PHY1 – Integrated Sensing and Communication
Tuesday, 4 June 2024, 11:00-13:00, room Gorilla 3
Session Chair: Ahmad Nimr (Technische Universität Dresden, DE)
Constant Modulus Constrained Codebook Synthesis for Self-Interference Suppression in Integrated Sensing and Communication at mWave
Guang Chai (Technical University of Berlin & Huawei Technologies Duesseldorf GmbH, Germany); Zhibin Yu (Huawei Technologies Duesseldorf GmbH, Germany); Muhammad Nabeel (Huawei, Germany); Xiaofeng Wu (Huawei Technol. Duesseldorf GmbH, Germany); Giuseppe Caire (Technische Universität Berlin, Germany)
Self-interference (SI) is one of the most critical issues for an integrated sensing and communication (ISAC) device. This paper presents an efficient codebook synthesis method which suppresses the SI by using the analog phase shifters in a millimeter wave (mmWave) ISAC device. First, we formulate the problem as a signal-to-interference-and-noise ratio (SINR) maximization problem with constant modulus (CM) constraints. Then we propose a two-step method wherein the solution is initialized by a minimum variance distortionless response (MVDR) beamformer with imposed CM constraint, and then refined by a local search procedure using constant modulus gradient descent (CM-GD). Simulations have been conducted for realistic TX/RX antenna array placements for a cellular user equipment (UE). The results show that the proposed method achieves a much better performance than a sub-optimal MVDR beamformer which has imposed CM constraint. Meanwhile, the proposed method can almost approximate the performance of the optimal MVDR beamformer which does not have the CM constraint.
Distributed Intelligent Integrated Sensing and Communications: The 6G-DISAC Approach
Emilio Calvanese Strinati (CEA-LETI, France); George C. Alexandropoulos (University of Athens, Greece); Giyyarpuram Madhusudan (Orange Labs, France); Philippe Sehier (Nokia Standards, France); Sami Mekki (Nokia Networks France, France); Vincenzo Sciancalepore (NEC Laboratories Europe GmbH, Germany); Maximilian Stark (Robert Bosch GmbH, Germany); Mohamed Sana (CEA LETI Grenoble, France); Benoit Denis (CEA-Leti Minatec, France); Maurizio Crozzoli (Telecom Italia, Italy); Navid Amani (Chalmers University of Technology, Sweden); Placido Mursia (NEC Laboratories Europe GmbH, Germany); Raffaele D’Errico (CEA, LETI & Université Grenoble-Alpes, France); Mauro Boldi (Telecom Italia, Italy); Francesca Costanzo (CEA Leti, France); Francois Rivet (University of Bordeaux, France); Henk Wymeersch (Chalmers University of Technology, Sweden)
This paper introduces the concept of Distributed Intelligent integrated Sensing and Communications (DISAC), which expands the capabilities of Integrated Sensing and Communications (ISAC) towards distributed architectures. Additionally, the DISAC framework integrates novel waveform design with new semantic and goal-oriented communication paradigms, enabling ISAC technologies to transition from traditional data fusion to the semantic composition of diverse sensed and shared information. This progress facilitates large-scale, energy-efficient support for high-precision spatial-temporal processing, optimizing ISAC resource utilization, and enabling effective multi-modal sensing performance. Addressing key challenges such as efficient data management and connect-compute resource utilization, 6G-DISAC stands to revolutionize applications in diverse sectors including transportation, healthcare, and industrial automation. Our study encapsulates the project’s vision, methodologies, and potential impact, marking a significant stride towards a more connected and intelligent world.
Compressed Sensing-Based Terahertz Imaging Algorithm in 6G-ISAC Terminal Systems
Jue Lyu (Huawei Technologies Co., Ltd., China); Qiao Liu (Huawei Technologies co. ltd., China); Guangjian Wang (Huawei Technologies Co., Ltd., China)
As an important embodiment of integrated sensing and communication (ISAC), terminal imaging using orthogonal frequency division multiplexing (OFDM) communication waveforms in terahertz (THz) band is an important application component of future the sixth generation (6G) networks, because of the THz band has very small wavelengths and high imaging resolution combined with virtual array imaging technology. It means that THz terminal system can get high-resolution images as long as moving through space. However, OFDM-THz imaging system with fully sampling strategy exists an unbearable longterm time cost since the fully sampling spatial step length needs no more than half of the wavelength. How to achieve high-efficiency and high-quality image reconstruction under the premise of reducing the cost of sampling time is a difficult problem to overcome. Compressed sensing (CS) theory is a rapidly developing theory in recent years, which can reconstruct signal from under-sampled data by utilizing sparsity of signal. In this paper, a random under-sampling motion policy is applied for reducing time cost, and a CS-based algorithm is proposed for reconstructing image from under-sampled echo data. The algorithm utilizes the sparse prior information of image discrete gradient domain, decomposes sparse operators, and constructs several sub-problems that can be solved in parallel. A iterative residual correction term is used to realize fast iteration convergence of the algorithm, thus improving the convergence speed. The validity of the proposed algorithm is proved by theoretical analysis and experimental verification. With the comparison of fast parallel proximal algorithm (FPPA), it can be obvious that the proposed algorithm has both higher convergence speed and superior image quality under structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR).
Sequential MAP Parametric OFDM Channel Estimation for Joint Sensing and Communication
Enrique T. R. Pinto and Markku Juntti (University of Oulu, Finland)
Uplink sensing is still a relatively unexplored scenario in integrated sensing and communication which can be used to improve positioning and sensing estimates. We introduce a pilot-based maximum likelihood, and a maximum a posteriori parametric channel estimation procedure using an Orthogonal Frequency Division Multiplexing (OFDM) waveform in uplink sensing. The algorithm is capable of estimating the multipath components of the channel, such as the angles of arrival, departure, path coefficient, and the delay and Doppler terms. As an advantage, when compared to other existing methods, the proposed procedure presents expressions for exact alternating coordinate updates, which can be further improved to achieve a competitive multipath channel estimation tool.
Cooperative Sensing of Side Lobes Interference for mmWave Blockages Localization and Mapping
Hiba Dakdouk (CEA-LETI, France); Mohamed Sana (CEA LETI Grenoble, France); Benoit Denis (CEA-Leti Minatec, France)
Radio localization and sensing are anticipated to play a crucial role in enhancing radio resource management in future networks. In this work, we focus on millimeter-wave communications, which are highly vulnerable to blockages, leading to severe attenuation and performance degradation. In a previous work, we proposed a novel mechanism that senses the radio environment to estimate the angular position of a moving blocker with respect to the sensing node. Building upon this foundation, this paper investigates the benefits of cooperation between different entities in the network by sharing sensed data to jointly locate the moving blocker while mapping the interference profile to probe the radio environment. Numerical evaluations demonstrate that cooperative sensing can achieve a more precise location estimation of the blocker as it further allows accurate estimation of its distance rather than its relative angular position only, leading to effective assessment of the blocker direction, trajectory and possibly, its speed, and size.
CRAP Part II: Clutter Removal with Continuous Acquisitions Under Phase Noise
Marcus Henninger (University of Stuttgart & Nokia Bell Labs, Germany); Silvio Mandelli and Artjom Grudnitsky (Nokia Bell Labs, Germany); Stephan ten Brink (University of Stuttgart, Germany)
The mitigation of clutter is an important research branch in Integrated Sensing and Communication (ISAC), one of the emerging technologies of future cellular networks. In this work, we extend our previously introduced method Clutter Removal with Acquisitions Under Phase Noise (CRAP) by means to track clutter over time. This is necessary in scenarios that require high reliability but can change dynamically, like safety applications in factory floors. To that end, exponential smoothing is leveraged to process new measurements and previous clutter information in a unique matrix using the singular value decomposition, allowing adaptation to changing environments in an efficient way.We further propose a singular value threshold based on the Marchenko-Pastur distribution to select the meaningful clutter components. Results from both simulations and measurements show that continuously updating the clutter components with new acquisitions according to our proposed algorithm Smoothed CRAP (SCRAP) enables coping with dynamic clutter environments and facilitates the detection of sensing targets.
Bayesian Learning for Sparse Parameter Estimation in OTFS-Aided mmWave MIMO Radar Systems
Meesam Jafri (IIT KANPUR, India); Suraj Srivastava (Indian Institute of Technology Jodhpur, India); Aditya K Jagannatham (Indian Institute of Technology Kanpur, India)
This paper proposes an orthogonal time-frequency space (OTFS) modulation aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) phased-array radar (OmM-PAR) system for sparse radar target parameter estimation. Initially, we derive the delay-Doppler (DD)-domain end-to-end input-output model for the OmM-PAR system, which employs a single RF chain (RFC) both at the radar transmitter and receiver (R-TRX). Subsequently, a Bayesian learning (BL)-based procedure is developed for improved sparse radar target parameter estimation. Finally, our simulation results illustrate the enhanced performance of the proposed parameter learning framework for OmM-PAR systems. Furthermore, the performance of the proposed scheme is also benchmarked against the Bayesian Cramér-Rao lower bounds (BCRLB).
