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.

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.

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.

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 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.

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.

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.

In this work, we investigate a spectrum-sharing network, where an Internet of Things (IoT) system and a WiFi system coexist in the unlicensed band. The IoT devices can actively transmit signal by contending for the channel with the WiFi users. Alternatively, they can also convey information by passively backscattering the ambient WiFi signal, and thus form symbiosis with the WiFi during the transmission. Since the active and the passive transmission mode have their own merits under specific circumstances, how to select one of them adaptively and efficiently with guaranteeing the normal service of the WiFi system is of high importance. To solve this problem, we first investigate the effective throughput of the IoT system from the cross-layer perspective. Then, by jointly considering the effective throughput and the power consumption of the IoT system, we formulate the transmission mode selection problem. Two solutions are given depending on whether the signaling between the two systems is available. By entailing the intersystem parameter sharing, the model-based transmission mode selection scheme is proposed, which gives the closed-form optimal solution. When the intersystem parameter sharing is absent, the model-less transmission mode selection scheme is proposed by using the deep reinforcement learning (DRL) technique. Extensive simulation results validate the theoretical analysis, and demonstrate the effectiveness of the proposed transmission mode selection schemes.