A Pinching-Antenna SyStem (PASS)-assisted covert communication framework is proposed. PASS utilizes dielectric waveguides with freely positioned pinching antennas (PAs) to establish strong line-of-sight links. Capitalizing on the high reconfigurable flexibility of waveguides, the potential of PASS for covert communications is investigated. 1) For the single-waveguide single-PA (SWSP) scenario, a closed-form optimal PA position that maximizes the covert rate is first derived. Subsequently, a one-dimensional search is performed to obtain the optimal transmit power, while a truncation method is applied simultaneously to reduce the search region. With antenna mobility on a scale of meters, PASS can deal with the challenging situation of the eavesdropper enjoying better channel conditions than the legal user. 2) For the multi-waveguide multi-PA (MWMP) scenario, the positions of multiple PAs are optimized to enable effective pinching beamforming, thereby enhancing the covert rate. In particular, an upper bound on the malicious user’s beam gain over its position-uncertainty region is first derived. Building on this, the original optimization can be converted into an equivalent problem determined solely by the PA positions. To address this problem, a particle swarm optimization (PSO)–based method is devised to solve it efficiently. Numerical results demonstrate that: i) the proposed approaches can effectively resolve the optimization problems; ii) PASS achieves a higher covert rate than conventional fixed-position antenna architectures; and iii) with enhanced flexibility, the MWMP setup outperforms the SWSP counterpart.

A sensing-aided covert communication network empowered by pinching antenna systems (PASS) is proposed in this work. Unlike conventional fixed-position multiple-input multiple-output (MIMO) arrays, PASS reconfigures wireless channels by repositioning pinching antennas (PAs) to enhance transmission covertness. To further obtain the adversary’s channel state information (CSI), a sensing function is leveraged to track the malicious warden’s movements. In particular, this paper first proposes an extended Kalman filter (EKF) based approach to fulfilling the tracking function. Building on this, a covert communication problem is formulated as a joint design of beamforming, artificial noise (AN) signals, and PA positions. Then, the beamforming and AN design subproblems are resolved using a subspace approach, while the PA position optimization subproblem is handled by a deep reinforcement learning (DRL) approach by treating the evolution of the warden’s mobility status as a temporally correlated process. Numerical results are presented and demonstrate that: i) the EKF approach can accurately track the warden’s CSI with low complexity, ii) the effectiveness of the proposed solution is verified by its outperformance over the greedy and searching-based benchmarks, and iii) with new design degrees of freedom (DoFs), the performance of PASS is superior to the conventional fully-digital MIMO systems.

Beam alignment for multiple-input and multiple-output fluid antenna systems (MIMO-FAS) is studied, where two-sided beamforming and port activation are optimized without channel estimation to enhance transmission rate. In contrast to conventional position-fixed MIMO setups, MIMO-FAS leverages flexible beamforming to achieve higher gains with a smaller number of antennas. However, realizing these gains typically requires high-complexity channel estimation methods, especially in MIMO scenarios. To overcome this challenge, a channel estimation-free active-sensing framework for beam alignment in MIMO-FAS is proposed, which consists of three components: 1) A new ping-pong transmission protocol is conceived, enabling full-dimensional pilot reception through sequential sub-array activation. 2) Based on this protocol, two learning-based active-sensing algorithms are proposed for full-dimensional beam alignment via online and offline learning, respectively. 3) A greedy-policy-based method is developed to design the port activation matrices and associated beamforming vectors based on the active-sensing results. Numerical results demonstrate that: i) the proposed active-sensing framework can effectively utilize the advantages of FAS over conventional MIMO systems without channel estimations; and ii) the online-learning method enhances generalizability by eliminating the need for extensive centralized offline training, while the offline-learning method ensures robustness and low-complexity beam alignment by leveraging prior knowledge from the training phase.

A dual-scale deployment (DSD) framework is proposed for pinching antenna systems (PASS), under which four implementation protocols are developed. In the proposed framework, coarse-scale deployment moves the pinching antenna (PA) over a wide range along the waveguide, while fine-scale deployment adjusts the PA with high precision within a local region. By jointly optimizing these two scales, the DSD framework fully exploits the flexibility of PA deployment while maintaining low computational complexity. Based on this framework, we establish a practical power-consumption model and derive closed-form expressions for the energy efficiency of PASS. An energy-efficiency maximization problem is then formulated to jointly optimize transmit precoding, PA radiation power, and dual-scale PA deployment. To solve this non-convex and highly coupled problem, a low-complexity penalty-based alternating optimization algorithm is proposed. Simulation results validate the accuracy of the theoretical analysis and the convergence of the proposed algorithm. The proposed DSD framework delivers about 70% higher energy efficiency than the conventional cell-free architecture and nearly atwofoldimprovement over MIMO systems.

The fundamental sensing limit of pinching-antenna systems (PASS) is studied from a Bayesian Cramér-Rao bound (BCRB) perspective. Compared to conventional CRB, the BCRB is independent of the exact values of sensing parameters and is not restricted by the unbiasedness of estimators, thus offering a global lower bound for evaluating sensing performance. A system where multiple targets transmit uplink pilots to a single-waveguide PASS under a time-division multiple access (TDMA) scheme is analyzed. In the single-target scenario, our analysis reveals a unique mismatch between the sensing centroid (i.e., the PA position that minimizes the BCRB) and the distribution centroid (i.e., the center of the target’s prior distribution), underscoring the necessity of pinching beamforming, i.e., repositioning PAs along the waveguide. In the multi-target scenario, two scheduling protocols are proposed: 1) pinch switching (PS), which performs separate pinching beamforming for each time slot, and 2) pinch multiplexing (PM), which applies a single pinching beamforming across all slots. Based on these protocols, both the total power minimization problem under a BCRB threshold and the min-max BCRB problem under a total power constraint are formulated. By leveraging Karush-Kuhn-Tucker (KKT) conditions, these problems are equivalently converted into a search over PA positions and solved using an element-wise algorithm. Numerical results show that: i) PASS, endowed with large-scale reconfigurability, can significantly enhance the sensing performance compared with conventional fixed-position arrays, and ii) PS provides more robust performance than PM at the cost of higher computational complexity.

A segmented waveguide-enabled pinching-antenna system (SWAN) is proposed, in which a segmented waveguide composed of multiple short dielectric waveguide segments is employed to radiate or receive signals through the pinching antennas (PAs) deployed on each segment. Based on this architecture, three practical operating protocols are proposed: segment selection (SS), segment aggregation (SA), and segment multiplexing (SM). For uplink SWAN communications, where one PA is activated per segment, the segmented structure eliminates the inter-antenna radiation effect, i.e., signals captured by one PA may re-radiate through other PAs along the same waveguide. This yields a tractable and physically consistent uplink signal model for a multi-PA pinching-antenna system (PASS), which has not been established for conventional PASS using a single long waveguide. Building on this model, PA placement algorithms are proposed to maximize the uplink signal-to-noise ratio (SNR). Closed-form expressions for the received SNR under the three protocols are derived, and the corresponding scaling laws with respect to the number of segments are analyzed. It is proven that the segmented architecture reduces both the average PA-to-user distance and the PA-to-feed distance, thereby mitigating both large-scale path loss and in-waveguide propagation loss. These results are extended to downlink SWAN communications, where multiple PAs are activated per segment, and PA placement methods are proposed to maximize the downlink received SNR under the three protocols. Numerical results demonstrate that: i) among the three protocols, SM achieves the best performance, followed by SA and then SS; and ii) for all protocols, the proposed SWAN achieves a higher SNR than conventional PASS with a single long waveguide in both uplink and downlink scenarios.

The Pinching-Antenna SyStem (PASS) reconstructs wireless channels throughpinching beamforming, wherein the activated positions of pinching antennas along dielectric waveguides are optimized to shape the radiation pattern. The aim of this article is to analyze the performance limits of employing PASS in integrated sensing and communications (ISAC). Specifically, a PASS-assisted ISAC system is considered, where a pinched waveguide is utilized to simultaneously communicate with a user and sense a target. Closed-form expressions for the achievable communication rate (CR) and sensing rate (SR) are derived to characterize the information-theoretic limits of this dual-functional operation. i) For the single-pinch case, closed-form solutions for the optimal pinching antenna location are derived undersensing-centric (S-C),communications-centric (C-C), andPareto-optimaldesigns. On this basis, the CR-SR trade-off is characterized by deriving the full CR-SR rate region, which is shown to encompass that of conventional fixed-antenna systems. ii) For the multiple-pinch case, an antenna location refinement method is applied to obtain the optimal C-C and S-C pinching beamformers. As a further advance, inner and outer bounds on the achievable CR-SR region are derived using an element-wise alternating optimization technique and by invoking Cauchy-Schwarz and Karamata’s inequalities, respectively. Numerical results demonstrate that: i) the derived bounds closely approximate the true CR-SR region; and ii) PASS can achieve a significantly larger rate region than conventional-antenna systems.

Low earth orbit (LEO) satellite constellations are becoming a cornerstone of next-generation satellite networks, enabling worldwide high-precision navigation and high-quality remote sensing. This paper proposes a novel dual-function LEO satellite constellation frame structure that effectively integrating navigation and remote sensing. Then, the Cramer-Rao bound (CRB)-based positioning, velocity measurement, and timing (PVT) error and the signal-to-ambiguity-interference-noise ratio (SAINR) are derived as performance metrics for navigation and remote sensing, respectively. Based on it, a joint beamforming design is proposed by minimizing the average weighted PVT error for navigation user equipments (UEs) while ensuring SAINR requirement for remote sensing. Simulation results validate the proposed multi-satellite cooperative beamforming design, demonstrating its effectiveness as an integrated solution for next-generation multi-function LEO satellite constellations.

Pinching-antenna systems (PASS) improve wireless links by configuring the locations of activated pinching antennas along dielectric waveguides, namely pinching beamforming. In this paper, a novel adjustable power radiation model is proposed for PASS, where power radiation ratios of pinching antennas can be flexibly controlled by tuning coupling spacing between pinching antennas and waveguides. The closed-form coupling spacings are derived to achieve flexible and equal-power radiation. Based on the commonly-assumed equal-power radiation, a practical PASS framework relying on discrete activation is considered, where pinching antennas can only be activated among a set of predefined locations. A transmit power minimization problem is formulated, which jointly optimizes the transmit beamforming, pinching beamforming, and the numbers of activated pinching antennas, subject to each user’s minimum rate requirement. (1) To obtain globally optimal solutions of the resulting highly coupled mixed-integer nonlinear programming (MINLP) problem, branch-and-bound (BnB)-based algorithms are proposed for both single-user and multi-user scenarios. (2) A low-complexity many-to-many matching algorithm is further developed. Combined with the Karush-Kuhn-Tucker (KKT) theory, locally optimal and pairwise-stable solutions are obtained within polynomial-time complexity. Simulation results demonstrate that: (i) PASS significantly outperforms conventional multi-antenna architectures, particularly when the number of users and the spatial range increase; and (ii) The proposed matching-based algorithm achieves near-optimal performance, resulting in only a slight performance loss while significantly reducing computational overheads. Code is available at https://github.com/xiaoxiaxusummer/PASS_Discrete.

A novel near-field wideband integrated sensing and communication framework is proposed to address the prohibitively high pilot overhead challenge in extremely large-scale MIMO systems. Unlike conventional approaches that rely on exhaustive two-dimensional codebook search, a unified architecture leveraging true-time-delay-based rainbow beamforming with controllable distance-dependent beam squint is proposed to extend spatial coverage. Furthermore, the inter-antenna phase ambiguity is harnessed to introduce beam split phenomena, enabling simultaneous multi-angle and multi-distance sensing within a single pilot transmission. Based on this architecture, a two-stage low-complexity sensing protocol is carried out, where distance-ring identification via beam-split-enhanced rainbow beams is performed in the first stage using sub-array structures, followed by angle refinement in the second stage. To mitigate frequency-dependent beamwidth variations, a frequency-compensated joint reconstruction algorithm based on virtual grid mapping and sparse optimization is proposed. Additionally, an echo-aided velocity estimation method exploiting intra-symbol Doppler diversity across subcarriers is developed, eliminating the need for multiple pulse transmissions. Simulation results demonstrate that: 1) complete spatial coverage is achieved with only two OFDM symbols, representing over 98% overhead reduction compared to exhaustive search methods; 2) the proposed scheme achieves superior localization accuracy with root-mean-square errors below 0.001 in normalized angle domain and 0.01 in distance-ring domain at moderate SNR; 3) communication rates are improved by 7% to 15% compared to conventional near-field beam training approaches under identical pilot budgets.

The joint transmit and pinching beamforming design for spectral efficiency (SE) and energy efficiency (EE) tradeoff in pinching-antenna systems (PASS) is proposed, under practical channel and energy consumption models. In the single-user scenario, it is proved that the optimal pinching antenna (PA) positions are independent of the transmit beamforming. Based on this insight, a two-stage joint beamforming design is proposed. Specifically, in the first stage, a general PA placement framework is proposed for multi-waveguide systems. In the second stage, the closed-form solution for the optimal transmit beamformer is derived given the optimized PA positions. In the multi-user scenario, an alternating optimization (AO)-based joint beamforming design is proposed to balance the SE-EE performance while taking the quality-of-service (QoS) requirements into account. It is proved that the proposed AO-based algorithm is guaranteed to converge when no constraints are violated in PA placement subproblem. Numerical results demonstrate that: 1) the proposed algorithms effectively improve joint SE-EE performance; 2) PASS exhibits strong robustness against variations in the service area along the waveguide direction.

Mobile Edge Generation (MEG) is presented as a distributed framework in which an identical diffusion model (DM) is deployed on both an edge server (ES) and user equipment (UE). In MEG, most computations and generation steps of UEs are offloaded to the ES. However, heterogeneous user preferences cannot be captured by a uniform DM. To address this, a Personalized Mobile Edge Generation (P-MEG) framework is proposed, where a lightweight personalized U Net is trained on the UE in collaboration with the pre-trained DM from the ES. During inference, pre-trained ES features are fused with UE features through scaling coefficients that encode user-specific preferences. The training stability of P MEG and the robustness of feature fusion under noisy wireless channels are theoretically investigated, where bounds are derived on forward and backward feature oscillations, backpropagation gradients, and feature fusion errors in the presence of additive white Gaussian noise (AWGN) noise. These bounds are shown to depend on the fusion scale, and robustness under AWGN follows the same dependence. A multi-U-Net training model with AWGN perturbations is introduced to emulate over-the air training. Inspired by these insights, a constant scaling connection (CSC) method is proposed to stabilize training by exponentially scaling the fusion coefficients, and a random mask training (RMT) strategy is introduced to reduce computational requirements by adjusting transmission ratios of personalized features. Experimental evaluations on MNIST, EMNIST and PACS demonstrate that: 1) P-MEG enables effective personalized image generation, 2) RMT alleviates computational demands with only slight training overhead, and 3) CSC stabilizes feature oscillations under noisy channels, yielding a 1.4-fold acceleration in training.

A Cramér-Rao bound (CRB) optimization framework for near-field sensing (NISE) with continuous-aperture arrays (CAPAs) is proposed. In contrast to conventional spatially discrete arrays (SPDAs), CAPAs emit electromagnetic (EM) probing signals through continuous source currents for target sensing, thereby exploiting the full spatial degrees of freedom (DoFs). The maximum likelihood estimation (MLE) method for estimating target locations in the near-field region is developed. To evaluate the NISE performance with CAPAs, the CRB for estimating target locations is derived based on continuous transmit and receive array responses of CAPAs. Subsequently, a CRB minimization problem is formulated to optimize the continuous source current of CAPAs. This results in a non-convex, integral-based functional optimization problem. To address this challenge, the optimal structure of the source current is derived and proven to be spanned by a series of basis functions determined by the system geometry. To solve the CRB minimization problem, a low-complexity subspace manifold gradient descent (SMGD) method is proposed, leveraging the derived optimal structure of the source current. Our simulation results validate the effectiveness of the proposed SMGD method and further demonstrate that i) the proposed SMGD method can effectively solve the CRB minimization problem with reduced computational complexity, and ii) CAPA achieves a tenfold improvement in sensing performance compared to its SPDA counterpart, due to full exploitation of spatial DoFs.

A near-field motion parameter estimation method is proposed. In contrast to far-field sensing systems, the near-field sensing system leverages spherical-wave characteristics to enable full-vector location and velocity estimation. Despite promising advantages, the near-field sensing system faces a significant challenge, where location and velocity parameters are intricately coupled within the signal. To address this challenge, a novel subarray-based variational message passing (VMP) method is proposed for near-field joint location and velocity estimation. First, a factor graph representation is introduced, employing subarray-level directional and Doppler parameters as intermediate variables to decouple the complex location-velocity dependencies. Based on this, the variational Bayesian inference is employed to obtain closed-form posterior distributions of subarray-level parameters. Subsequently, the message passing technique is employed, enabling tractable computation of location and velocity marginal distributions. Two implementation strategies are proposed: 1) System-level fusion that aggregates all subarray posteriors for centralized estimation, or 2) Subarray-level fusion where locally processed estimates from subarrays are fused through Guassian product rule. Cramér-Rao bounds for location and velocity estimation are derived, providing theoretical performance limits. Numerical results demonstrate that the proposed VMP method outperforms existing approaches while achieving a magnitude lower complexity. Specifically, the proposed VMP method achieves centimeter-level location accuracy and sub-m/s velocity accuracy. It also demonstrates robust performance for high-mobility targets, making the proposed VMP method suitable for real-time near-field sensing and communication applications.

Unlike conventional systems using a fixed-location antenna, the channel capacity of the pinching-antenna system (PASS) is determined by the activated positions of pinching antennas. This article characterizes the capacity region of multiuser PASS, where a single pinched waveguide is deployed to enable both uplink and downlink communications. The capacity region of the uplink channel is first characterized. i) For the single-pinch case, closed-form expressions are derived for the optimal antenna activation position, along with the corresponding capacity region and the achievable data rate regions under time-division multiple access (TDMA) and frequency-division multiple access (FDMA). It is proven that the capacity region of PASS encompasses that of conventional fixed-antenna systems, and that the FDMA rate region contains the TDMA rate region. ii) For the multiple-pinch case, inner and outer bounds on the capacity region are derived using an element-wise alternating antenna position optimization technique and the Cauchy-Schwarz inequality, respectively. The achievable FDMA rate region is also derived using the same optimization framework, while the TDMA rate region is obtained through an antenna position refinement approach. The analysis is then extended to the downlink PASS using the uplink-downlink duality framework. It is proven that the relationships among the downlink capacity and rate regions are consistent with those in the uplink case. Numerical results demonstrate that: i) the derived bounds closely approximate the exact capacity region, ii) PASS yields a significantly enlarged capacity region compared to conventional fixed-antenna systems, and iii) in the multiple-pinch case, TDMA and FDMA are capable of approaching the channel capacity limit.

The performance of linear receive beamforming in continuous-aperture array (CAPA)-based uplink communications is analyzed. Three continuous beamforming techniques are proposed under the criteria of maximum-ratio combining (MRC), zero-forcing (ZF), and minimum mean-squared error (MMSE). i) ForMRC beamforming, a closed-form expression for the beamformer is derived to maximize per-user signal power. The achieved uplink rate and mean-squared error (MSE) in detecting received data symbols are analyzed. ii) ForZF beamforming, a closed-form beamformer is derived based on channel correlation to eliminate interference. As a further advance, its optimality in maximizing effective channel gain while ensuring zero inter-user interference is proven. iii)MMSE beamformingis established as the optimal linear receive approach for CAPAs in terms of maximizing per-user rate and minimizing MSE. Closed-form expressions are derived for the MMSE beamformer and the achievable sum-rate and sum-MSE. It is mathematically proven that all proposed beamformers lie within the signal subspace spanned by users’ spatial responses. Numerical results demonstrate that CAPAs outperform conventional spatially-discrete arrays (SPDAs) by achieving higher sum-rates and lower sum- MSEs under the proposed linear beamforming techniques.

We address the modeling and optimal beamforming (BF) design for multiple-input multiple-output (MIMO) continuous aperture array (CAPA) systems operating over doubly-dispersive (DD) channels. First, a comprehensive DD continuous MIMO (DDC MIMO) channel model that incorporates CAPAs at both the transmitter (TX) and receiver (RX) is derived, which is used to obtain explicit input-output (I/O) relations for various waveforms well suited to integrated sensing and communications (ISAC) and robust to DD channels, namely orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM). Then, functional optimization problems are formulated for the design of TX and RX BF matrices that maximize received power, in which novel low-complexity, closed-form solutions are obtained via the calculus of variations (CoV) method, yielding expressions closely related to the classical matched filter commonly used in conventional MIMO systems. Simulation results confirm that the proposed TX/RX BF designs with CAPAs provide significant performance and computational complexity gains over conventional MIMO systems in DD channels.

Continuous aperture array (CAPA) is considered a promising technology for 6G networks, offering the potential to fully exploit spatial degrees of freedom (DoFs) and achieve the theoretical limits of channel capacity. This paper investigates the performance gain of a CAPA-based downlink secure transmission system, where multiple legitimate user terminals (LUTs) coexist with multiple eavesdroppers (Eves). The system’s secrecy performance is evaluated using a weighted secrecy sum-rate (WSSR) under a power constraint. We then propose two solutions for the secure current pattern design. The first solution is a block coordinate descent (BCD) optimization method based on fractional programming (FP), which introduces a continuous-function inversion theory corresponding to matrix inversion in the discrete domain. This approach derives a closed-form expression for the optimal source current pattern. Based on this, it can be found that the optimal current pattern is essentially a linear combination of the channel spatial responses, thus eliminating the need for complex integration operations during the algorithm’s optimization process. The second solution is a heuristic algorithm based on zero-forcing (ZF), which constructs a zero-leakage current pattern using the channel correlation matrix. It further employs a water-filling approach to design an optimal power allocation scheme that maximizes the WSSR. In high signal-to-noise ratio regions, this solution gradually approaches the first solution, ensuring zero leakage while offering lower computational complexity. Simulation results demonstrate that: 1) CAPA-based systems achieve better WSSR compared to discrete multiple-input multiple-output (MIMO) systems. 2) The proposed methods, whether optimization-based or heuristic, provide significant performance improvements over existing state-of-the-art Fourier-based discretization methods, while considerably reducing computational complexity.

An efficient beamforming design is proposed for continuous aperture array (CAPA)-based point-to-point multiple-input multiple-output (MIMO) systems. In contrast to conventional spatially discrete array (SPDA)-MIMO systems, whose optimal beamforming can be obtained using singular-value decomposition, CAPA-MIMO systems require solving the eigendecomposition of a Hermitian kernel operator, which is computationally prohibitive. To address this challenge, an explicit closed-form expression for the achievable rate of CAPA-MIMO systems is first derived as a function of the continuous transmit beamformer. Subsequently, an iterative weighted minimum mean-squared error (WMMSE) algorithm is proposed, directly addressing the CAPA-MIMO beamforming optimization without discretization approximation. Closed-form updates for each iteration of the WMMSE algorithm are derived via the calculus of variations (CoV) method. For low-complexity implementation, an equivalent matrix-based iterative solution is introduced using Gauss-Legendre quadrature. Our numerical results demonstrate that 1) CAPA-MIMO achieves substantial performance gain over the SPDA-MIMO, 2) the proposed WMMSE algorithm enhances performance while significantly reducing computational complexity compared to state-of-the-art Fourier-based approaches, and 3) the proposed WMMSE algorithm enables practical realization of parallel, non-interfering transmissions.

The phenomenon of mutual coupling in continuous aperture arrays (CAPAs) is studied. First, a general physical model for the phenomenon that accounts for both polarization and surface dissipation losses is developed. Then, the unipolarized coupling kernel is characterized, revealing that polarization induces anisotropic coupling and invalidates the conventional half-wavelength spacing rule for coupling elimination. Next, the beamforming design problem for CAPAs with coupling is formulated as a functional optimization problem, leading to the derivation of optimal beamforming structures via the calculus of variations. To address the challenge of inverting the coupling kernel in the optimal structure, two methods are proposed: 1) the kernel approximation method, which yields a closed-form solution via wavenumber-domain transformation and GaussLegendre quadrature, and 2) the conjugate gradient method, which addresses an equivalent quadratic functional optimization problem iteratively. Furthermore, the optimal array gain and beampattern are analyzed at the large-aperture limit. Finally, the proposed continuous mutual coupling model is extended to spatially discrete arrays (SPDAs), and comprehensive numerical results are provided, demonstrating that: 1) coupled SPDA performance correctly converges to the CAPA limit, while uncoupled models are shown to violate physics, 2) polarization results in anisotropic array gain behavior, and 3) the coupled beampattern exhibits higher directivity than the uncoupled beampattern.

In this paper, sensing-assisted secure communication in a multi-user multi-eavesdropper integrated sensing and communication (ISAC) system is investigated. Confidential communication signals and dedicated sensing signals are jointly transmitted by a base station (BS) to simultaneously serve users and sense aerial eavesdroppers (AEs). A sum rate maximization problem is formulated under AEs’ Signal-to-Interference-plus-Noise Ratio (SINR) and sensing Signal-to-Clutter-plus-Noise Ratio (SCNR) constraints. A fractional-programming-based alternating optimization algorithm is developed to solve this problem for fully digital arrays, where successive convex approximation (SCA) and semidefinite relaxation (SDR) are leveraged to handle non-convex constraints. Furthermore, the minimum number of dedicated sensing beams is analyzed via a worst-case rank bound, upon which the proposed beamforming design is further extended to the hybrid analog-digital (HAD) array architecture, where the unit-modulus constraint is addressed by manifold optimization. Simulation results demonstrate that only a small number of sensing beams are sufficient for both sensing and jamming AEs, and the proposed designs consistently outperform strong baselines while also revealing the communication–sensing trade-off.

The performance of linear receive beamforming in continuous-aperture array (CAPA)-based uplink communications is investigated. Three continuous beamforming strategies are proposed based on the principles of maximum-ratio combining (MRC), zero-forcing (ZF), and maximum signal-to-interference-plus-noise ratio (SINR) (i.e., optimal beamforming). For MRC beamforming, closed-form expressions for both the beamformer and the achievable sum-rate are derived. For ZF beamforming, a closed-form solution is developed using channel correlation to effectively eliminate inter-user interference. For optimal beamforming, a closed-form beamformer is obtained by solving an operator-based Rayleigh quotient maximization problem, and the associated achievable sum-rate is characterized. Numerical results confirm that CAPAs outperform traditional spatially-discrete arrays (SPDAs), achieving superior sum-rate performance under all three beamforming schemes.

The application of continuous aperture array (CAPA) for mono-static sensing is studied in this paper. Specifically, the transmit CAPA emits a probing signal to a sensing target (ST) and then positions this ST using the reflected echo signal from the ST. To evaluate the sensing performance, the CramérRao Bound (CRB) is derived according to the proposed roundtrip channel model based on electromagnetic theory. Moreover, a maximum likelihood detection scheme is proposed to position the ST under the maximum likelihood criteria. Simulation results demonstrate the high accuracy of CAPA-enabled mono-static sensing.

A subarray-based near-field joint location and velocity estimation framework is proposed for sensing a moving target using extremely large-scale antenna arrays. To tackle the intricate near-field non-linear phase, the piecewise-far-field channel model is adopted, approximating the near-field channel by partitioning the transmit and receive arrays into subarrays and applying the near-field assumption between subarrays and the far-field assumption within each subarray. Based on this model, the complex near-field estimation problem can be transformed into a far-field joint multiple bistatic radar parameter estimation problem, enabling separable location and velocity estimation. An efficient three-stage algorithm is developed, exploiting joint sparsity across transmit-receive subarray pairs. In the first stage, a mixed-norm minimization method is employed to obtain coarse estimates of the location and complex channel gain, which are refined using the gradient descent method in the second stage. Finally, the velocity is estimated using the multiple signal classification spectrum estimation method, based on the refined location estimate. Simulation results demonstrate the effectiveness of the proposed framework and reveal a trade-off in system design: location estimation accuracy improves with increased subarray size, while velocity estimation benefits from a greater number of smaller subarrays.

The diversity and multiplexing performance of continuous aperture array (CAPA)-based multiple-input multiple-output (MIMO) channels is analyzed. Angular-domain fading models are derived, and an angular-domain transmission framework is proposed to support CAPA-based MIMO communications. Asymptotic expressions are derived for the achievable outage probability (OP) and average data rate (ADR), and insights into the diversity-multiplexing trade-off (DMT) and associated array gain are provided. Further, the performance of CAPAs is compared with that of conventional spatially discrete arrays (SPDAs) to highlight the advantages of CAPAs. Analytical and numerical results demonstrate that: i) CAPAs achieve lower OP and higher ADR than SPDAs; ii) CAPAs attain the same DMT as SPDAs with half-wavelength antenna spacing but with a higher array gain; and iii) CAPAs outperform SPDAs in DMT when the antenna spacing exceeds half a wavelength.

The beamforming optimization for maximizing weighted sum-rate (WSR) in continuous aperture array (CAPA)-based multi-user communications is studied. In particular, the transmit beamformers of CAPA are modelled as continuous source current patterns, rendering the beamforming optimization problem as a non-convex integral-based functional programming problem. In contrast to the state-of-the-art Fourier-based method that requires numerous Fourier basis functions to approximate the functional programming, a low-complexity calculus of variations (CoV)-based method is proposed to solve the functional programming problem for WSR maximization directly, where the optimal form of the continuous source patterns is derived. Based on this optimal form, a low-complexity integral-free iterative algorithm is developed. Our numerical results validate the effectiveness of the proposed designs. It is revealed that compared to the state-of-the-art Fourier-based method, the proposed CoV based method not only improves WSR performance but also reduces computational complexity by up to hundreds of times for large CAPA apertures and high frequencies.

A Pinching-Antenna SyStem (PASS)-assisted integrated sensing and communications (ISAC) framework is established, where a pinched waveguide is utilized to simultaneously communicate with a user and sense a target. Closed-form expressions for the achievable communication rate (CR) and sensing rate (SR) are derived to characterize the information-theoretic limits of this dual-functional operation. Closed-form solutions for the optimal pinching antenna location are derived under sensing-centric (S-C), communications-centric (C-C), and Pareto-optimal designs. On this basis, the CR-SR trade-off is characterized by deriving the full CR-SR rate region. Numerical results demonstrate that PASS can achieve a larger rate region than conventional fixed-antenna systems.

The near-field spatial correlation for multiple-input multiple-output (MIMO) communications in multi-path fading channels is analyzed. Based on the general non-uniform spherical wave (NUSW) model, an analytical integral-form expression for near-field spatial correlation is derived, which generalizes the conventional uniform plane wave (UPW)-based far-field spatial correlation. Furthermore, by considering the specific von Mises-Fisher distribution of scatterer locations, a simplified closed-form near-field spatial correlation expression is derived. It is rigorously proved that 1) in contrast to the far-field spatial correlation, the near-field spatial correlation no longer exhibits spatial stationary property, and 2) the NUSW-based near-field spatial correlation model depends on the power location spectrum, which encompasses both the angles and distances of the scatterers. Next, the developed near-field spatial correlation can be utilized to derive a closed-form expression for the effective degrees of freedom (EDoF). Additionally, a correlation-based stochastic channel model is constructed for MIMO communications, from which an optimal transmission strategy is devised. Subsequently, the power allocation is optimized to achieve the maximum ergodic spectral efficiency. Numerical results validate 1) the significance of near-field spatial correlation modeling for MIMO communications, 2) near-field MIMO exhibits a higher EDoF than the conventional far-field counterpart, and 3) the constructed correlation-based stochastic channel model facilitates the derivation of an optimal transmission strategy, thereby maximizing ergodic spectral efficiency.

A novel modular extremely large-scale multiple-input-multiple-output integrated sensing and communication system is investigated in this paper. The piecewise-far-field channel model is employed to characterize both communication and sensing channels, capturing the far-field propagation within each subarray and the near-field effects among subarrays due to the small subarray aperture and large inter-subarray spacing. Then, a joint transmit-receive beamforming problem is formulated to optimize communication spectral efficiency while satisfying the sensing signal-to-clutter-plus-noise ratio requirement. To solve this problem, an alternating optimization framework is proposed to iteratively update the transmit beamformer and receive beamformer until convergence. For a fixed receive beamformer, a closed-form optimal analog beamformer is firstly derived by exploiting the near-field propagation characteristics among subarrays, transforming the transmit hybrid beamforming problem into a low-dimensional digital beamforming optimization and substantially reducing the computational complexity. Then, two efficient algorithms are proposed to solve the rank-constrained digital beamforming problem. First, the semi-closed form of the optimal digital beamformer is derived and shown to form a complex Stiefel manifold. Based on this structure, a joint Riemannian-Euclidean gradient descent algorithm is developed for iterative optimization. Second, an semidefinite relaxation-based approach is proposed, where a near-optimal solution is obtained through rank constraint relaxation and randomization. Extensive simulations validate the superiority of the proposed algorithms, revealing that the optimal subarray scale balances spatial multiplexing and beamforming gains based on user distance, while increasing subarray numbers significantly enhances range resolution due to more pronounced spherical wavefronts.

A near-field wideband communication system is investigated in which a base station (BS) employs an extra-large scale antenna array (ELAA) to serve multiple users in its near-field region. To facilitate near-field multi-user beamforming and mitigate the spatial wideband effect, the BS employs a hybrid beamforming architecture based on true-time delayers (TTDs). In addition to the conventional fully-connected TTD-based hybrid beamforming architecture, a new sub-connected architecture is proposed to improve energy efficiency and reduce hardware requirements. Two wideband beamforming optimization approaches are proposed to maximize spectral efficiency for both architectures. 1) Fully-digital approximation (FDA) approach: In this method, the TTD-based hybrid beamformer is optimized by the block-coordinate descent and penalty method to approximate the optimal digital beamformer. This approach ensures convergence to the stationary point of the spectral efficiency maximization problem. 2) Heuristic two-stage (HTS) approach: In this approach, the analog and digital beamformers are designed in two stages. In particular, two low-complexity methods are proposed to design the high-dimensional analog beamformers based on approximate and exact line-of-sight channels, respectively. Subsequently, the low-dimensional digital beamformer is optimized based on the low-dimensional equivalent channels, resulting in reduced computational complexity and channel estimation complexity. Our numerical results show that 1) the proposed approach effectively eliminates the spatial wideband effect, and 2) the proposed sub-connected architecture is more energy efficient and has fewer hardware constraints on the TTD and system bandwidth compared to the fully-connected architecture.

The optimal beamforming design for multi-user continuous aperture array (CAPA) systems is proposed. In contrast to conventional spatially discrete array (SPDA), the beamformer for CAPA is a continuous function rather than a discrete vector or matrix, rendering beamforming optimization a non-convex integral-based functional programming. To address this challenging issue, the closed-form optimal structure of the CAPA beamformer is first derived for maximizing generic system utility functions, by addressing the inversion of continuous functions and using the Lagrangian duality and the calculus of variations. The derived optimal structure is a linear combination of the continuous channel responses for CAPA, with the linear weights determined by the channel correlations. As a further advance, a monotonic optimization method is proposed for obtaining globally optimal CAPA beamforming based on the derived optimal structure. More particularly, a closed-form fixed-point iteration is proposed to obtain the globally optimal solution to the power minimization problem for CAPA beamforming. Furthermore, based on the optimal structure, the low-complexity maximum ratio transmission (MRT), zero-forcing (ZF), and minimum mean-squared error (MMSE) designs for CAPA beamforming are derived. It is theoretically proved that: 1) the MRT and ZF designs are asymptotically optimal in low and high signal-to-noise ratio (SNR) regimes, respectively, and 2) the MMSE design is optimal for signal-to-leakage-plus-noise ratio (SLNR) maximization. Our numerical results validate the effectiveness of the proposed designs and reveal that:i)CAPA achieves significant communication performance gain over SPDA, andii)the MMSE design achieves nearly optimal performance in most cases, while the MRT and ZF designs achieve nearly optimal performance in specific cases.

The Pinching-Antenna SyStem (PASS) is a revolutionary flexible antenna technology designed to enhance wireless communication by establishing strong line-of-sight (LoS) links, reducing free-space path loss and enabling antenna array reconfigurability. PASS uses dielectric waveguides with low propagation loss for signal transmission, radiating via a passive pinching antenna, which is a small dielectric element applied to the waveguide. This paper first proposes a physics-based hardware model for PASS, where the pinching antenna is modeled as an open-ended directional coupler, and the electromagnetic field behavior is analyzed using coupled-mode theory. A simplified signal model characterizes the coupling effect between multiple antennas on the same waveguide. Based on this, two power models are proposed: equal power and proportional power models. Additionally, a transmit power minimization problem is formulated/studied for the joint optimization of transmit and pinching beamforming under both continuous and discrete pinching antenna activations. Two algorithms are proposed to solve this multimodal optimization problem: the penalty-based alternating optimization algorithm and a low-complexity zero-forcing (ZF)-based algorithm. Numerical results show that 1) the ZF-based low-complexity algorithm performs similarly to the penalty-based algorithm, 2) PASS reduces transmit power by over 95% compared to conventional and massive MIMO, 3) discrete activation causes minimal performance loss but requires a dense antenna set to match continuous activation, and 4) the proportional power model yields performance comparable to the equal power model.

A general fading model for multipath channels between two non-parallel continuous-aperture arrays (CAPAs) is proposed. Building on this model, the performance of diversity and multiplexing achieved by CAPAs over fading channels is analyzed. i) For multiple-input single-output (MISO) and singleinput multiple-output (SIMO) channels, Landau’s eigenvalue theorem is applied to analyze the autocorrelation of the spatial response. Closed-form expressions are derived for the outage probability (OP) and ergodic channel capacity (ECC). Asymptotic analyses in the high signal-to-noise ratio (SNR) regime are conducted to reveal the maximal achievable diversity and multiplexing gains. The diversity-multiplexing trade-off (DMT) is characterized, along with the array gain within the DMT framework. ii) For multiple-input multiple-output (MIMO) channels, a wavenumber-domain-based transmission framework is proposed to leverage the spatial degrees of freedom offered by CAPAs. Asymptotic approximations for the OP and ECC are derived, and the DMT is explored. The performance of CAPAs is further compared with that of conventional spatially-discrete arrays (SPDAs). Analytical and numerical results demonstrate that: i) CAPAs achieve a lower OP and higher ECC than SPDAs; ii) CAPAs achieve the same DMT as SPDAs with antenna spacing no larger than half a wavelength while attaining a higher array gain; and iii) CAPAs outperform SPDAs with antenna spacing greater than half a wavelength in terms of DMT.

True-time delayers (TTDs) are popular analog devices for facilitating near-field wideband beamforming subject to the spatial-wideband effect. In this paper, an adaptive TTD configuration is proposed for short-range TTDs. Compared to the existing TTD configurations, the proposed one can effectively combat the spatial-wideband effect for arbitrary user locations and array shapes with the aid of a switch network. A novel end-to-end deep neural network is proposed to optimize the hybrid beamforming with adaptive TTDs for maximizing spectral efficiency. First, based on the U-Net architecture, a near-field channel learning module (NFC-LM) is proposed for adaptive beamformer design through extracting the latent channel response features of various users across different frequencies. In the NFC-LM, an improved cross attention (CA) is introduced to further optimize beamformer design by enhancing the latent feature connection between near-field channel and different beamformers. Second, a switch multi-user transformer (S-MT) is proposed to adaptively control the connection between TTDs and phase shifters (PSs). In the S-MT, an improved multi-head attention, namely multi-user attention (MSA), is introduced to optimize the switch network by exploring the latent channel relations among various users. Third, a multi-feature cross attention (MCA) is introduced to simultaneously optimize the NFC-LM and S-MT by enhancing the latent feature correlation between beamformers and the switch network. Numerical simulation results show that 1) the proposed adaptive TTD configuration effectively eliminates the spatial-wideband effect under uniform linear array (ULA) and uniform circular array (UCA) architectures, and 2) the proposed deep neural network can provide near-optimal spectral efficiency, and solve the multi-user beamformer design and dynamical connection problem in real-time.

The performance of near-field sensing (NISE) in a legacy wideband multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system is analyzed. The maximum likelihood estimates (MLE) for the target’s distance and angle relative to the antenna array are derived. To evaluate the estimation error, closedform analytical expressions of Cram´er-Rao bounds (CRBs) are derived for both uniform linear arrays (ULAs) and uniform circular arrays (UCAs). The asymptotic CRBs are then analyzed to reveal the scaling laws of CRBs with respect to key system parameters, including array size, bandwidth, and target distance. Our results reveal that 1) the mean-squared error achieved by MLEs approaches CRBs in the high signal-to-noise ratio regime; 2) a larger array aperture does not necessarily improve NISE performance, especially with ultra-large bandwidth; 3) large bandwidth sets an estimation error ceiling for NISE as target distance increases; 4) array aperture and bandwidth, rather than the number of antennas and subcarriers, are the key factors affecting wideband NISE performance; and 5) UCAs offer superior, angle-independent wideband NISE performance compared to ULAs with the same aperture.

The beamforming optimization in continuous aperture array (CAPA)-based multi-user communications is studied. In contrast to conventional spatially discrete antenna arrays, CAPAs can exploit the full spatial degrees of freedom (DoFs) by emitting information-bearing electromagnetic (EM) waves through continuous source current distributed across the aperture. Nevertheless, such an operation renders the beamforming optimization problem as a non-convex integral-based functional programming problem, which is challenging for conventional discrete optimization methods. A couple of low-complexity approaches are proposed to solve the functional programming problem. 1) Calculus of variations (CoV)-based approach: Closed-form structure of the optimal continuous source patterns are derived based on CoV, inspiring a low-complexity integral-free iterative algorithm for solving the functional programming problem. 2) Correlation-based zero-forcing (Corr-ZF) approach: Closed-form ZF source current patterns that completely eliminate the inter-user interference are derived based on the channel correlations. By using these patterns, the original functional programming problem is transformed to a simple power allocation problem, which can be solved using the classical water-filling approach with reduced complexity. Our numerical results validate the effectiveness of the proposed designs and reveal that: 1) compared to the state-of-the-art Fourier-based discretization approach, the proposed CoV-based approach not only improves communication performance but also reduces computational complexity by up to hundreds of times for large CAPA apertures and high frequencies, and 2) the proposed Corr-ZF approach achieves asymptotically optimal performance compared to the CoV-based approach.

A near-field sensing (NISE)-enabled user tracking scheme is proposed. Compared to conventional angle-only sensing in the far-field scenario, NISE offers the capability of joint angle and distance sensing. In the proposed user tracking scheme, a deep unfolding neural network (DUNN)-based method is employed to sense radial and transverse velocities, which can further facilitate predicting the user’s location in the next time instant. Specifically, the DUNN is training on a synthesized dataset to update trainable parameters in an offline manner. Then, the DUNN is implemented online to extract the radial and transverse velocities from the received echo signal. Moreover, an online fine-tuning module is attached to the DUNN to refine the output of the pre-trained DUNN. Finally, based on estimated velocities, the user position in the consecutive time instant can be predicted, thus enabling user tracking. Simulation results show that the proposed scheme can extract velocities from echo signals and track the user accurately.

The beamforming performance of the uniform circular array (UCA) in near-field wideband communication systems is investigated. Firstly, the unique beam squint effect in near-field wideband UCA systems is analyzed in both the distance and angular domains. It is demonstrated that the generated beams at different frequencies are focused at different locations, resulting in significant beamforming loss. To alleviate this unique beam squint effect and facilitate beamfocusing, an energy-efficient beamforming scheme based on the true-time delay (TTD) architecture is proposed. Specifically, the phase shifters (PSs) and the time delay of TTDs are designed based on the analytical formula for beamforming gain. Additionally, the minimum number of TTDs required to achieve a predetermined beamforming gain while minimizing energy consumption is quantified. Numerical results show that the proposed beamforming scheme effectively eliminates the near-field beam squint and outperforms the conventional schemes in terms of spectral efficiency and energy efficiency.

A personalized mobile edge generation (P-MEG) stable diffusion structure is proposed. The stable diffusion model is deployed on the edge server, enabling users to train personalized weights for customized generation tasks. To mitigate oscillations and accelerate convergence speed during user-personalized training, an effective constant scaling connection (CSC) with random model segmentation method is introduced. In addition, the stability of forward propagation in conditioned stable diffusion generation is explored. Furthermore, we theoretically analyze the benefits of CSC in enhancing the stability of user-personalized model training. The numerical results demonstrate that the proposed CSC method effectively assists the user in training personalized weights. Additionally, the CSC significantly stabilizes hidden feature oscillations and accelerates convergence speed during the training of personalized stable diffusion model.

The performance bounds of near-field sensing are studied for circular arrays, focusing on the impact of bandwidth and array size. The closed-form Cramér-Rao bounds (CRBs) for angle and distance estimation are derived, revealing the scaling laws of the CRBs with bandwidth and array size. Contrary to expectations, enlarging array size does not always enhance sensing performance. Furthermore, the asymptotic CRBs are analyzed under different conditions, unveiling that the derived expressions include the existing results as special cases. Finally, the derived expressions are validated through numerical results.

A novel near-field transmission framework is proposed for dynamic metasurface antenna (DMA)-enabled non-orthogonal multiple access (NOMA) networks. The base station (BS) exploits the hybrid beamforming to communicate with multiple near users (NUs) and far users (FUs) using the NOMA principle. Based on this framework, a beam-steering scheme is proposed. The metric of beam pattern error (BPE) is introduced for the characterization of the gap between the hybrid beamformers and the desired ideal beamformers, where a two-layer algorithm is proposed to minimize BPE by optimizing hybrid beamformers. Then, the optimal power allocation strategy is obtained to maximize the sum achievable rate of the network. Numerical results validate that the proposed beamforming schemes exhibit superior performance compared with the existing imperfect-resolution-based beamforming scheme.

The application of inverse synthetic aperture radar (ISAR) is investigated in orthogonal frequency-division multiplexing (OFDM) integrated sensing and communication (ISAC) systems. In contrast to velocity sensing of a point target of most ISAC works, ISAR enables rotational velocity sensing to obtain the cross-range values of different scatterers on an extended target. To utilize this characteristic, we initially derive the ISAR OFDM received signal reconstruction in the frequency domain, which demonstrates that the ISAR OFDM echo signal can be equivalent to the signal received by an array, including the decoupled radial range and cross-range parameters. According to the derived signal model, a supporting parameter estimation algorithm based on the equivalent array form is proposed to estimate the range and cross-range parameters for resolvable scatterers on the extended target. Finally, numerical results confirm the effectiveness of utilizing ISAR sensing in wideband ISAC systems.

A downlink near-field integrated sensing and communication (ISAC) framework is proposed. A novel double-array structure at the BS is proposed, where an assisting receiver (AR) is attached to the main transmitter (MT) to enable the near-field communication (NFC) system with the ability of target positioning. The joint angle and distance Cramér-Rao bound (CRB) is derived and then minimized subject to the communication quality of ser-vice (QoS) requirement and the hybrid-analog-and-digital (HAD) structure constraint. A double-loop iterative algorithm utilizing the penalty dual decomposition (PDD) framework is proposed to tackle the non-convex problem. The numerical results show that: 1) The proposed ISAC system can locate the target in both angle and distance domains; 2) The performance of the HAD ISAC approaches the performance of fully digital (FD) ISAC when the communication QoS requirement is not stringent.

An active-sensing-based learning algorithm is proposed to solve the near-field beam alignment problem with the aid of wavenumber-domain transform matrices (WTMs). Specifically, WTMs can transform the antenna-domain channel into a sparse representation in the wavenumber domain. The dimensions of WTMs can be further reduced by exploiting the dominance of line-of-sight (LoS) links. By employing these lower-dimensional WTMs as mapping functions, the active-sensing-based algorithm is executed in the wavenumber domain, resulting in an acceleration of convergence. Compared with the codebook-based beam alignment methods, the proposed method finds the optimal beam pair in a ping-pong fashion, thus avoiding high training overheads caused by beam sweeping. Finally, the numerical results validate the effectiveness of the proposed method.

True-time delayers (TTDs) are popular components for hybrid beamforming architectures to combat the spatial-wideband effect in wideband near-field communications. A se-rial and a hybrid serial-parallel TTD configuration are inves-tigated for hybrid beamforming architectures. Compared to the conventional parallel configuration, the serial configuration exhibits a cumulative time delay through multiple TTDs, which potentially alleviates the maximum delay requirements on the TTDs. However, independent control of individual TTDs becomes impossible in the serial configuration. In this context, a hybrid TTD configuration is proposed as a compromise solution. More-over, the wideband near-field beamforming design for different configurations is studied for maximizing the spectral efficiency in single-user systems. In particular, a closed-form solution for the beamforming design is derived. The preferred user locations and the required maximum time delay of each TTD configuration are characterized. Our numerical results confirm the effectiveness of the proposed designs.

A near-field simultaneous wireless information and power transfer (SWIPT) network is investigated, where the hybrid beamforming architecture is employed at the base station to send the information beams for information transmission while charging energy harvesting users. A transmit power minimization problem is formulated by jointly optimizing the analog beamformer and the baseband digital beamformers. To tackle the non-convex optimization problem, a penalty-based two-layer (PTL) algorithm is proposed to optimize the analog beamformer and baseband digital information beamformers. By employing the block coordinate descent method, the optimal analog beamformer, and baseband digital information beamformers are obtained in the closed-form expressions. Moreover, a low-complexity two-stage algorithm to reduce the high computational complexity caused by the large number of antennas is proposed. Numerical results illustrate that: 1) the proposed PTL algorithm can achieve near-optimal performance; and 2) in contrast to the far-field SWIPT, a single near-field beamformer can focus the energy on multiple locations.

A novel near-field transmission framework is proposed for dynamic metasurface antenna (DMA)-enabled nonorthogonal multiple access (NOMA) networks. The base station (BS) exploits the hybrid beamforming to communicate with multiple near users (NUs) and far users (FUs) using the NOMA principle. Based on this framework, two novel beamforming schemes are proposed. 1) For the case of the grouped users distributed in the same direction, a beam-steering scheme is developed. The metric of beam pattern error (BPE) is introduced for the characterization of the gap between the hybrid beamformers and the desired ideal beamformers, where a two-layer algorithm is proposed to minimize BPE by optimizing hybrid beamformers. Then, the optimal power allocation strategy is obtained to maximize the sum achievable rate of the network. 2) For the case of users randomly distributed, a beam-splitting scheme is proposed, where two subbeamformers are extracted from the single beamformer to serve different users in the same group. An alternating optimization (AO) algorithm is proposed for hybrid beamformer optimization, and the optimal power allocation is also derived. Numerical results validate that: 1) the proposed beamforming schemes exhibit superior performance compared with the existing imperfect-resolution-based beamforming scheme and 2) the communication rate of the proposed transmission framework is sensitive to the imperfect distance knowledge of NUs but not to that of FUs.

A near-field simultaneous wireless information and power transfer (SWIPT) network is investigated, where the hybrid beamforming architecture is employed at the base station (BS) for information transmission while charging the energy harvesting users. A transmit power minimization problem is formulated by jointly optimizing the analog beamformer, the baseband digital information/energy beamformers, and the number of dedicated energy beams. To tackle the uncertain number of dedicated energy beams, a semidefinite relaxation-based rank-one solution construction method is proposed to obtain the optimal baseband digital beamformers under the fixed analog precoder. Based on the structure of the optimal baseband digital beamformers, it is proved that no dedicated energy beam is required in the near-field SWIPT. To further exploit this insight, a penalty-based two-layer (PTL) algorithm is proposed to optimize the analog beamformer and baseband digital information beamformers. By employing the block coordinate descent method, the optimal analog beamformer, and baseband digital information beamformers are obtained in the closed-form expressions. Moreover, to reduce the high computational complexity caused by the large number of antennas, a low-complexity two-stage algorithm is proposed. Numerical results illustrate that: 1) the proposed PTL algorithm can achieve near-optimal performance and 2) in contrast to the far-field SWIPT, a single near-field beamformer can focus the energy on multiple locations.

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 Cramér-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.

The ultimate goal of next generation multiple access (NGMA) is to support massive terminals and facilitate multiple functionalities over the limited radio resources of wireless networks in the most efficient manner possible. However, the random and uncontrollable wireless radio environment is a major obstacle to realizing this NGMA vision. Given the prominent feature of achieving a 360° smart radio environment, simultaneously transmitting and reflecting surfaces (STARS) are emerging as one key enabling technology among the family of reconfigurable intelligent surfaces for NGMA. This article provides a comprehensive overview of the recent research progress of STARS, focusing on fundamentals, performance analysis, and full-space beamforming design, as well as promising employments of STARS in NGMA. In particular, we first introduce the basics of STARS by elaborating on the foundational principles and operating protocols as well as discussing different STARS categories and prototypes. Moreover, we systematically survey the existing performance analysis and beamforming design for STARS-aided wireless communications in terms of diverse objectives and different mathematical approaches. Given the superiority of STARS, we further discuss advanced STARS applications as well as the attractive interplay between STARS and other emerging techniques to motivate future works for realizing efficient NGMA.

Multiple-antenna technologies are advancing toward the development of extremely large aperture arrays and the utilization of extremely high frequencies, driving the progress of next-generation multiple access (NGMA). This evolution is accompanied by the emergence of near-field communications (NFCs), characterized by spherical-wave propagation, which introduces additional range dimensions to the channel and enhances system throughput. In this context, a tutorial-based primer on NFC is presented, emphasizing its applications in multiuser communications and multiple access (MA). The following areas are investigated: 1) the commonly used near-field channel models are reviewed along with their simplifications under various near-field conditions; 2) building upon these models, the information-theoretic capacity limits of NFC-MA are analyzed, including the derivation of the sum-rate capacity and capacity region, and their upper limits for both downlink and uplink scenarios; and 3) a detailed investigation of near-field multiuser beamforming design is presented, offering low-complexity and effective NFC-MA design methodologies in both the spatial and wavenumber (angular) domains. Throughout these investigations, near-field MA is compared with its far-field counterpart to highlight its superiority and flexibility in terms of interference management, thereby laying the groundwork for achieving NGMA.

Unmanned aerial vehicle (UAV) assisted communications have been regarded as an effective solution to provide instant services. This paper proposes a novel reconfigurable intelligent surface (RIS) assisted multi-UAV system, where ground users are formed as multicast groups and served by multiple UAVs with coordinated multi-point technique. The goal is to maximize the sum of the minimum rates for all groups by jointly optimizing the trajectories, the cooperative beamforming of the clustered UAVs, and the passive beamforming of the RIS. A hybrid learning scheme is proposed, integrating a multi-agent deep reinforcement learning algorithm, RES-QMIX, and a majorization-minimization (MM)-based alternating optimization. First, the RES-QMIX algorithm is proposed to optimize the trajectories of all UAVs. Then, the alternating optimization is invoked to decouple the joint beamforming into two sub-problems, and each is transformed into a convex quadratic cone programming problem with the MM algorithm. Moreover, the alternating optimization is employed to estimate the reward of the action in RES-QMIX algorithm, thus reducing the action space and achieving the joint optimization. Numerical results show that: 1) The proposed hybrid learning framework achieves fast convergence and outperforms heuristic algorithms; 2) The proposed system obtains a more significant communication rate than CoMP and RIS-only systems.

True-time delayers (TTDs) are popular components for hybrid beamforming architectures to combat the spatial-wideband effect in wideband near-field communications. In this paper, aserialand ahybrid serial-parallelTTD configuration are investigated for hybrid beamforming architectures. Compared to the conventional parallel configuration, the serial configuration exhibits acumulativetime delay caused by multiple TTDs, which potentially alleviates the maximum delay requirements on the individual TTDs. However, independent control of individual TTDs becomes impossible in the serial configuration. Therefore, a hybrid TTD configuration is proposed as a compromise solution. Furthermore, a power equalization approach is proposed to address the cumulative insertion loss of the serial and hybrid TTD configurations. Moreover, the wideband near-field beamforming design for different configurations is studied to maximize the spectral efficiency in both single-user and multiple-user systems. 1) For single-user systems, a closed-form solution for the beamforming design is derived. The preferred user locations and the required maximum time delay of each TTD configuration are characterized. 2) For multi-user systems, a penalty-based iterative algorithm is developed to obtain a stationary point of the spectral efficiency maximization problem for the considered TTD configurations. In addition, a hybrid-forward-and-backward (HFB) implementation is proposed to enhance the performance of the serial configuration. Our numerical results confirm the effectiveness of the proposed designs and unveil that i) compared to the conventional parallel configuration, both the serial and hybrid configurations can significantly reduce the maximum time delays required for the individual TTDs and ii) the hybrid configuration excels in single-user systems, while the HFB serial configuration is preferred in multi-user systems.

A joint cascaded channel estimation scheme is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) systems with hardware imperfections. In particular, the practical hybrid near- and far-field electromagnetic radiation with spatial non-stationarity is investigated. By exploiting the cascaded channel correlations between different users and between different STAR-RIS elements, a multi-task learning (MTL)-based channel estimation framework is proposed. This framework is capable of estimating the cascaded channels for transmission and reflection simultaneously based on noisy observations of the mixture channel. Following the design guideline of the proposed MTL framework, an efficient multi-task network (MTN) is developed to reconstruct the high-dimensional channels with limited pilot overhead. In the proposed MTN architecture, a mixed convolution and multilayer perception module is exploited to capture the effective hybrid-field channel features. This module integrates the locality bias modeling of the channel-wise convolution and the long-range dependency modeling of MLP, which finely learns both local spatial correlations and specific spatial non-stationarity of the hybrid-field cascaded channels. Numerical results show that the proposed MTN achieves superior channel estimation accuracy with less training overhead compared with the existing state-of-the-art benchmarks, in terms of required pilots, computations, and network parameters.

The three-dimensional (3D) beamforming property of the uniform circular array (UCA) in near-field wideband communication systems is comprehensively analyzed in both the distance and angular domains. It is rigorously demonstrated that the beam focal point only exists at a specific frequency in wideband UCA systems, resulting in significant beamforming loss. To facilitate near-field beamfocusing and alleviate the beam squint effect, the true-time delay (TTD)-based beamforming architecture is exploited. In particular, two wideband beamforming optimization approaches leveraging TTD units are proposed. 1)Analytical approach: In this approach, the phase shifters (PSs) and the time delay of TTD units are designed based on the analytical formula for beamforming gain. Following this design, the minimum number of TTD units required to achieve a predetermined beamforming gain is quantified. 2)Joint-optimization approach: In this method, the PSs and the TTD units are jointly optimized under practical maximum delay constraints to approximate the optimal unconstrained analog beamformer. Specifically, an efficient alternating optimization algorithm is proposed, where the PSs and the TTD units are alternately updated using either the closed-form solution or the low-complexity linear search approach. Extensive numerical results demonstrate that 1) the proposed beamforming schemes effectively mitigate the beam squint effect, and 2) the joint-optimization approach outperforms the analytical approach in terms of array gain and achievable spectral efficiency.

An active-sensing-based sense-then-train (STT) scheme is proposed for beam training in near-field multiple-input multiple-output (MIMO) systems. Compared to conventional codebook-based schemes, the proposed STT scheme is capable of not only addressing the complex spherical-wave propagation but also effectively exploiting the additional degrees-of-freedoms (DoFs). The STT scheme is tailored for both single-beam and multi-beam cases. 1) For the single-beam case, the STT scheme first utilizes a sensing phase to estimate a low-dimensional representation of the near-field MIMO channel in the truncated wavenumber domain. Then, in the subsequent training phase, the neural network modules at transceivers are updated online to align beams, utilizing sequentially received ping-pong pilots. This approach can efficiently obtain the aligned beam pair without relying on predefined codebooks or training datasets. 2) For the multi-beam case, based on the single-beam STT, a Gram-Schmidt method is further utilized to guarantee the orthogonality between beams in the training phase. Numerical results unveil that 1) the proposed STT scheme can significantly enhance the beam training performance in the near field compared to the conventional far-field codebook-based schemes, and 2) the proposed STT scheme can perform fast and low-complexity beam training, while achieving a near-optimal performance without full channel state information in both cases.

A bidirectional integrated sensing and communication (ISAC) system is proposed, in which a pair of transceivers carry out two-way communication and mutual sensing. Both full-duplex and half-duplex operations in narrowband and wideband systems are conceived for the bidirectional ISAC. 1) For the narrowband system, the conventional full-duplex and half-duplex operations are redesigned to take into account sensing echo signals. Then, the transmit beamforming design of both transceivers is proposed for addressing the sensing and communication (S&C) tradeoff. A one-layer iterative algorithm relying on successive convex approximation (SCA) is proposed to obtain Karush-Kuhn-Tucker (KKT) optimal solutions. 2) For the wideband system, the new full-duplex and half-duplex operations are proposed for the bidirectional ISAC. In particular, the frequency-selective fading channel is tackled by delay pre-compensation and path-based beamforming. By redesigning the proposed SCA-based algorithm, the KKT optimal solutions for path-based beamforming for characterizing the S&C tradeoff are obtained. Finally, the numerical results show that: i) For both bandwidth scenarios,full-duplex mode may not always be preferable to half-duplex modedue to the presence of the sensing interference; and ii) For both duplex operations, it is sufficient to reuse communication signals for sensing in the narrowband system, while an additional dedicated sensing signal is required in the wideband system.

A multi-scale attention based channel estimation framework is proposed for reconfigurable intelligent surface (RIS) aided massive multiple-input multiple-output systems, in which hardware imperfections and time-varying characteristics of the cascaded channel are investigated. By exploiting the spatial correlations of different scales in the RIS reflection element domain, we construct a Laplacian pyramid attention network (LPAN) to realize the high-dimensional cascaded channel reconstruction with limited pilot overhead. In LPAN, we leverage the multi-scale supervision learning to progressively capture the spatial correlations of the cascaded channel, where the attention mechanism based dual-branch architecture is designed. To balance network performance and complexity of LPAN, we further propose a lightweight LPAN-L architecture. In LPAN-L, the partial standard convolutional layers are decomposed into the group convolution, dilated convolution and point-wise convolution, which forms a sparse convolutional filter set to extract the channel feature with less computation cost. Furthermore, we leverage parameter sharing and recursion strategy to reduce the space complexity. Moreover, a selective fine-tuning strategy is developed to realize the domain adaption. Simulation results show that the proposed LPAN can achieve higher estimation accuracy than the existing estimation schemes, while the LPAN-L architecture with a close performance to LPAN efficiently reduces the network complexity1.

A simultaneously transmitting and reflecting surface (STARS) enabled two-phase integrated sensing and communications (ISAC) framework is proposed, where a novel bi-directional sensing-STARS architecture is devised to facilitate the full-space communication and sensing in a time-switching manner. Based on the proposed framework, a joint optimization problem is formulated, where the Cram$\acute {\text {e}}\text{r}$-Rao bound (CRB) for estimating the 2-dimension direction-of-arrival of the sensing target is minimized. Two cases are considered for sensing performance enhancement. 1) For the two-user case with the fixed number of sensors, an alternating optimization algorithm is proposed. In particular, the maximum number of deployable sensors is obtained in the closed-form expressions, where the maximum number of sensors is revealed to be only relevant to the QoS requirements of communications. 2) For the multi-user case with the variable number of sensors, an extended CRB (ECRB) metric is proposed to characterize the impact of the number of sensors on the sensing performance. A generic decoupling approach is proposed to convexify the non-convex ECRB expression. Based on this, a novel penalty-based double-loop (PDL) algorithm is proposed. Simulation results reveal that 1) the proposed PDL algorithm achieves a near-optimal performance with consideration of sensor deployment; 2) it is preferable to deploy more passive elements than sensors in terms of achieving optimal sensing performance.

A novel dynamic hybrid beamforming architecture is proposed to achieve the spatial multiplexing-power consumption tradeoff for near-field multiple-input multiple-output (MIMO) networks, where a switch module is integrated between the baseband digital and analog phase-shift module to control the number of activated RF chains. Based on this architecture, an optimization problem is formulated that maximizes the sum of achievable rates while minimizing the hardware power consumption. Both continuous and discrete phase shifters are considered. 1) For continuous phase shifters, a wavenumber-domain weighted minimum mean-square error (WD-WMMSE) algorithm is proposed, which exploits the sparsity of WD near-field channels to achieve the low-dimensional beamformer design. 2) For discrete phase shifters, a penalty-based layered iterative (PLI) algorithm is proposed. The closed-form analog and baseband digital beamformers are derived in each iteration. Simulation results demonstrate that: 1) the proposed dynamic beamforming architecture outperforms the conventional fixed hybrid beamforming architecture in terms of spatial multiplexing-power consumption tradeoff, and 2) the proposed algorithms achieve better performance than the other baseline schemes.

A wideband simultaneously transmitting and reflecting surface (STARS) aided terahertz (THz) communication system is proposed. The spatial wideband effect at the base station (BS) and STARS leads to significant performance degradation due to the beam split issue. To address this, true time delayers (TTDs) are introduced into the conventional hybrid beamforming structure for facilitating wideband beamforming. The hybrid beamforming at the BS and the passive beamforming at the STARS are jointly designed to maximize the spectral efficiency of the proposed system. A double-loop iterative algorithm based on penalty dual decomposition is proposed to solve the resulting optimization problem. Finally, the numerical results confirm the effectiveness of exploiting STARS in wideband THz systems.

A near-field wideband multi-user communication system is studied. To eliminate the near-field beam split caused by the wideband spatial effect, a hybrid beamforming architecture based on true-time delayers (TTDs) is exploited. A heuristic two-stage approach is proposed for optimizing the analog and digital beamformers of the TTD-based hybrid beamforming architecture to facilitate near-field wideband beamfocusing. In particular, in the first stage, a closed-form analog beamformer design based on a piecewise-near-field approximation is proposed to maximize the array gain at users. Next, in the second stage, the digital beamformers are optimized by exploiting the successive convex approximation. Finally, our numerical results demonstrate that the proposed approach can effectively eliminate the near-field beam split and outperforms the existing approach in terms of spectral efficiency.

A non-orthogonal multiple access (NOMA)-aided joint communication, sensing, and multi-tier computing (JCSMC) framework is proposed. In this framework, a multi-functional base station (BS) simultaneously carries out target sensing and provide edge computing services to the nearby users. To enhance the computation efficiency, the multi-tier computing structure is exploited, where the BS can further offload the computation tasks to a powerful Cloud server (CS). The potential benefits of employing NOMA in the proposed JCSMC framework are investigated, which can maximize the computation offloading capacity and suppress inter-functionality interference. Based on the proposed framework, the transmit beamformer of the BS and computing resource allocation among the BS and CS are jointly optimized to maximize the computation rate subject to the communication-computation causality and the sensing quality constraints. Both partial and binary computation offloading modes are considered: 1) For the partial offloading mode, a weighted minimum mean square error based alternating optimization algorithm is proposed to solve the corresponding non-convex optimization problem. It is proved that a Karush–Kuhn–Tucker optimal solution can be obtained; 2) For the binary offloading mode, the resultant highly-coupled mixed-integer optimization problem is first transformed to an equivalent but more tractable form. Then, the reformulated problem is solved by utilizing the alternating direction method of multipliers approach to obtain a nearly optimal solution. Finally, numerical results verify the effectiveness of the proposed algorithms and reveal that: i) the computation rate can be significantly enhanced by exploiting the multi-tier computing architecture when the BS is resource-limited, and ii) the proposed NOMA-aided JSCMC framework is superior in inter-functionality interference management and can achieve high-quality sensing and computing performance simultaneously compared with other benchmark schemes.

Abstract Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been attracting significant attention in both academia and industry for their advantages of achieving 360° coverage and enhanced degrees-of-freedom. This article first identifies the fundamentals of STAR-RIS, by discussing the hardware models, channel models, and signal models. Then, three representative categorizing approaches for STAR-RISs are introduced from the phase-shift, directional, and energy consumption perspectives. Furthermore, the beamforming design of STAR-RISs is investigated for both independent and coupled phase-shift cases. As a recent advance, a general optimization framework, which has high compatibility and provable optimality regardless of the application scenarios, is proposed. As a further advance, several promising applications are discussed to demonstrate the potential benefits of applying STAR-RISs in sixth-generation wireless communication. Lastly, a few future directions and research opportunities are highlighted.

A simultaneously transmitting and reflecting surface (STARS) enabled integrated sensing and communications (ISAC) framework is proposed, where the entire space is partitioned by STARS into a sensing space and a communication space. A novel sensing-at-STARS structure is proposed, where dedicated sensors are mounted at STARS to address the significant path loss and clutter interference of sensing. The Cramér-Rao bound (CRB) of the two-dimensional (2D) direction-of-arrivals (DOAs) estimation of the sensing target is derived, which is then minimized subject to the minimum communication requirement. A novel approach is proposed to transform the complicated CRB minimization problem into a trackable modified Fisher information matrix (FIM) optimization problem. Both independent and coupled phase-shift models of STARS are investigated: 1) For the independent phase-shift model, to address the coupling problem of ISAC waveform and STARS coefficient, an efficient double-loop iterative algorithm based on the penalty dual decomposition (PDD) framework is conceived; 2) For the coupled phase-shift model, based on the PDD framework, a low complexity alternating optimization algorithm is proposed to tackle the coupled phase-shift constraint by alternately optimizing the amplitude and phase-shift coefficients of STARS with closed-form expressions. Finally, the numerical results demonstrate that: 1) STARS significantly outperforms conventional RIS in terms of CRB under the communication constraints; 2) coupled phase-shift model achieves comparable performance to the independent one for low communication requirements or sufficient STARS elements; 3) it is more efficient to increase the number of passive elements of STARS than the active elements of the sensor; 4) higher sensing accuracy can be achieved by STARS using the practical 2D maximum likelihood estimator compared with the conventional RIS.

A non-orthogonal multiple access (NOMA)-aided joint communication, sensing, and multi-tier computing (JCSMC) framework is proposed. In this framework, a multi-functional base station (BS) simultaneously carries out target sensing and provide edge computing services to the nearby users. To enhance the computation efficiency, the multi-tier computing structure is exploited, where the BS can further offload the computation tasks to a powerful Cloud server (CS). The potential benefits of employing NOMA in the proposed JCSMC framework are investigated. Based on the proposed framework, the transmit beamformer of the BS and computing resource allocation among the BS and CS are jointly optimized to maximize the computation rate subject to the communication-computation causality and the sensing quality constraints. A weighted minimum mean square error based alternating optimization algorithm is proposed to solve the corresponding non-convex optimization problem. Fi-nally, numerical results show the significant performance gain achieved by the proposed schemes over the benchmark schemes.

A non-orthogonal multiple access (NOMA) inspired integrated sensing and communication (ISAC) system is investigated. A dual-functional base station (BS) serves multiple communication users while sensing multiple targets, by trans-mitting the non-orthogonal superposition of the communication and sensing signals. A NOMA inspired interference cancellation scheme is proposed, where part of the dedicated sensing signal is treated as the virtual communication signals to be mitigated at each communication user via successive interference cancellation (SIC). Based on this framework, the transmitted communication and sensing signals are jointly optimized to match the desired sensing beampattern, while satisfying the minimum rate requirement and the SIC condition at the communication users. Then, the formulated non-convex optimization problem is solved by invoking the successive convex approximation (SCA) to obtain a near-optimal solution. The numerical results show the proposed NOMA-inspired ISAC system can achieve better performance than the conventional ISAC system and comparable performance to the ideal ISAC system where all sensing interference is assumed to be removed unconditionally.

A simultaneously transmitting and reflecting intelligent surface (STARS) enabled integrated sensing and communications (ISAC) framework is proposed, where the whole space is divided by STARS into a sensing space and a communication space. A novel sensing-at-STARS structure, where dedicated sensors are installed at the STARS, is proposed to address the significant path loss and clutter interference for sensing. The Cramér-Rao bound (CRB) of the 2-dimension (2D) direction-of-arrivals (DOAs) estimation of the sensing target is derived, which is then minimized subject to the minimum communication requirement. A novel approach is proposed to transform the complicated CRB minimization problem into a trackable modified Fisher information matrix (FIM) optimization problem. Moreover, to address the coupled issue in the modified FIM, an efficient double-loop iterative algorithm based on the penalty dual decomposition method is conceived. The numerical results demonstrate that: 1) STARS significantly outperforms the conventional transmitting/reflecting-only intelligent surface; 2) High sensing accuracy can be achieved by STARS using the practical 2D maximum likelihood estimator.