (LoS) air-ground channels, the UAV-enabled ISAC introduces both new opportunities and challenges. This article provides an overview on UAV-enabled ISAC, by presenting various solutions for optimizing the S&C performance. In particular, we first present the UAV-enabled joint sensing and communication, and discuss the UAV maneuver control, wireless resource allocation, and interference management in the cases with single and multiple UAVs. Then, we present two application scenarios to exploit the mutual assistance between S&C, namely sensingassisted UAV communication and communication-assisted UAV sensing. Finally, we highlight several interesting research directions to motivate future work.

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Section: A Single-uav-enabled Isacmentioning

confidence: 99%

UAV-Enabled Integrated Sensing and Communication: Opportunities and Challenges

Meng¹,

Wu²,

Xu³

et al. 2022

Preprint

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“…On the contrary, the communication-centric design normally relies on the well-known OFDM waveform due to its good properties for both communication and sensing [26], [27]. However, both radar-centric and communication-centric designs cannot achieve scalable trade-off between the two function.…”

Section: ) Studies On Isacmentioning

confidence: 99%

NOMA-aided Joint Communication, Sensing, and Multi-tier Computing Systems

Wang¹,

Mu²,

Liu³

et al. 2022

Preprint

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A non-orthogonal multiple access (NOMA)-aided joint communication, sensing, and multi-tier computing (JCSMC) framework is proposed. In this framework, a multi-functional base station (BS) carries out target sensing, while providing edge computing services to the nearby users. To enhance the computation efficiency, the multi-tier computing structure is exploited, where the BS can further offload the computation tasks to a powerful Cloud server (CS). The potential benefits of employing NOMA in the proposed JCSMC framework is investigated, which can maximize the computation offloading capacity and suppress the inter-function interference. Based on the proposed framework, the transmit beamformer of the BS and computation resource allocation at the BS and the CS are jointly optimized to maximize the computation rate subject to the communication-computation causality and the sensing quality constraints.Both partial and binary computation offloading modes are considered: 1) For the partial offloading mode, a weighted minimum mean square error based alternating optimization algorithm is proposed to solve the corresponding non-convex optimization problem. It is proved that a Karush-Kuhn-Tucker optimal solution can be obtained; 2) For the binary offloading mode, the resultant highly-coupled mixedinteger optimization problem is first transformed to an equivalent but more tractable form. Then, the reformulated problem is solved by utilizing the alternating direction method of multipliers approach to obtain a nearly optimal solution. Finally, numerical results verify the effectiveness of the proposed algorithms and reveal that: i) the computation rate can be significantly enhanced by exploiting the multitier computing architecture when the BS is resource-limited; and ii) the proposed NOMA-aided JSCMC framework is superior in multi-function interference management and can achieve high-quality sensing and computing functions simultaneously compared with other benchmark schemes.

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“…The grey dotted line is the trajectory of the agent, and we draw two positions of an agent at t = 1 and t = t . design, power allocation [36], and intercarrier interference utilization [37]. Our main contributions are presented in detail as follows:…”

Section: Introductionmentioning

confidence: 99%

Hybrid Active and Passive Sensing for SLAM in Wireless Communication Systems

Yang,

Wen,

Jin

2022

Preprint

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Integrating sensing functions into future mobile equipment has become an important trend. Realizing different types of sensing and achieving mutual enhancement under the existing communication hardware architecture is a crucial challenge in realizing the deep integration of sensing and communication.In the 5G New Radio context, active sensing can be performed through uplink beam sweeping on the user equipment (UE) side to observe the surrounding environment. In addition, the UE can perform passive sensing through downlink channel estimation to measure the multipath component (MPC) information. This study is the first to develop a hybrid simultaneous localization and mapping (SLAM) mechanism that combines active and passive sensing, in which mutual enhancement between the two sensing modes is realized in communication systems. Specifically, we first establish a common feature associated with the reflective surface to bridge active and passive sensing, thus enabling information fusion. Based on the common feature, we can attain physical anchor initialization through MPC with the assistance of active sensing. Then, we extend the classic probabilistic data association SLAM mechanism to achieve UE localization and continuously refine the physical anchor and target reflections through the subsequent passive sensing. Numerical results show that the proposed hybrid active and passive sensing-based SLAM mechanism can work successfully in tricky scenarios without any prior information on the floor plan, anchors, or agents. Moreover, the proposed algorithm demonstrates significant performance gains compared with active or passive sensing only mechanisms.

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MIMO-OFDM Joint Radar-Communications: Is ICI Friend or Foe?

Cited by 88 publications

References 42 publications

UAV-Enabled Integrated Sensing and Communication: Opportunities and Challenges

UAV-Enabled Integrated Sensing and Communication: Opportunities and Challenges

NOMA-aided Joint Communication, Sensing, and Multi-tier Computing Systems

Hybrid Active and Passive Sensing for SLAM in Wireless Communication Systems

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