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.

In future 6G networks, mobile edge computing (MEC) is envisioned to offer integrated computing, communication, and storage services at the network edge, enhancing both computational efficiency and communication quality. However, most existing MEC designs neglect the impact of jamming attacks, especially those from intelligent and adaptive jammers. To fill this important research gap, this paper investigates a jamming-resilient MEC framework that aims to improve communication reliability and reduce system delay under adversarial interference. Leveraging the emerging movable antenna (MA) technology, which allows dynamic adjustment of antenna positions and orientations, we propose a novel MA-assisted anti-jamming MEC architecture. Unlike existing works, our model explicitly considers the movement delay caused by MA, which is critical for practical deployment. We jointly optimize the MA positions at both the user equipment (UE) and the base station (BS), BS transmit beamforming, and task offloading ratios to minimize the total system delay. The resulting optimization problem is non-convex and highly coupled. Thus, we develop an efficient algorithm based on penalty dual decomposition (PDD) and successive convex approximation (SCA). Simulation results demonstrate that the proposed scheme significantly outperforms traditional fixed-position antenna (FPA) baselines in terms of jamming resilience and delay minimization, offering new insights into robust MEC system design for 6G networks.

Cooperative-integrated sensing and communication (C-ISAC) networks have emerged as promising solutions for communication and target sensing. However, imperfect channel state information (CSI) estimation and time synchronization (TS) errors degrade performance, affecting communication and sensing accuracy. This paper addresses these challenges by employing movable antennas (MAs) to enhance C-ISAC robustness. We analyze the impact of CSI errors on achievable rates and introduce a hybrid Cramer-Rao lower bound (HCRLB) to evaluate the effect of TS errors on target localization accuracy. Based on these models, we derive the worst-case achievable rate and sensing precision under such errors. We optimize cooperative beamforming, base station (BS) selection factor and MA position to minimize power consumption while ensuring accuracy. We then propose a constrained deep reinforcement learning (C-DRL) approach to solve this non-convex optimization problem, using a modified deep deterministic policy gradient (DDPG) algorithm with a Wolpertinger architecture for efficient training under complex constraints. To the best of our knowledge, this is the first work that jointly integrates TS errors and imperfect CSI into a unified MA-aided cooperative ISAC framework, providing a physically consistent model for both communication and sensing under dual uncertainties. Simulation results show that the proposed method significantly improves system robustness against CSI and TS errors, where robustness mean reliable data transmission under poor channel conditions. These findings demonstrate the potential of MA technology to reduce power consumption in imperfect CSI and TS environments.

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.

Device-to-device (D2D)-aided mobile edge computing (MEC) has emerged as a key enabling technology for future sixth-generation (6G) wireless networks. The goal of D2D-MEC is to reduce system latency for edge user equipments (UEs) by enabling access to cloud computing capabilities at the network edge, thereby supporting high transmission rates. To address the vulnerability of communication signals to physical obstructions, we employ relay techniques to enhance system performance and extend coverage. However, relay nodes and base station (BS) are typically equipped with large-scale antenna arrays, which lead to significant implementation costs and limiting practical deployment. To address this issue in a cost-efficient manner without sacrificing system performance, movable antenna (MA) technology is introduced. The key idea of MA technology lies in dynamically optimizing antenna positions to improve system capacity. Therefore, we propose a novel resource allocation framework for an movable relay-aided D2D-MEC system. The proposed scheme jointly optimizes the MA positions at UEs, relays, and the BS, along with the associated beamforming vectors, MEC server resource allocation, and computational task offloading rates. The objective is to minimize the maximum system latency while satisfying both computation and communication rate constraints. Furthermore, considering that current MA control mechanisms primarily rely on mechanical actuation, MA movement delay is incorporated into the latency model to capture the trade-off between antenna mobility and system delay. The resulting optimization problem is non-convex and involves multiple coupled variables. To solve this problem, we develop a parallel and distributed algorithm based on the penalty dual decomposition (PDD) framework, which is further integrated with the successive convex approximation (SCA) method to obtain a suboptimal solution. Simulation results demonstrate that the proposed algorithm significantly reduces system latency and enhances overall efficiency compared to benchmark schemes employing conventional fixed-position antennas (FPAs) at the relays and BS.

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.

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.

For millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) communication systems, we propose an innovative near-field (NF) transmission framework based on dynamic metasurface antenna (DMA) technology. In this framework, a base station (BS) utilizes the DMA hybrid beamforming technology combined with the NOMA principle to maximize communication efficiency between near-field users (NUs) and far-field users (FUs). In conventional communication systems, obtaining channel state information (CSI) requires substantial pilot signals, significantly reducing system communication efficiency. We propose a beamforming design scheme based on position information to address with this challenge. This scheme does not depend on pilot signals but indirectly obtains CSI by analyzing the geometric relationship between user position information and channel models. However, in practical applications, the accuracy of position information is challenging to guarantee and may contain errors. We propose a robust beamforming design strategy based on the worst-case scenario to tackle this issue. Since this problem is a multi-variable coupled non-convex problem, we employ a dual-loop iterative joint optimization algorithm to update beamforming using block coordinate descent (BCD) and derive the optimal power allocation (PA) expression. We analyze its convergence and complexity to verify the proposed algorithm’s performance and robustness thoroughly. We validate the theoretical derivation of the CSI error bound through simulation experiments. Numerical results show that our proposed scheme performs better than traditional beamforming schemes. Additionally, the transmission framework exhibits strong robustness to NU and FU position errors, laying a solid foundation for the practical application of mmWave NOMA communication systems. The NF transmission framework for mmWave NOMA communication systems based on DMA technology proposed in this work shows significant advantages in improving communication sum rate, reducing reliance on pilot signals, and coping with position errors. This provides new insights for the future development of mmWave NOMA communication technology.

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.

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.

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.

In this paper, we focus on the sum-rate optimization in a multi-user millimeter-wave (mmWave) system with distributed intelligent reflecting surfaces (D-IRSs), where a base station (BS) communicates with users via multiple IRSs. The BS transmit beamforming, IRS switch vector, and phase shifts of the IRS are jointly optimized to maximize the sum-rate under minimum user rate, unit-modulus, and transmit power constraints. To solve the resulting non-convex optimization problem, we develop an efficient alternating optimization (AO) algorithm. Specifically, the non-convex problem is converted into three subproblems, which are solved alternatively. The solution to transmit beamforming at the BS and the phase shifts at the IRS are derived by using the successive convex approximation (SCA)-based algorithm, and a greedy algorithm is proposed to design the IRS switch vector. The complexity of the proposed AO algorithm is analyzed theoretically. Numerical results show that the D-IRSs-aided scheme can significantly improve the sum-rate and energy efficiency performance.