Delay guarantee is essential for wireless communication technology, which is beneficial for real-time data processing. Such guarantee manifests as hard delay constraint, whose resolution facilitates reliable task fulfillment within strict deadline while enabling efficient communication resource scheduling. In this paper, we investigate the problem of transmitting a certain amount of data within a deadline and optimizing the expected total energy in an Orthogonal Frequency Division Multiplexing (OFDM) system. We represent the problem as a finite-horizon stochastic dynamic optimization problem, and aim to derive decision rule for allocating communication transmission rates across subcarriers. We propose a method named Multicarrier Deterministic Approximation Method (MDA). First, to address the complexity of expectation computation, we approximate the expected value function. Subsequently, for the resulting deterministic optimization problem, we introduce auxiliary variable, and adopt low-complexity two-layer optimization framework. For the proposed method, we derive performance upper bound for deterministic parameter with specific value and asymptotic performance upper bound for deterministic parameter with general value. The performance bound theoretically demonstrates the superiority of the proposed method over both the equal rate method and the first slot method. Furthermore, it proves that increasing the deterministic parameter under relaxed delay constraint can achieve enhanced theoretical performance guarantee. Finally, the effectiveness of the proposed method is validated through simulations.

Accurate acquisition of channel state information (CSI) is crucial for achieving high-rate communication, yet it introduces significant training overhead and latency, particularly in high-mobility cell-free massive multiple-input multipleoutput (CF-mMIMO) communication systems with unmanned aerial vehicles (UAVs). To address this challenge, we propose a predictive beamforming and resource allocation framework that significantly reduces training overhead while enhancing system throughput. Specifically, a novel frame structure is designed in which uplink training is performed only in the first time slot of each beam tracking frame, while distributed beam tracking and predictive beamforming are applied in all subsequent slots using the extended Kalman filter (EKF) at each access point (AP). Moreover, we develop a centralized information fusion algorithm that exploits cell-free multi-point cooperation to improve estimation accuracy with low fronthaul overhead. Then, we derive the theoretical posterior Cram´er-Rao bound (PCRB) for the fused estimation and show that, under the local linear-Gaussian approximation, the predicted PCRB coincides with the covariance of the fused estimate. We further establish an explicit analytical mapping between the predicted PCRB and the uplink pilot length. Leveraging this theoretical bridge, we formulate a prediction-aware joint optimization problem involving uplink pilot length, downlink AP-user association, and power allocation to actively adapt the training overhead and maximize the effective sum spectral efficiency (SE). A low-complexity iterative algorithm based on fractional programming is proposed to solve this problem. Numerical results demonstrate that the proposed framework achieves a favorable trade-off between signaling overhead and system throughput. Specifically, it reduces the training overhead by 95% with only a 3% decrease in positioning accuracy, incurs only modest additional fronthaul overhead, and improves the effective sum SE by 31% compared to traditional schemes. Furthermore, comprehensive evaluations show that the framework remains effective under multipath fading and higher UAV velocities, and continues to benefit from cooperative gains in expanded network deployments.

Cell-free (CF) networks have attracted increasing attention for their effectiveness in mitigating inter-cell interference through cooperative transmission among distributed access points (APs). However, conventional terrestrial CF networks often lack spatial flexibility and struggle to adapt to dynamic environments. To overcome these limitations, we propose a new CF network served by unmanned aerial vehicles (UAVs) equipped with a three-dimensional (3D) rotatable antenna array. Combined with the UAV’s controllable 3D position, the resulting six-dimensional (6D) spatial reconfigurability enables the active beam steering of such aerial APs, thereby enhancing interference mitigation and dynamic user association. However, this design, referred to as 6D aerial rotatable antenna arrays (6DARAs), faces several critical challenges, such as high-dimensional coupled control variables, time-varying user positions, and increased channel state information (CSI) estimation overhead. To address these issues, we develop a two-timescale optimization framework that separates large-timescale 6DARA control (i.e., clustering, position, and rotation) from small-timescale signal processing. At the small-timescale, a closed-form team minimum mean-squared error decoder is derived using local and statistical CSI. At the large-timescale, 6DARA clustering is modeled as a local altruistic game and solved via a concurrent update algorithm, while 6DARA mobility is managed by an enhanced multi-agent reinforcement learning algorithm for efficient position and rotation adaptation under partial observability. Simulation results demonstrate that the proposed network and optimization framework significantly outperform existing baselines in terms of throughput, scalability, and robustness in dynamic environments.

This paper proposes a joint communication and computation scheme (JCCS) for cell-free multiple-input multiple-output (CF-MIMO) network to support federated learning (FL). The JCCS allows users to choose between a model update process or a data offloading process, where the offloaded data and the uploaded model gradient are respectively sent to the distributed processing units (DPUs) deployed on the access points (APs) for further model updates. Moreover, we define a performance metric called iteration error gap as the difference between model errors of adjacent iterations and decouple the total training time minimization problem into the error gap maximization problem within fixed time limit. Base on common machine learning (ML) assumptions, we derive a lower bound of iteration error gap, which is determined by the minimum batch size among all DPUs. An optimization problem aiming to maximize the minimum batch size is then formulated to jointly optimize the time division, power control, and user selection. By employing the block coordinate descent approach, we develop a new algorithm to solve the formulated non-convex mixed integer programming problem. Our simulation results verify the convergence of proposed algorithm and show that it reduces the relative error of a single FL iteration by more than 1.5 dB compared with other baseline schemes. Furthermore, the CF-MIMO network integrated with JCCS achieves a relative error reduction exceeding 1 dB per iteration when compared to collocated MIMO network. In addition, an FL example of handwritten digits classification shows that the JCCS indeed accelerates the convergence of FL model.

As a paradigm shift towards pervasive intelligence, semantic communication (SemCom) has shown great potentials to improve communication efficiency and provide user-centric services by delivering task-oriented semantic meanings. However, the exponential growth in connected devices, data volumes, and communication demands presents significant challenges for practical SemCom design, particularly in resource-constrained wireless networks. In this work, we propose a task-agnostic semantic communication (TASC) framework capable of supporting multimodal data across diverse tasks. To investigate the interplay between communication and intelligent tasks from an information-theoretic perspective, we introduce a distributed multimodal information bottleneck (DMIB) principle, which enables the extraction of minimal sufficient unimodal and multimodal representations by eliminating redundant information while preserving task-relevant semantics. To further reduce the communication overhead, we develop an adaptive semantic feature transmission method under dynamic channel conditions. Then, TASC is trained based on federated meta-learning (FML) to learn a well-initialized model for rapid adaptation and generalization. To gain deep insights, we conduct theoretical analysis and devise resource management to accelerate convergence while minimizing the training latency and energy cost. Moreover, we develop a joint user selection and resource allocation algorithm to address the non-convex problem with theoretical guarantees. Extensive simulation results validate the effectiveness and superiority of the proposed TASC compared to baselines.

Deterministic low-latency communication is critical for emerging applications such as industrial automation and autonomous driving. Cell-free (CF) is a promising network architecture for deterministic delay communications, owing to its user-centric cooperative transmission. In this paper, we address the joint optimization problem of resource allocation and beam selection for deterministic delay in the CF network. Specifically, we propose a delay-aware hierarchical distributed deep reinforcement learning (DRL) framework that enables distributed decision-making and improves scalability in the CF network. This framework incorporates a safe reinforcement learning (RL) algorithm to effectively address the delay constraint. Simulation results demonstrate that the proposed scheme improves spectral efficiency and reduces the delay violation ratio, achieving a delay violation ratio of 10−4at a system load of around 90%, an order of magnitude lower than the 10−3achieved by the modified largest weighted delay first (MLWDF) scheme.

Cell-free massive multiple-input multiple-output (CF-mMIMO) networks improve spectral efficiency via coordinated transmission and flexible beam selection. However, the resource allocation in such networks presents a high-dimensional optimization challenge due to the distributed architecture with multiple access points and antennas. To address this, we first formulate a beam selection problem, and then propose an efficient Q-value mixing (QMIX)-based algorithm. Furthermore, recognizing the inherent limitations of deep reinforcement learning (DRL) in practical applications, such as costly training, risky exploration phases, and suboptimal convergence speeds, we design a data-driven digital twin (DT) framework to optimize the DRL training phase. Simulation results show that our approach achieves accelerated convergence and enhanced stability compared to conventional methods. DT-based pre-training establishes a robust performance lower bound prior to real-system deployment.

Radio map, or pathloss map prediction, is a crucial method for wireless network modeling and management. By leveraging deep learning to construct pathloss patterns from geographical maps, an accurate digital replica of the transmission environment could be established with less computational overhead and lower prediction error compared to traditional model-driven techniques. While existing state-of-the-art (SOTA) methods predominantly rely on convolutional architectures, this paper introduces a hybrid transformer-convolution model, termed RM-Transformer, to enhance the accuracy of radio map prediction. The proposed model features a multi-scale transformer-based encoder for efficient feature extraction and a convolution-based decoder for precise pixel-level image reconstruction. Simulation results demonstrate that the proposed scheme significantly improves prediction accuracy, and over a 30% reduction in root mean square error (RMSE) is achieved compared to typical SOTA approaches.

Multi-timeslot multi-user communication scenarios necessitate achieving a balance among system efficiency, user fairness, and reliability under jamming attacks. Millimeter-wave (mmWave) technology can achieve high data rates, but its weak penetration capability and security vulnerabilities make the practical anti-jamming schemes critical to mitigate the attacks. Due to the time-varying nature of wireless channels, statistical quality of service (QoS) provisioning is critical for supporting real-time wireless communication. In this paper, we design a practical anti-jamming strategy for a multi-timeslot multi-user mm Wave system. By jointly designing beamforming and user scheduling, we formulate a cumulative sum-rate maximization problem subject to statistical QoS provisioning. To address the system causality and channel state uncertainty, we introduce residual performance vectors and a penalty function, which transforms the original problem into finite time domain dynamic programming. The problem is subsequently discretized to reduce computational complexity. The proposed algorithm achieves 15% and 22% performance gains over the proportional fair and greedy subcarrier allocation schemes, and approaches the ideal programming algorithm.

In this letter, we study the beamforming design for channel estimation of multi-functional reconfigurable intelligent surface (MF-RIS)-aided multi-user communications that supports simultaneous signal reflection, refraction, and amplification. A least square (LS) based channel estimator is proposed for MF-RIS by considering both the coupled MF-RIS beams and the introduced thermal noise. With the discrete fourier transform (DFT)-matrix, the MF-RIS beamforming design problem is simplified under the proposed LS channel estimator. The optimal MF-RIS beamforming design that achieves the Cramér–Rao lower bound (CRLB) of channel estimator is obtained with the proposed alternating optimization algorithm. Simulation results demonstrate the effectiveness of the proposed beamforming design in reducing the impact of thermal noise.

Distributed over-the-air computation (AirComp) is a promising technology for fast data aggregation in wireless networks by leveraging multiple access channel to achieve communication and computation simultaneously. However, device-to-device (D2D) links applied are vulnerable to obstacles, and the performance of distributed AirComp is restricted by the device with the worst channel condition. To tackle these issues, we introduce a multi-functional reconfigurable intelligent surface (MF-RIS) to reconstruct the wireless propagation environment, where the MF-RIS can achieve signal reflection, refraction, and amplification simultaneously. Specifically, we propose an MF-RIS-aided distributed AirComp framework, where MF-RIS receives the aggregated data from all devices and then transmits it to each device for post-processing. We formulate a mean-squared error (MSE) minimization problem by jointly optimizing transmit scalar, receive scalar, and MF-RIS coefficients. To address this non-convex problem, we employ an alternating optimization (AO) technique to decompose it into three subproblems, where semi-closed form or closed form solutions are obtained. Then, we extend the single-input single-output (SISO) system into multiple-input multiple-output (MIMO) one. Next, we derive the asymptotic MSE performance for SISO and MIMO cases when the number of RIS elements and that of transmit/receive antennas are very large. Numerical results demonstrate the superiority of MF-RIS in improving MSE performance compared to the baseline without RIS. Additionally, the MF-RIS outperforms its passive counterparts, which reveals the advantages of deploying MF-RIS in distributed AirComp systems to reduce data aggregation error.

The robust beamforming design in multi-functional reconfigurable intelligent surface (MF-RIS) assisted wireless networks is investigated in this work, where the MF-RIS supports signal reflection, refraction, and amplification to address the double-fading attenuation and half-space coverage issues faced by traditional RISs. Specifically, we aim to maximize the system energy efficiency by jointly optimizing the transmit beamforming vector and MF-RIS coefficients in the case of imperfect channel state information (CSI). We first leverage the S-procedure and Bernstein-Type Inequality approaches to transform the formulated problem into tractable forms in the bounded and statistical CSI error cases, respectively. Then, we optimize the MF-RIS coefficients and the transmit beamforming vector alternately by adopting an alternating optimization framework, under the quality of service constraint for the bounded CSI error model and the rate outage probability constraint for the statistical CSI error model. Simulation results demonstrate the significant performance improvement of MF-RIS compared to benchmark schemes. In addition, it is revealed that the cumulative CSI error caused by increasing the number of RIS elements is larger than that caused by increasing the number of transmit antennas.

In this paper, we propose a novel multi-functional reconfigurable intelligent surface (MF-RIS) that supports signal reflection, refraction, amplification, and target sensing simultaneously. Our MF-RIS aims to enhance integrated communication and sensing (ISAC) systems, particularly in multi-user and multi-target scenarios. Equipped with reflection and refraction components (i.e., amplifiers and phase shifters), MF-RIS is able to adjust the amplitude and phase shift of both communication and sensing signals on demand. Additionally, with the assistance of sensing elements, MF-RIS is capable of capturing the echo signals from multiple targets, thereby mitigating the signal attenuation typically associated with multi-hop links. We propose a MF-RIS-enabled multi-user and multi-target ISAC system, and formulate an optimization problem to maximize the signal-to-interference-plus-noise ratio (SINR) of sensing targets. This problem involves jointly optimizing the transmit beamforming and MF-RIS configurations, subject to constraints on the communication rate, total power budget, and MF-RIS coefficients. We decompose the formulated non-convex problem into three sub-problems, and then solve them via an efficient iterative algorithm. Simulation results demonstrate that: 1) The performance of MF-RIS varies under different operating protocols, and energy splitting (ES) exhibits the best performance in the considered MF-RIS-enabled multi-user multi-target ISAC system; 2) Under the same total power budget, the proposed MF-RIS with ES protocol attains$\rm {52.2}\%$,$\rm {73.5}\%$, and$\rm {60.86}\%$sensing SINR gains over active RIS, passive RIS, and simultaneously transmitting and reflecting RIS (STAR-RIS), respectively; 3) The number of sensing elements will no longer improve sensing performance after exceeding a certain number.

In this paper, we propose and study a multi-functional reconfigurable intelligent surface (MF-RIS) architecture. In contrast to conventional single-functional RIS (SF-RIS) that only reflects signals, the proposed MF-RIS simultaneously supports multiple functions with one surface, including reflection, refraction, amplification, and energy harvesting of wireless signals. As such, the proposed MF-RIS is capable of significantly enhancing RIS signal coverage by amplifying the signal reflected/refracted by the RIS with the energy harvested. We present the signal model of the proposed MF-RIS, and formulate an optimization problem to maximize the sum-rate of multiple users in an MF-RIS-aided non-orthogonal multiple access network. We jointly optimize the transmit beamforming, power allocations as well as the operating modes and parameters for different elements of the MF-RIS and its deployment location, via an efficient iterative algorithm. Simulation results are provided which show significant performance gains of the MF-RIS over SF-RISs with only some of its functions available. Moreover, we demonstrate that there exists a fundamental trade-off between sum-rate maximization and harvested energy maximization. In contrast to SF-RISs which can be deployed near either the transmitter or receiver, the proposed MF-RIS should be deployed closer to the transmitter for maximizing its communication throughput with more energy harvested.

In this paper, we propose a new multi-functional reconfigurable intelligent surface (MF-RIS) architecture. Different from conventional RIS that only reflects signals, MF-RIS supports multiple functionalities on one surface, including reflection, transmission, amplification, and energy harvesting. As such, MF-RIS is capable of overcoming the double-fading attenuation and achieving full-space coverage by harvesting energy from the base station (BS). The physical implementation and the signal model of MF-RIS are introduced from the perspective of wireless communications. Then, we formulate a sum rate (SR) maximization problem in an MF-RIS-aided non-orthogonal multiple access network. By jointly optimizing the transmit strategy of the BS and the coefficient of the MF-RIS, we design an iterative algorithm to solve the formulated non-convex problem efficiently. Simulation results show that: i) MF-RIS provides up to 98.8% higher SR gain than self-sustainable RIS. ii) There exists a non-trivial trade-off between throughput improvement and self-sustainability, due to the limited number of RIS elements.

In this article, we propose a multi-functional reconfigurable intelligent surface (MF-RIS) to address the half-space coverage and double-fading attenuation issues faced by existing RISs. By simultaneously reflecting, refracting, and amplifying the incident signal, the proposed MF-RIS is capable of realizing full-space coverage with the mitigated signal degradation. The operation principle of the MF-RIS is first provided, and then an efficient beamforming scheme is proposed for MF-RIS-aided wireless communications. Simulation results show that, through combining multiple functions on one surface, the MF-RIS achieves significant throughput improvement over existing RISs.

Although reconfigurable intelligent surfaces (RISs) can improve the performance of wireless networks by smartly reconfiguring the radio environment, existing passive RISs face two key challenges, i.e., double-fading attenuation and dependence on grid/battery. To address these challenges, this paper proposes a new RIS architecture, called multi-functional RIS (MF-RIS). Different from conventional reflecting-only RIS, the proposed MF-RIS is capable of supporting multiple functions with one surface, including signal reflection, amplification, and energy harvesting. As such, our MF-RIS is able to overcome the double-fading attenuation by harvesting energy from incident signals. Through theoretical analysis, we derive the achievable capacity of an MF-RIS-aided communication network. Compared to the capacity achieved by the existing self-sustainable RIS, we derive the number of reflective elements required for MF-RIS to outperform self-sustainable RIS. To realize a self-sustainable communication system, we investigate the use of MF-RIS in improving the sum-rate of multi-user wireless networks. Specifically, we solve a non-convex optimization problem by jointly designing the transmit beamforming and MF-RIS coefficients. As an extension, we investigate a resource allocation problem in a practical scenario with imperfect channel state information. By approximating the semi-infinite constraints with the$\mathcal {S}$-procedure and the general sign-definiteness, we propose a robust beamforming scheme to combat the inevitable channel estimation errors. Finally, numerical results show that: 1) compared to the self-sustainable RIS, MF-RIS can strike a better balance between energy self-sustainability and throughput improvement; and 2) unlike reflecting-only RIS which can be deployed near the transmitter or receiver, MF-RIS should be deployed closer to the transmitter for higher spectrum efficiency.

This paper proposes a novel concept of multi-functional reconfigurable intelligent surfaces (MF-RISs). Different from conventional single-functional RISs (SF-RISs) that only reflect signals, the proposed MF-RIS simultaneously supports multiple functionalities, namely, reflection, amplification, and energy harvesting. Specifically, by harvesting energy from incident signals, MF-RIS is able to simultaneously reflect and amplify signals without an external power supply, which is beneficial for overcoming the double-fading attenuation in a flexible manner. A new operation protocol of MF-RIS is presented, and then a sum rate (SR) maximization problem is formulated for an MF-RIS aided multi-user network. Next, an efficient iterative algorithm is proposed to solve this non-convex problem. Furthermore, through theoretical analysis, we determine the number of reflection elements required for MF-RISs to outperform self-sustainable RISs. Finally, our numerical results show that: 1) MF-RISs are able to provide up to 81.1% higher SR than the self-sustainable RISs. 2) Unlike the SF-RIS, which prefers to be deployed near the transmitter or receiver, MF-RISs should be deployed closer to the transmitter for better performance.

This paper investigates the problem of secure communication in the unmanned aerial vehicle (UAV) enabled net-works aided by a reconfigurable intelligent surface (RIS) from the physical layer security perspective. Specifically, the RIS is deployed to assist the wireless transmission from the UAV to the ground user in the presence of an eavesdropper. The objective of this work is to maximize the secrecy rate by jointly optimizing the phase shifts at the RIS as well as the transmit beamforming vector and location of the UAV. However, the formulated problem is difficult to solve directly due to the non-linear and non-convex objective function and constraints. By invoking the successive convex approximation and fractional programming techniques, the intractable original problem is transformed into convex ones, then an alternating algorithm is proposed to solve the challenging problem effectively. Simulations results demonstrate that the designed algorithm for RIS-aided UAV communications can achieve higher secrecy rate than benchmarks.