Titlebook: Integrated Sensing and Communications; Fan Liu,Christos Masouros,Yonina C. Eldar Book 2023 The Editor(s) (if applicable) and The Author(s)

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infatuation

Integrated Sensing and Communications: Background and Applicationsook. Prior to that, of particular importance in the development of ISAC is to identify “.”, “.”, and “.”. In this chapter, we aim to answer the above questions by overviewing the definition, driving forces, applications, as well as the standardization progress of ISAC.

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PHY Tradeoff and Resource Allocation for ISACcation schemes based on . and . criteria are considered for the aforementioned sensing services, which can flexibly allocate the limited power and bandwidth resources according to both sensing and communication (S&C) QoSs. The numerical simulations are provided to evaluate the performance trade-off between S&C services.

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ISAC Design Using OTFS WaveformsOTFS based on the DZT. Furthermore, we demonstrate the potentials of using the DZT to bridge the functionalities of both communication and radar sensing. Finally, we provide some preliminary results on ISAC transmissions using OTFS waveforms via leveraging the DZT, where some future research directions are also highlighted.

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Fundamental Limits for ISAC: Information and Communication Theoretic Perspective backscattered signal for sensing while sending data via a common waveform. Then, we extend our study to more realistic setups ranging from imperfect channel state information at receiver, correlated channel states to two-user broadcast and multiple access channels. In the presence of multiple users

桉树

Fundamental Limits for ISAC—Radar Perspectivescription for the quantization is analyzed for the case of a Gaussian signal and a simpler expression for the Cramer-Rao bound (CRB) is obtained. Using the obtained simpler expression of the CRBs, the achievable performance limit for the parameter estimation can be analyzed. Further, the performance

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Fundamental Limits for ISAC—Asymptotics in Massive MIMO Sensing Systems, we considered a co-located MIMO radar having a massive number of virtual spatial antenna channels. We focus on the target detection problem by showing that the massive regime allows for the derivation of a cognitive, robust, reinforcement learning (RL)-based, Wald-type test that guarantees certain

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