Rethinking Integrated Sensing and Communication: When Near Field Meets Wideband

This article re-examines integrated sensing and communication (ISAC) systems operating in the near-field region of a large antenna array while exploiting a large bandwidth. We first reveal the fundamental characteristics of wideband sensing and communication (S&C) channels and highlight the key changes that occur during the transition from the far-field to the near-field region. Specifically, there are two fundamental changes in the near-field region: strong angular-delay correlation and element-specific Doppler frequencies. It is highlighted that the near-field effect can enable the wideband-like S&C functionalities in terms of signal multiplexing and range sensing due to the strong angular-delay correlation, thus allowing the trading of large antenna arrays for large bandwidths. Furthermore, it also introduces the wideband-unattainable functionalities in high mobility S&C scenarios by leveraging the element-specific Doppler frequencies. We then delineate certain paradigm shifts in thinking required to advance toward near-field wideband ISAC systems, with a particular emphasis on resource allocation, antenna array arrangement, and transceiver architecture. Lastly, some other promising directions are discussed.

Near-Field Velocity Sensing and Predictive Beamforming

The novel concept of near-field velocity sensing is proposed. In contrast to far-field velocity sensing, near-field velocity sensing enables the simultaneous estimation of both radial and transverse velocities of a moving target. A maximum-likelihood-based method is proposed for jointly estimating the radial and transverse velocities from the echo signals. Assisted by near-field velocity sensing, a predictive beamforming framework is proposed for a moving communication user, which requires no channel estimation but achieves seamless data transmission. Finally, numerical examples validate the proposed approaches.

Near-Field Integrated Sensing, Positioning, and Communication: A Downlink and Uplink Framework

A near-field integrated sensing, positioning, and communication (ISPAC) framework is proposed, where a base station (BS) simultaneously serves multiple communication users and carries out target sensing and positioning. A novel double-array structure is proposed to enable the near-field ISPAC at the BS. Specifically, a small-scale assisting transceiver (AT) is attached to the large-scale main transceiver (MT) to empower the communication system with the ability of sensing and positioning. Based on the proposed framework, the joint angle and distance Cram\'er-Rao bound (CRB) is first derived. Then, the CRB is minimized subject to the minimum communication rate requirement in both downlink and uplink ISPAC scenarios: 1) For downlink ISPAC, a downlink target positioning algorithm is proposed and a penalty dual decomposition (PDD)-based double-loop algorithm is developed to tackle the non-convex optimization problem. 2) For uplink ISPAC, an uplink target positioning algorithm is proposed and an efficient alternating optimization algorithm is conceived to solve the non-convex CRB minimization problem with coupled user communication and target probing design. Both proposed optimization algorithms can converge to a stationary point of the CRB minimization problem. Numerical results show that: 1) The proposed ISPAC system can locate the target in both angle and distance domains merely relying on single BS and limited bandwidths; and 2) the positioning performance achieved by the hybrid-analog-and-digital ISPAC approaches that achieved by fully digital ISPAC when the communication rate requirement is not stringent.

Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks

Extremely large-scale multiple-input-multiple output (XL-MIMO) is a promising technology to achieve high spectral efficiency (SE) and energy efficiency (EE) in future wireless systems. The larger array aperture of XL-MIMO makes communication scenarios closer to the near-field region. Therefore, near-field resource allocation is essential in realizing the above key performance indicators (KPIs). Moreover, the overall performance of XL-MIMO systems heavily depends on the channel characteristics of the selected users, eliminating interference between users through beamforming, power control, etc. The above resource allocation issue constitutes a complex joint multi-objective optimization problem since many variables and parameters must be optimized, including the spatial degree of freedom, rate, power allocation, and transmission technique. In this article, we review the basic properties of near-field communications and focus on the corresponding "resource allocation" problems. First, we identify available resources in near-field communication systems and highlight their distinctions from far-field communications. Then, we summarize optimization tools, such as numerical techniques and machine learning methods, for addressing near-field resource allocation, emphasizing their strengths and limitations. Finally, several important research directions of near-field communications are pointed out for further investigation.

On the Performance of Near-Field ISAC

The technical trends for the next-generation wireless network significantly extend the near-field region, necessitating a reevaluation for the performance of integrated sensing and communications (ISAC) to account for the effects introduced by the near field. In this paper, a near-field ISAC framework is proposed with a more accurate channel model than the three conventional models (TCMs): uniform plane wave, uniform spherical wave, and non-uniform spherical wave, in which the effective aperture of the antenna is considered. Based on the proposed model, sensing and communication (S&C) performance in both downlink and uplink scenarios are analyzed. For the downlink case, three distinct designs are studied: the communications-centric (C-C) design, the sensing-centric (S-C) design, and the Pareto optimal design. Regarding the uplink case, the C-C design, the S-C design and the time-sharing strategy are considered. Within each design, sensing rates (SRs) and communication rates (CRs) are derived. To gain further insights, high signal-to-noise ratio slopes and rate scaling laws concerning the number of antennas are also examined. Finally, the attainable SR-CR regions of the near-field ISAC are characterized. Numerical results reveal that 1) as the number of antennas grows, the SRs and CRs of the proposed model converges to constants, while those of the TCMs increase unboundedly; 2) ISAC achieves a more extensive rate region than the conventional frequency-division S&C in both downlink and uplink cases.

Near-field Integrated Sensing and Communication: Opportunities and Challenges

With the extremely large-scale array XL-array deployed in future wireless systems, wireless communication and sensing are expected to operate in the radiative near-field region, which needs to be characterized by the spherical rather than planar wavefronts. Unlike most existing works that considered far-field integrated sensing and communication (ISAC), we study in this article the new near-field ISAC, which integrates both functions of sensing and communication in the near-field region. To this end, we first discuss the appealing advantages of near-field communication and sensing over their far-field counterparts, respectively. Then, we introduce three approaches for near-field ISAC, including joint near-field communication and sensing, sensing-assisted near-field communication, and communication-assisted near-field sensing. We discuss their individual research opportunities, new design issues, as well as propose promising solutions. Finally, several important directions in near-field ISAC are also highlighted to motivate future work.

A unified framework for STAR-RIS coefficients optimization

Simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS), which serves users located on both sides of the surface, has recently emerged as a promising enhancement to the traditional reflective only RIS. Due to the lack of a unified comparison of communication systems equipped with different modes of STAR-RIS and the performance degradation caused by the constraints involving discrete selection, this paper proposes a unified optimization framework for handling the STAR-RIS operating mode and discrete phase constraints. With a judiciously introduced penalty term, this framework transforms the original problem into two iterative subproblems, with one containing the selection-type constraints, and the other subproblem handling other wireless resource. Convergent point of the whole algorithm is found to be at least a stationary point under mild conditions. As an illustrative example, the proposed framework is applied to a sum-rate maximization problem in the downlink transmission. Simulation results show that the algorithms from the proposed framework outperform other existing algorithms tailored for different STAR-RIS scenarios. Furthermore, it is found that 4 or even 2 discrete phases STAR-RIS could achieve almost the same sum-rate performance as the continuous phase setting, showing for the first time that discrete phase is not necessarily a cause of significant performance degradation.

RIS-aided Near-Field MIMO Communications: Codebook and Beam Training Design

Downlink reconfigurable intelligent surface (RIS)-assisted multi-input-multi-output (MIMO) systems are considered with far-field, near-field, and hybrid-far-near-field channels. According to the angular or distance information contained in the received signals, 1) a distance-based codebook is designed for near-field MIMO channels, based on which a hierarchical beam training scheme is proposed to reduce the training overhead; 2) a combined angular-distance codebook is designed for mixed-far-near-field MIMO channels, based on which a two-stage beam training scheme is proposed to achieve alignment in the angular and distance domains separately. For maximizing the achievable rate while reducing the complexity, an alternating optimization algorithm is proposed to carry out the joint optimization iteratively. Specifically, the RIS coefficient matrix is optimized through the beam training process, the optimal combining matrix is obtained from the closed-form solution for the mean square error (MSE) minimization problem, and the active beamforming matrix is optimized by exploiting the relationship between the achievable rate and MSE. Numerical results reveal that: 1) the proposed beam training schemes achieve near-optimal performance with a significantly decreased training overhead; 2) compared to the angular-only far-field channel model, taking the additional distance information into consideration will effectively improve the achievable rate when carrying out beam design for near-field communications.

Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation

Few-Shot Video Object Segmentation (FSVOS) aims to segment objects in a query video with the same category defined by a few annotated support images. However, this task was seldom explored. In this work, based on IPMT, a state-of-the-art few-shot image segmentation method that combines external support guidance information with adaptive query guidance cues, we propose to leverage multi-grained temporal guidance information for handling the temporal correlation nature of video data. We decompose the query video information into a clip prototype and a memory prototype for capturing local and long-term internal temporal guidance, respectively. Frame prototypes are further used for each frame independently to handle fine-grained adaptive guidance and enable bidirectional clip-frame prototype communication. To reduce the influence of noisy memory, we propose to leverage the structural similarity relation among different predicted regions and the support for selecting reliable memory frames. Furthermore, a new segmentation loss is also proposed to enhance the category discriminability of the learned prototypes. Experimental results demonstrate that our proposed video IPMT model significantly outperforms previous models on two benchmark datasets. Code is available at https://github.com/nankepan/VIPMT.