How Does CP Length Affect the Sensing Range for OFDM-ISAC?

Orthogonal frequency division multiplexing (OFDM), which has been the dominating waveform for contemporary wireless communications, is also regarded as a competitive candidate for future integrated sensing and communication (ISAC) systems. Existing works on OFDM-ISAC usually assume that the maximum sensing range should be limited by the cyclic prefix (CP) length since inter-symbol interference (ISI) and inter-carrier interference (ICI) should be avoided. However, in this paper, we provide rigorous analysis to reveal that the random data embedded in OFDM-ISAC signal can actually act as a free ``mask" for ISI, which makes ISI/ICI random and hence greatly attenuated after radar signal processing. The derived signal-to-interference-plus-noise ratio (SINR) in the range profile demonstrates that the maximum sensing range of OFDM-ISAC can greatly exceed the ISI-free distance that is limited by the CP length, which is validated by simulation results. To further mitigate power degradation for long-range targets, a novel sliding window sensing method is proposed, which iteratively detects and cancels short-range targets before shifting the detection window. The shifted detection window can effectively compensate the power degradation due to insufficient CP length for long-range targets. Such results provide valuable guidance for the CP length design in OFDM-ISAC systems.

Integrated Localization and Communication with Sparse MIMO: Will Virtual Array Technology also Benefit Wireless Communication?

For the 6G wireless networks, achieving high-performance integrated localization and communication (ILAC) is critical to unlock the full potential of wireless networks. To simultaneously enhance localization and communication performance cost-effectively, this paper proposes sparse multiple-input multiple-output (MIMO) based ILAC with nested and co-prime sparse arrays deployed at the base station. Sparse MIMO relaxes the traditional half-wavelength antenna spacing constraint to enlarge the antenna aperture, thus enhancing localization degrees of freedom and providing finer spatial resolution. However, it also leads to undesired grating lobes, which may cause severe inter-user interference for communication and angular ambiguity for localization. While the latter issue can be effectively addressed by the virtual array technology, by forming sum or difference co-arrays via signal (conjugate) correlation among array elements, it is unclear whether the similar virtual array technology also benefits wireless communications for ILAC systems. In this paper, we first reveal that the answer to the above question is negative, by showing that forming virtual arrays for wireless communication will cause destruction of phase information, degradation of signal-to-noise ratio and aggravation of multi-user interference. Therefore, we propose the hybrid processing for sparse MIMO based ILAC, i.e., physical array based communication while virtual array based localization. To this end, we characterize the beam pattern of sparse arrays by three metrics, demonstrating that despite of the introduction of grating lobes, sparse arrays can also bring benefits to communications thanks to its narrower main lobe beam width than the conventional compact arrays. Extensive simulation results are presented to demonstrate the performance gains of sparse MIMO based ILAC over that based on the conventional compact MIMO.

A Tutorial on Movable Antennas for Wireless Networks

Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed.

Machine learning for modelling unstructured grid data in computational physics: a review

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field.

Achieving Full-Bandwidth Sensing Performance with Partial Bandwidth Allocation for ISAC

This letter studies an uplink integrated sensing and communication (ISAC) system using discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) transmission. We try to answer the following fundamental question: With only a fractional bandwidth allocated to the user with sensing task, can the same delay resolution and unambiguous range be achieved as if all bandwidth were allocated to it? We affirmatively answer the question by proposing a novel two-stage delay estimation (TSDE) method that exploits the following facts: without increasing the allocated bandwidth, higher delay resolution can be achieved via distributed subcarrier allocation compared to its collocated counterpart, while there is a trade-off between delay resolution and unambiguous range by varying the decimation factor of subcarriers. Therefore, the key idea of the proposed TSDE method is to first perform coarse delay estimation with collocated subcarriers to achieve a large unambiguous range, and then use distributed subcarriers with optimized decimation factor to enhance delay resolution while avoiding delay ambiguity. Our analysis shows that the proposed TSDE method can achieve the full-bandwidth delay resolution and unambiguous range, by using only at most half of the full bandwidth, provided that the channel delay spread is less than half of the unambiguous range. Numerical results show the superiority of the proposed method over the conventional method with collocated subcarriers.

Wireless Communication with Flexible Reflector: Joint Placement and Rotation Optimization for Coverage Enhancement

Passive metal reflectors for communication enhancement have appealing advantages such as ultra low cost, zero energy expenditure, maintenance-free operation, long life span, and full compatibility with legacy wireless systems. To unleash the full potential of passive reflectors for wireless communications, this paper proposes a new passive reflector architecture, termed flexible reflector (FR), for enabling the flexible adjustment of beamforming direction via the FR placement and rotation optimization. We consider the multi-FR aided area coverage enhancement and aim to maximize the minimum expected receive power over all locations within the target coverage area, by jointly optimizing the placement positions and rotation angles of multiple FRs. To gain useful insights, the special case of movable reflector (MR) with fixed rotation is first studied to maximize the expected receive power at a target location, where the optimal single-MR placement positions for electrically large and small reflectors are derived in closed-form, respectively. It is shown that the reflector should be placed at the specular reflection point for electrically large reflector. While for area coverage enhancement, the optimal placement is obtained for the single-MR case and a sequential placement algorithm is proposed for the multi-MR case. Moreover, for the general case of FR, joint placement and rotation design is considered for the single-/multi-FR aided coverage enhancement, respectively. Numerical results are presented which demonstrate significant performance gains of FRs over various benchmark schemes under different practical setups in terms of receive power enhancement.

Generative CKM Construction using Partially Observed Data with Diffusion Model

Channel knowledge map (CKM) is a promising technique that enables environment-aware wireless networks by utilizing location-specific channel prior information to improve communication and sensing performance. A fundamental problem for CKM construction is how to utilize partially observed channel knowledge data to reconstruct a complete CKM for all possible locations of interest. This problem resembles the long-standing ill-posed inverse problem, which tries to infer from a set of limited observations the cause factors that produced them. By utilizing the recent advances of solving inverse problems with generative artificial intelligence (AI), in this paper, we propose generative CKM construction method using partially observed data by solving inverse problems with diffusion models. Simulation results show that the proposed method significantly improves the performance of CKM construction compared with benchmarking schemes.

FFT-Enhanced Low-Complexity Near-Field Super-Resolution Sensing

In this letter, a fast Fourier transform (FFT)-enhanced low-complexity super-resolution sensing algorithm for near-field source localization with both angle and range estimation is proposed. Most traditional near-field source localization algorithms suffer from excessive computational complexity or incompatibility with existing array architectures. To address such issues, this letter proposes a novel near-field sensing algorithm that combines coarse and fine granularity of spectrum peak search. Specifically, a spectral pattern in the angle domain is first constructed using FFT to identify potential angles where sources are present. Afterwards, a 1D beamforming is performed in the distance domain to obtain potential distance regions. Finally, a refined 2D multiple signal classification (MUSIC) is conducted within each narrowed angle-distance region to estimate the precise location of the sources. Numerical results demonstrate that the proposed algorithm can significantly reduce the computational complexity of 2D spectrum peak searches and achieve target localization with high-resolution.

Generating CKM Using Others' Data: Cross-AP CKM Inference with Deep Learning

Channel knowledge map (CKM) is a promising paradigm shift towards environment-aware communication and sensing by providing location-specific prior channel knowledge before real-time communication. Although CKM is particularly appealing for dense networks such as cell-free networks, it remains a challenge to efficiently generate CKMs in dense networks. For a dense network with CKMs of existing access points (APs), it will be useful to efficiently generate CKMs of potentially new APs with only AP location information. The generation of inferred CKMs across APs can help dense networks achieve convenient initial CKM generation, environment-aware AP deployment, and cost-effective CKM updates. Considering that different APs in the same region share the same physical environment, there exists a natural correlation between the channel knowledge of different APs. Therefore, by mining the implicit correlation between location-specific channel knowledge, cross-AP CKM inference can be realized using data from other APs. This paper proposes a cross-AP inference method to generate CKMs of potentially new APs with deep learning. The location of the target AP is fed into the UNet model in combination with the channel knowledge of other existing APs, and supervised learning is performed based on the channel knowledge of the target AP. Based on the trained UNet and the channel knowledge of the existing APs, the CKM inference of the potentially new AP can be generated across APs. The generation results of the inferred CKM validate the feasibility and effectiveness of cross-AP CKM inference with other APs' channel knowledge.

Double-Side Delay Alignment Modulation for Multi-User Millimeter Wave and TeraHertz Communications

Delay alignment modulation (DAM) is an innovative broadband modulation technique well suited for millimeter wave (mmWave) and terahertz (THz) massive multiple-input multiple-output (MIMO) communication systems. Leveraging the high spatial resolution and sparsity of multi-path channels, DAM mitigates inter-symbol interference (ISI) effectively, by aligning all multi-path components through a combination of delay pre/post-compensation and path-based beamforming. As such, ISI is eliminated while preserving multi-path power gains. In this paper, we explore multi-user double-side DAM with both delay pre-compensation at the transmitter and post-compensation at the receiver, contrasting with prior one-side DAM that primarily focuses on delay pre-compensation only. Firstly, we reveal the constraint for the introduced delays and the delay pre/post-compensation vectors tailored for multi-user double-side DAM, given a specific number of delay pre/post-compensations. Furthermore, we show that as long as the number of base station (BS)/user equipment (UE) antennas is sufficiently large, single-side DAM, where delay compensation is only performed at the BS/UE, is preferred than double-side DAM since the former results in less ISI to be spatially eliminated. Next, we propose two low-complexity path-based beamforming strategies based on the eigen-beamforming transmission and ISI-zero forcing (ZF) principles, respectively, based on which the achievable sum rates are studied. Simulation results verify that with sufficiently large BS/UE antennas, single-side DAM is sufficient. Furthermore, compared to the benchmark scheme of orthogonal frequency division multiplexing (OFDM), multi-user BS-side DAM achieves higher spectral efficiency and/or lower peak-to-average power ratio (PAPR).