By enabling spectrum sharing between radar and communication operations, the cell-free dual-functional radar–communication (CF-DFRC) system is a promising candidate to significantly improve spectrum efficiency in future sixth-generation (6G) wireless networks. However, in wideband scenarios, synchronization errors caused by carrier frequency offset (CFO) can severely reduce both communication capacity and sensing accuracy, especially when multiple geographically distributed full-duplex (FD) access points (APs) are jointly coordinated. In this paper, we consider a wideband FD CF-DFRC system where each AP is equipped with movable antennas (MAs). This setting is fundamentally different from existing DFRC or MA-aided designs that typically assume fixed-position antennas, half-duplex operation, or perfect synchronization. First, we develop a field-response-based channel model and derive a worst-case weighted communication–sensing rate (WCSR) that explicitly captures the impact of inter-AP CFO on both the uplink communication signal-to-interference-plus-noise ratio (SINR) and the radar echo SINR. Our analysis reveals that CFO increases the Cramér–Rao lower bound (CRLB) of target position estimation, thereby degrading sensing accuracy. Based on this characterization, we formulate a robust worst-case WCSR maximization problem that jointly optimizes MA positions, transmit beamforming vectors, receive filters, and CFO-related parameters under transmit power and MA-position constraints. To tackle the resulting highly non-convex problem, we propose a two-stage robust optimization framework. In the first stage, we employ fractional programming together with manifold optimization (MO) and penalty dual decomposition (PDD) to solve the worst-case CFO subproblem on the complex unit-modulus manifold, thus obtaining a CFO-robust closed-form structure for the WCSR. In the second stage, we design a meta–reinforcement learning (MRL) based resource allocation scheme that jointly optimizes the MA positions and beamforming vectors in a data-driven manner for dynamic wireless environments. Unlike conventional deep reinforcement learning (DRL) methods, the proposed MRL framework learns a meta-policy that can rapidly adapt to varying channel and CFO realizations, substantially improving convergence speed and scalability. Simulation results show that the proposed robust MO–PDD–MRL framework significantly outperforms existing DRL-based and non-robust CF-DFRC schemes in terms of both communication and sensing performance under CFO impairments. Furthermore, compared to fixed-position antenna (FPA) architectures, the MA-aided CF-DFRC system exhibits markedly enhanced robustness and adaptability to CFO effects and target mobility.

The integration of sensing, communication, and power transfer (ISCPT) has emerged as a promising paradigm for energy- and spectrum-efficient 6G networks. Recent studies have revealed that sensing accuracy, achievable rate, and harvested energy inherently exhibit conflicting design requirements and form a nontrivial trade-off region. However, existing integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT) schemes typically optimize at most two of these functionalities and lack a unified resource-allocation framework that can flexibly balance all three under stringent power budgets. Motivated by this gap, we consider a reconfigurable intelligent surface (RIS)-aided ISCPT system that employs non-orthogonal multiple access (NOMA) to support multi-user connectivity. In the proposed design, the RIS reshapes the wireless propagation environment in an energy-efficient manner to enhance both sensing and power transfer, while NOMA provides power-domain multiplexing to improve spectral efficiency and user scalability. We formulate a total transmit power minimization problem by jointly optimizing the base-station beamforming, RIS phase shifts, power splitting (PS) ratios, and NOMA decoding order under quality-of-service (QoS), Cramér–Rao-bound-based sensing accuracy, and energy-harvesting constraints. The resulting problem is highly non-convex due to the coupling among the design variables. To solve it efficiently, we develop a block coordinate descent (BCD)-based algorithm that leverages semidefinite relaxation (SDR), successive convex approximation (SCA), and the alternating direction method of multipliers (ADMM). Simulation results verify that the proposed RIS-aided NOMA-ISCPT framework significantly reduces the base-station transmit power while achieving favorable trade-offs among communication reliability, sensing precision, and energy-transfer efficiency.

Sixth-generation (6G) wireless networks have been driving growing demands for the full utilization of spectral efficiency and spatial degrees of freedom (DoFs). This paper investigates a non-orthogonal multiple access (NOMA) empowered integrated sensing and communication (ISAC) system assisted by movable antennas (MAs). We consider a dual functional radar and communication (DFRC) base station (BS) equipped with a two-dimensional (2D) MA array, which simultaneously senses multiple targets and serves users divided into multiple clusters. Successive interference cancellation (SIC) is employed within each cluster to suppress intra-cluster interference. To enhance the total illumination power at the sensing targets while guaranteeing the communication signal-to-interference-plus-noise-ratio (SINR) requirements at the users, we formulate an optimization problem for joint power allocation, beamforming, and antenna position design. To address this highly coupled and non-convex problem, an alternating optimization-based algorithm is proposed. We first determine the SIC decoding order by the equivalent-channel-to-interference-plus-noise-ratios (ECINRs), and derive the close-form solutions of the optimal intra-and-inter cluster power allocation coefficients. The sub-problems of beamforming and antenna position design are solved by semidefinite relaxation (SDR) and successive convex approximation (SCA) based schemes, respectively. Numerical simulation results are provided to verify the effectiveness of the proposed algorithm. The proposed algorithm significantly outperforms baseline schemes, which achieves approximately 2 dB illumination power gain compared to the conventional fixed position antennas (FPA), demonstrating the promising potential of MAs in wireless networks.

The cell-free integrated sensing and communication (CF-ISAC) system, which effectively mitigates intra-cell interference and provides precise sensing accuracy, is a promising technology for future 6G networks. However, to fully capitalize on the potential of CF-ISAC, accurate time synchronization (TS) between access points (APs) is critical. Due to the limitations of current synchronization technologies, TS errors have become a significant challenge in the development of the CF-ISAC system. In this paper, we propose a novel CF-ISAC architecture based on movable antennas (MAs), which exploits spatial diversity to enhance communication rates, maintain sensing accuracy, and reduce the impact of TS errors. We formulate a worst-case sensing accuracy optimization problem for TS errors to address this challenge, deriving the worst-case Cramér-Rao lower bound (CRLB). Subsequently, we develop a joint optimization framework for AP beamforming and MA positions to satisfy communication rate constraints while improving sensing accuracy. A robust optimization framework is designed for the highly complex and non-convex problem. Specifically, we employ manifold optimization (MO) to solve the worst-case sensing accuracy optimization problem. Then, we propose an MA-enabled meta-reinforcement learning (MA-MetaRL) to design optimization variables while satisfying constraints on MA positions, communication rate, and transmit power, thereby improving sensing accuracy. The simulation results demonstrate that the proposed robust optimization algorithm significantly improves the accuracy of the detection and is strong against TS errors. Moreover, compared to conventional fixed position antenna (FPA) technologies, the proposed MA-aided CF-ISAC architecture achieves higher system capacity, thus validating its effectiveness.

This paper investigates a movable antenna (MA)-aided integrated sensing and communication (ISAC) system using non-orthogonal multiple access (NOMA). A base station (BS) configured with a two-dimensional MA array simultaneously serves multiple communication users and sensing multiple targets. To enhance system capacity, superimposed symbols are transmitted to communication users, with successive interference cancellation (SIC) employed for signal decoding. Our objective is to maximize the total illumination power at the targets while satisfying the minimum signal-to-interference-plus-noise ratio (SINR) requirements for communication users. To achieve this goal, we propose an alternating optimization (AO)-based algorithm that jointly optimizes the transmit power allocation, beamforming, sensing covariance matrix, and antenna positions. Numerical results show that the MA system achieves significant improvement in illumination power compared to fixed-position antennas (FPAs), with particularly significant gains under high SINR requirements.

Effective elements selection (ES) is essential to the deployment of reconfigurable intelligent surface (RIS), which, however, receives little attention. In this letter, we propose two novel ES strategies intended for deployment in RIS-assisted multiuser multiple-input-single-output (MISO) wireless networks. The first scheme is designed to maximize the effective gains of channels, while the second ES scheme presents a linear swapping selection (LSS) method that focuses on optimizing the total achievable rate. Numerical results show that the second scheme using the LSS method performs better than the first one, and is able to achieve a near-optimal performance but with significantly reduced computational complexity compared with the optimal exhaustive search scheme.

In this paper, we investigate a novel integrated sensing and communication (ISAC) system aided by movable antennas (MAs). A bistatic radar system, in which the base station (BS) is configured with MAs, is integrated into a multi-user multiple-input-single-output (MU-MISO) system. Flexible beamforming is studied by jointly optimizing the antenna coefficients and the antenna positions. Compared to conventional fixed-position antennas (FPAs), MAs provide a new degree of freedom (DoF) in beamforming to reconfigure the field response, and further improve the received signal quality for both wireless communication and sensing. We propose a communication rate and sensing mutual information (MI) maximization problem by flexible beamforming optimization. The complex fractional objective function with logarithms are first transformed with the fractional programming (FP) framework. Then, we propose an efficient algorithm to address the non-convex problem with coupled variables by alternatively solving four sub-problems. We derive the closed-form expression to update the antenna coefficients by Karush-Kuhn-Tucker (KKT) conditions. To improve the direct gradient ascent (DGA) scheme in updating the positions of the antennas, a 3-stage search-based projected GA (SPGA) method is proposed. Simulation results show that MAs significantly enhance the overall performance of the ISAC system, achieving 59.8% performance gain compared to conventional ISAC system enabled by FPAs. Meanwhile, the proposed SPGA-based method has remarkable performance improvement compared the DGA method in antenna position optimization.

Flexible antenna arrays (FAAs), distinguished by their rotatable, bendable, and foldable properties, are extensively employed in flexible radio systems to achieve customized radiation patterns. This paper aims to illustrate that FAAs, capable of dynamically adjusting surface shapes, can enhance communication performances with both omni-directional and directional antenna patterns, in terms of multi-path channel power and channel angle Cramér-Rao bounds. To this end, we develop a mathematical model that elucidates the impacts of the variations in antenna positions and orientations as the array transitions from a flat to a rotated, bent, and folded state, all contingent on the flexible degree-of-freedom. Moreover, since the array shape adjustment operates across the entire beamspace, especially with directional patterns, we discuss the sum-rate in the multi-sector base station that covers the 360° communication area. Particularly, to thoroughly explore the multi-sector sum-rate, we propose separate flexible precoding (SFP), joint flexible precoding (JFP), and semi-joint flexible precoding (SJFP), respectively. In our numerical analysis comparing the optimized FAA to the fixed uniform planar array, we find that the bendable FAA achieves a remarkable 156% sum-rate improvement compared to the fixed planar array in the case of JFP with the directional pattern. Furthermore, the rotatable FAA exhibits notably superior performance in SFP and SJFP cases with omni-directional patterns, with respective 35% and 281%.

In this paper, reconfigurable intelligent surface (RIS) is employed in a millimeter wave (mmWave) integrated sensing and communications (ISAC) system. To alleviate the multi-hop attenuation, the semi-self sensing RIS approach is adopted, wherein sensors are configured at the RIS to receive the radar echo signal. Focusing on the estimation accuracy, the Cramér-Rao bound (CRB) for estimating the direction-of-the-angles is derived as the metric for sensing performance. A joint optimization problem on hybrid beamforming and RIS phase shifts is proposed to minimize the CRB, while maintaining satisfactory communication performance evaluated by the achievable data rate. The CRB minimization problem is first transformed as a more tractable form based on Fisher information matrix (FIM). To solve the complex non-convex problem, a double layer loop algorithm is proposed based on penalty concave-convex procedure (penalty-CCCP) and block coordinate descent (BCD) method with two sub-problems. Successive convex approximation (SCA) algorithm and second order cone (SOC) constraints are employed to tackle the non-convexity in the hybrid beamforming optimization. To optimize the unit modulus constrained analog beamforming and phase shifts, manifold optimization (MO) is adopted. Finally, the numerical results verify the effectiveness of the proposed CRB minimization algorithm, and show the performance improvement compared with other baselines. Additionally, the proposed hybrid beamforming algorithm can achieve approximately 96% of the sensing performance exhibited by the full digital approach within only a limited number of radio frequency (RF) chains.

Near-field communications present new opportunities over near-field channels, however, the spherical wavefront propagation makes near-field signal processing challenging. In this context, this paper proposes efficient near-field channel estimation methods for wideband MIMO mmWave systems with the aid of extremely large-scale reconfigurable intelligent surfaces (XL-RIS). For the wideband signals reflected by the analog RIS, we characterize their near-field beam squint effect in both angle and distance domains. Based on the mathematical analysis of the near-field beam patterns over all frequencies, a wideband spherical-domain dictionary is constructed by minimizing the coherence of two arbitrary beams. In light of this, we formulate a two-dimensional compressive sensing problem to recover the channel parameter based on the spherical-domain sparsity of mmWave channels. To this end, we present a correlation coefficient-based atom matching method within our proposed multi-frequency parallelizable subspace recovery framework for efficient solutions. Additionally, we propose a two-dimensional oracle estimator as a benchmark and derive its lower bound across all subcarriers. Our findings emphasize the significance of system hyperparameters and the sensing matrix of each subcarrier in determining the accuracy of the estimation. Finally, numerical results show that our proposed method achieves considerable performance compared with the lower bound and has a time complexity linear to the number of RIS elements.

This paper investigates the potential of near-field localization using widely-spaced multi-subarrays (WSMSs) and analyzing the corresponding angle and range Cramér-Rao bounds (CRBs). By employing the Riemann sum, closed-form CRB expressions are derived for the spherical wavefront-based WSMS (SW-WSMS). We find that the CRBs can be characterized by the angular span formed by the line connecting the array’s two ends to the target, and the different WSMSs with same angular spans but different number of subarrays have identical normalized CRBs. We provide a theoretical proof that, in certain scenarios, the CRB of WSMSs is smaller than that of uniform arrays. We further yield the closed-form CRBs for the hybrid spherical and planar wavefront-based WSMS (HSPW-WSMS), and its components can be seen as decompositions of the parameters from the CRBs for the SW-WSMS. Simulations are conducted to validate the accuracy of the derived closed-form CRBs and provide further insights into various system characteristics. Basically, this paper underscores the high resolution of utilizing WSMS for localization, reinforces the validity of adopting the HSPW assumption, and, considering its applications in communications, indicates a promising outlook for integrated sensing and communications based on HSPW-WSMSs.

Millimeter wave (mmWave) full-duplex (FD) is a promising technique for improving capacity by maximizing the utilization of both time and the rich mmWave frequency resources. Still, it has restrictions due to FD self-interference (SI) and mmWave’s limited coverage. Therefore, this study dives into FD mmWave MIMO with the assistance of reconfigurable intelligent surfaces (RIS) for capacity improvement. First, we demonstrate the angular-domain reciprocity of FD antenna arrays under the far-field planar wavefront assumption. Accordingly, a strategy for joint downlink-uplink (DL-UL) channel estimation is presented. For estimating the SI channel, the direct channel, and the cascaded channel, the Khatri-Rao product-based compressive sensing (KR-CS), distributed CS (D-CS), and two-stage multiple measurement vector-based D-CS (M-D-CS) frameworks are proposed, respectively. Additionally, we propose a passive beamforming optimization solution based on the angular-domain cascaded channel. With hybrid beamforming architectures, a novel hybrid weighted minimum mean squared error method for SI cancellation (H-WMMSE-SIC) is proposed. Simulations have revealed that joint DL-UL processing significantly improves estimation performance in comparison to separate DL/UL channel estimation. Particularly, when the interference-to-noise ratio is less than 35 dB, our proposed H-WMMSE-SIC offers spectral efficiency performance comparable to fully-digital WMMSE-SIC. Finally, the computational complexity is analyzed for our proposed methods.

Double-reconfigurable intelligent surface (RIS) is a promising technique, achieving a substantial gain improvement compared to single-RIS techniques. However, in double-RIS-aided systems, accurate channel estimation is more challenging than in single-RIS-aided systems. This work solves the problem of double-RIS-based channel estimation based on active RIS architectures with only one radio frequency (RF) chain. Since the slow time-varying channels, i.e., the BS-RIS 1, BS-RIS 2, and RIS 1-RIS 2 channels, can be obtained with active RIS architectures, a novel multi-user two-timescale channel estimation protocol is proposed to minimize the pilot overhead. First, we propose an uplink training scheme for slow time-varying channel estimation, which can effectively address the double-reflection channel estimation problem. With channels’ sparisty, a low-complexity Singular Value Decomposition Multiple Measurement Vector-Based Compressive Sensing (SVD-MMV-CS) framework with the line-of-sight (LoS)-aided off-grid MMV expectation maximization-based generalized approximate message passing (M-EM-GAMP) algorithm is proposed for channel parameter recovery. For fast time-varying channel estimation, based on the estimated large-timescale channels, a measurements-augmentation-estimate (MAE) framework is developed to decrease the pilot overhead. Additionally, a comprehensive analysis of pilot overhead and computing complexity is conducted. Finally, the simulation results demonstrate the effectiveness of our proposed multi-user two-timescale estimation strategy and the low-complexity Bayesian CS framework.