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Register a new userIntegrating Low-Complexity and Flexible Sensing into Communication Systems
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Added by
Kai Wu
Publication date
2021
fields
Electronic Engineering
and research's language is
English
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Integrating sensing into standardized communication systems can potentially benefit many consumer applications that require both radio frequency functions. However, without an effective sensing method, such integration may not achieve the expected gains of cost and energy efficiency. Existing sensing methods, which use communication payload signals, either have limited sensing performance or suffer from high complexity. In this paper, we develop a novel and flexible sensing framework which has a complexity only dominated by a Fourier transform and also provides the flexibility in adapting for different sensing needs. We propose to segment a whole block of echo signal evenly into sub-blocks; adjacent ones are allowed to overlap. We design a virtual cyclic prefix (VCP) for each sub-block that allows us to employ two common ways of removing communication data symbols and generate two types of range-Doppler maps (RDMs) for sensing. We perform a comprehensive analysis of the signal components in the RDMs, proving that their interference-plus-noise (IN) terms are approximately Gaussian distributed. The statistical properties of the distributions are derived, which leads to the analytical comparisons between the two RDMs as well as between the prior and our sensing methods. Moreover, the impact of the lengths of sub-block, VCP and overlapping signal on sensing performance is analyzed. Criteria for designing these lengths for better sensing performance are also provided. Extensive simulations validate the superiority of the proposed sensing framework over prior methods in terms of signal-to-IN ratios in RDMs, detecting performance and flexibility.
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OFDM sensing is gaining increasing popularity in wideband radar applications as well as in joint communication and radar/radio sensing (JCAS). As JCAS will potentially be integrated into future mobile networks where OFDM is crucial, OFDM sensing is envisioned to be ubiquitously deployed. A fast Fourier transform (FFT) based OFDM sensing (FOS) method was proposed a decade ago and has been regarded as a de facto standard given its simplicity. In this article, we introduce an easy trick -- a pre-processing on target echo -- to further reduce the computational complexity of FOS without degrading key sensing performance. Underlying the trick is a newly disclosed feature of the target echo in OFDM sensing which, to the best of our knowledge, has not been effectively exploited yet.
In this paper, we study optimal waveform design to maximize mutual information (MI) for a joint communication and (radio) sensing (JCAS, a.k.a., radar-communication) multi-input multi-output (MIMO) downlink system. We consider a typical packet-based signal structure which includes training and data symbols. We first derive the conditional MI for both sensing and communication under correlated channels by considering the training overhead and channel estimation error (CEE). Then, we derive a lower bound for the channel estimation error and optimize the power allocation between the training and data symbols to minimize the CEE. Based on the optimal power allocation, we provide optimal waveform design methods for three scenarios, including maximizing MI for communication only and for sensing only, and maximizing a weighted sum MI for both communication and sensing. We also present extensive simulation results that provide insights on waveform design and validate the effectiveness of the proposed designs.
Recent advances in wireless communication and solid-state circuits together with the enormous demands of sensing ability have given rise to a new enabling technology, integrated sensing and communications (ISAC). The ISAC captures two main advantages over dedicated sensing and communication functionalities: 1) Integration gain to efficiently utilize congested resources, and even, 2) Coordination gain to balance dual-functional performance or/and perform mutual assistance. Meanwhile, triggered by ISAC, we are also witnessing a paradigm shift in the ubiquitous IoT architecture, in which the sensing and communication layers are tending to converge into a new layer, namely, the signaling layer. In this paper, we first attempt to introduce a definition of ISAC, analyze the various influencing forces, and present several novel use cases. Then, we complement the understanding of the signaling layer by presenting several key benefits in the IoT era. We classify existing dominant ISAC solutions based on the layers in which integration is applied. Finally, several challenges and opportunities are discussed. We hope that this overview article will serve as a primary starting point for new researchers and offer a birds-eye view of the existing ISAC-related advances from academia and industry, ranging from solid-state circuitry, signal processing, and wireless communication to mobile computing.
Realizing edge intelligence consists of sensing, communication, training, and inference stages. Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time. This paper proposes to accelerate edge intelligence via integrated sensing and communication (ISAC). As such, the sensing and communication stages are merged so as to make the best use of the wireless signals for the dual purpose of dataset generation and uploading. However, ISAC also introduces additional interference between sensing and communication functionalities. To address this challenge, this paper proposes a classification error minimization formulation to design the ISAC beamforming and time allocation. Globally optimal solution is derived via the rank-1 guaranteed semidefinite relaxation, and performance analysis is performed to quantify the ISAC gain. Simulation results are provided to verify the effectiveness of the proposed ISAC scheme. Interestingly, it is found that when the sensing time dominates the communication time, ISAC is always beneficial. However, when the communication time dominates, the edge intelligence with ISAC scheme may not be better than that with the conventional scheme, since ISAC introduces harmful interference between the sensing and communication signals.
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