Reconfigurable intelligent surfaces (RISs) have been extensively applied in integrated sensing and communication (ISAC) systems due to the capability of enhancing physical layer security (PLS). However, conventional static RIS architectures lack the flexibility required for adaptive beam control in multi-user and multifunctional scenarios. To address this issue without introducing additional hardware complexity and power consumption, in this paper, we exploit a movable RIS (MRIS) architecture, which consists of a large fixed sub-surface and a smaller movable sub-surface that slides on the fixed sub-surface to achieve dynamic beam reconfiguration with static phase shifts. This paper investigates an MRIS-assisted ISAC system under imperfect sensing estimation, where dedicated radar signals serve as artificial noise to enhance secure transmission against potential eavesdroppers (Eves). The transmit beamforming vectors, MRIS phase shifts, and relative positions of the two sub-surfaces are jointly optimized to maximize the minimum secrecy rate, ensuring robust secrecy performance for the weakest user under the uncertainty of the Eves’ channels. To handle the non-convexity, a convex bound is derived for the Eve channel uncertainty, and the$\mathcal {S}$-procedure is employed to reformulate semi-infinite constraints as linear matrix inequalities. An efficient alternating optimization and penalty dual decomposition-based algorithm is developed. Simulation results demonstrate that the proposed MRIS architecture substantially improves secrecy performance, especially when only a small number of elements are allocated to the movable sub-surface.

Federated distillation (FD), which inherits the privacy-preserving nature of federated learning (FL), has recently attracted increasing attention due to its communication efficiency in training the global model through logits aggregation. However, FD performance suffers from slow convergence due to statistically heterogeneous data and failures in logits transmission from resource-constrained clients over unreliable wireless links. Client selection and efficient resource allocation are typical methods to speed up the convergence in FD. However, most existing works have not considered the inherent inter-dependencies between these two methods. To address this issue, in this paper, we propose a Stackelberg game-based framework to optimally balance client selection and resource allocation. Specifically, client selection is formulated as the leader-level problem to reduce the number of required communication rounds. Subsequently, resource allocation is formulated as the follower-level problem to maximize their successful uploading rates in each round. By decomposing the follower-level problem into three subproblems, the closed-form solutions of transmission power, computation frequency, and the number of uploaded logits allocations are derived through monotonicity analysis. To solve the leader-level problem, we first derive the upper bound of the convergence of the FD global loss. Based on this, an uncertainty-based client selection scheme and an attention-based logits sampling method are proposed to optimally solve the leader’s optimization problem. Finally, the Stackelberg equilibrium is reached when all selected clients can successfully upload logits to the server. Simulation results demonstrate that the proposed Stackelberg equilibrium solutions significantly enhance the global model convergence speed.

Semantic communications, which focus on transmitting the semantic meaning of data, have been proposed as a novel paradigm for achieving efficient and relevant communication. Meanwhile, non-orthogonal multiple access (NOMA) enhances spectral efficiency by allowing multiple users to share the same spectrum. However, semantic communications are unlikely to fully replace conventional bit-level communications in the near future, as the latter remain dominant. Therefore, integrating semantic users into a NOMA network alongside conventional bit-based users becomes a meaningful approach to improve both transmission and spectrum efficiency. Nonetheless, due to the lack of a mathematical model that accurately characterizes the relationship between the performance of semantic transceivers and wireless resource allocation, enhancing performance through resource optimization remains a challenge. Moreover, successive interference cancellation (SIC), a key technique in NOMA, introduces additional complexity in system design and implementation. To address these challenges, this paper first improves the deep semantic communication (DeepSC) transceiver to make it adaptive to varying wireless transmission conditions. Subsequently, a data-driven regression approach is employed to develop a mathematical model that captures the impact of wireless resources on semantic transceiver performance. In parallel, a multi-cluster hybrid NOMA (H-NOMA) framework is proposed, where each cluster consists of one semantic user and one bit-based user, to mitigate the complexity introduced by SIC. A total transmit power minimization problem is then formulated by jointly optimizing the beamforming design, bandwidth allocation, and semantic symbol factor. The formulated problem is non-convex and challenging to solve directly. To tackle this, a closed-form optimal solution for the beamforming vectors is first derived. Then, a block coordinate descent (BCD)-based algorithm is developed to determine the bandwidth allocation, while an exhaustive search method is used to optimize the semantic symbol factor. Simulation results illustrate the advantages of the semantic communication over the conventional bit-level communication and verify the superior performance of the proposed framework compared with existing benchmark schemes.

Abstract Non‐orthogonal multiple access (NOMA) technique introduces spectrum cooperation among different users and devices, which improves spectrum efficiency significantly. Energy‐limited devices benefit from the backscatter (BAC) technique to transmit signals without extra energy consumption. The combination of NOMA and BAC provides a promising solution for Internet of Things (IoT) networks, where massive devices simultaneously transmit and receive signals. This study investigates a system model with two NOMA downlink users and an uplink device. The aim is to maximise the data rate of the uplink device by optimising the power allocation coefficient and the backscattering coefficient. Meanwhile the quality of service requirements of two NOMA users are guaranteed. The closed‐form solution of two optimisation variables is derived, and an alternating algorithm is also proposed to solve the formulated optimisation problem efficiently. The proposed system verifies the feasibility of IoT devices being added into existing networks and provides a promising solution for wireless communication networks in the future.