Movable Intelligent Surface (MIS) for Wireless Communications: Architecture, Modeling, Algorithm, and Prototyping

Reconfigurable intelligent surfaces enhance wireless systems by reshaping propagation environments. However, dynamic metasurfaces (MSs) with numerous phase-shift elements incur undesired control and hardware costs. In contrast, static MSs (SMSs), configured with static phase shifts pre-designed for specific communication demands, offer a cost-effective alternative by eliminating element-wise tuning. Nevertheless, SMSs typically support a single beam pattern with limited flexibility. In this paper, we propose a novel Movable Intelligent Surface (MIS) technology that enables dynamic beamforming while maintaining static phase shifts. Specifically, we design a MIS architecture comprising two closely stacked transmissive MSs: a larger fixed-position MS 1 and a smaller movable MS 2. By differentially shifting MS 2's position relative to MS 1, the MIS synthesizes distinct beam patterns. Then, we model the interaction between MS 2 and MS 1 using binary selection matrices and padding vectors and formulate a new optimization problem that jointly designs the MIS phase shifts and selects shifting positions for worst-case signal-to-noise ratio maximization. This position selection, equal to beam pattern scheduling, offers a new degree of freedom for RIS-aided systems. To solve the intractable problem, we develop an efficient algorithm that handles unit-modulus and binary constraints and employs manifold optimization methods. Finally, extensive validation results are provided. We implement a MIS prototype and perform proof-of-concept experiments, demonstrating the MIS's ability to synthesize desired beam patterns that achieve one-dimensional beam steering. Numerical results show that by introducing MS 2 with a few elements, MIS effectively offers beamforming flexibility for significantly improved performance. We also draw insights into the optimal MIS configuration and element allocation strategy.

Efficient Joint Precoding Design for Wideband Intelligent Reflecting Surface-Assisted Cell-Free Network

In this paper, we propose an efficient joint precoding design method to maximize the weighted sum-rate in wideband intelligent reflecting surface (IRS)-assisted cell-free networks by jointly optimizing the active beamforming of base stations and the passive beamforming of IRS. Due to employing wideband transmissions, the frequency selectivity of IRSs has to been taken into account, whose response usually follows a Lorentzian-like profile. To address the high-dimensional non-convex optimization problem, we employ a fractional programming approach to decouple the non-convex problem into subproblems for alternating optimization between active and passive beamforming. The active beamforming subproblem is addressed using the consensus alternating direction method of multipliers (CADMM) algorithm, while the passive beamforming subproblem is tackled using the accelerated projection gradient (APG) method and Flecher-Reeves conjugate gradient method (FRCG). Simulation results demonstrate that our proposed approach achieves significant improvements in weighted sum-rate under various performance metrics compared to primal-dual subgradient (PDS) with ideal reflection matrix. This study provides valuable insights for computational complexity reduction and network capacity enhancement.

Space-Time-Modulated Wideband Radiation-Type Programmable Metasurface for Low Sidelobe Beamforming

Programmable metasurfaces promise a great potential to construct low-cost phased array systems due to the capability of elaborate modulation over electromagnetic (EM) waves. However, they are in either reflective or transmissive mode, and usually possess a relatively high profile as a result of the external feed source. Besides, it is difficult to conduct multibit phase shift in metasurfaces, when comparing with conventional phased arrays. Here, we propose a strategy of space-time modulated wideband radiation-type programmable metasurface for low side-lobe beamforming. The wideband programmable metasurface avoids the space-feed external source required by its traditional counterpart, thus achieving a significant reduction of profile through integration of a highefficiency microwave-fed excitation network and metasurface. Furthermore, through introducing space-time-modulated strategy, the high-accuracy amplitude-phase weight algorithm can also be synchronously carried out on the first harmonic component for low side-lobe beam-scanning. Most importantly, adaptive beamforming and generation of interference null can further be created after analyzing the harmonic component characteristics of received signals.

Throughput Maximization for Movable Antenna Systems with Movement Delay Consideration

In this paper, we model the minimum achievable throughput within a transmission block of restricted duration and aim to maximize it in movable antenna (MA)-enabled multiuser downlink communications. Particularly, we account for the antenna moving delay caused by mechanical movement, which has not been fully considered in previous studies, and reveal the trade-off between the delay and signal-to-interference-plus-noise ratio at users. To this end, we first consider a single-user setup to analyze the necessity of antenna movement. By quantizing the virtual angles of arrival, we derive the requisite region size for antenna moving, design the initial MA position, and elucidate the relationship between quantization resolution and moving region size. Furthermore, an efficient algorithm is developed to optimize MA position via successive convex approximation, which is subsequently extended to the general multiuser setup. Numerical results demonstrate that the proposed algorithms outperform fixed-position antenna schemes and existing ones without consideration of movement delay. Additionally, our algorithms exhibit excellent adaptability and stability across various transmission block durations and moving region sizes, and are robust to different antenna moving speeds. This allows the hardware cost of MA-aided systems to be reduced by employing low rotational speed motors.

DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV

To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Introducing the new metric Age of Information (AoI) provides a more comprehensive evaluation of communication system. Additionally, to ensure service quality, user terminals need to possess high computational capabilities, which may lead to increased energy consumption, necessitating a trade-off between communication energy consumption and effectiveness. Given the complexity and dynamics of communication systems, Deep Reinforcement Learning (DRL) serves as an intelligent learning method capable of learning optimal strategies in dynamic environments. Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL. Finally, through comparative simulations with baseline methods, the proposed approach demonstrates its advances in terms of energy consumption and AoI.

IREE Oriented Active RIS-Assisted Green communication System with Outdated CSI

The rapid evolution of communication technologies has spurred a growing demand for energy-efficient network architectures and performance metrics. Active Reconfigurable Intelligent Surfaces (RIS) are emerging as a key component in green network architectures. Compared to passive RIS, active RIS are equipped with amplifiers on each reflecting element, allowing them to simultaneously reflect and amplify signals, thereby overcoming the double multiplicative fading in the phase response, and improving both system coverage and performance. Additionally, the Integrated Relative Energy Efficiency (IREE) metric, as introduced in [1], addresses the dynamic variations in traffic and capacity over time and space, enabling more energy-efficient wireless systems. Building on these advancements, this paper investigates the problem of maximizing IREE in active RIS-assisted green communication systems. However, acquiring perfect Channel State Information (CSI) in practical systems poses significant challenges and costs. To address this, we derive the average achievable rate based on outdated CSI and formulated the corresponding IREE maximization problem, which is solved by jointly optimizing beamforming at both the base station and RIS. Given the non-convex nature of the problem, we propose an Alternating Optimization Successive Approximation (AOSO) algorithm. By applying quadratic transform and relaxation techniques, we simplify the original problem and alternately optimize the beamforming matrices at the base station and RIS. Furthermore, to handle the discrete constraints of the RIS reflection coefficients, we develop a successive approximation method. Experimental results validate our theoretical analysis of the algorithm's convergence , demonstrating the effectiveness of the proposed algorithm and highlighting the superiority of IREE in enhancing the performance of green communication networks.

Movable Antenna Enhanced Networked Full-Duplex Integrated Sensing and Communication System

Integrated sensing and communication (ISAC) is envisioned as a key technology for future sixth-generation (6G) networks. Classical ISAC system considering monostatic and/or bistatic settings will inevitably degrade both communication and sensing performance due to the limited service coverage and easily blocked transmission paths. Besides, existing ISAC studies usually focus on downlink (DL) or uplink (UL) communication demands and unable to achieve the systematic DL and UL communication tasks. These challenges can be overcome by networked FD ISAC framework. Moreover, ISAC generally considers the trade-off between communication and sensing, unavoidably leading to a loss in communication performance. This shortcoming can be solved by the emerging movable antenna (MA) technology. In this paper, we utilize the MA to promote communication capability with guaranteed sensing performance via jointly designing beamforming, power allocation, receiving filters and MA configuration towards maximizing sum rate. The optimization problem is highly difficult due to the unique channel model deriving from the MA. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) method, we develop an efficient solution that optimizes all variables via convex optimization techniques. Extensive simulation results verify the effectiveness of our proposed algorithms and demonstrate the substantial performance promotion by deploying MA in the networked FD ISAC system.

Model Predictive Control Enabled UAV Trajectory Optimization and Secure Resource Allocation

In this paper, we investigate a secure communication architecture based on unmanned aerial vehicle (UAV), which enhances the security performance of the communication system through UAV trajectory optimization. We formulate a control problem of minimizing the UAV flight path and power consumption while maximizing secure communication rate over infinite horizon by jointly optimizing UAV trajectory, transmit beamforming vector, and artificial noise (AN) vector. Given the non-uniqueness of optimization objective and significant coupling of the optimization variables, the problem is a non-convex optimization problem which is difficult to solve directly. To address this complex issue, an alternating-iteration technique is employed to decouple the optimization variables. Specifically, the problem is divided into three subproblems, i.e., UAV trajectory, transmit beamforming vector, and AN vector, which are solved alternately. Additionally, considering the susceptibility of UAV trajectory to disturbances, the model predictive control (MPC) approach is applied to obtain UAV trajectory and enhance the system robustness. Numerical results demonstrate the superiority of the proposed optimization algorithm in maintaining accurate UAV trajectory and high secure communication rate compared with other benchmark schemes.

Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning

This paper presents a semantic-aware multi-modal resource allocation (SAMRA) for multi-task using multi-agent reinforcement learning (MARL), termed SAMRAMARL, utilizing in platoon systems where cellular vehicle-to-everything (C-V2X) communication is employed. The proposed approach leverages the semantic information to optimize the allocation of communication resources. By integrating a distributed multi-agent reinforcement learning (MARL) algorithm, SAMRAMARL enables autonomous decision-making for each vehicle, channel assignment optimization, power allocation, and semantic symbol length based on the contextual importance of the transmitted information. This semantic-awareness ensures that both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications prioritize data that is critical for maintaining safe and efficient platoon operations. The framework also introduces a tailored quality of experience (QoE) metric for semantic communication, aiming to maximize QoE in V2V links while improving the success rate of semantic information transmission (SRS). Extensive simulations has demonstrated that SAMRAMARL outperforms existing methods, achieving significant gains in QoE and communication efficiency in C-V2X platooning scenarios.

Joint Discrete Antenna Positioning and Beamforming Optimization in Movable Antenna Enabled Full-Duplex ISAC Networks

In this paper, we propose a full-duplex integrated sensing and communication (ISAC) system enabled by a movable antenna (MA). By leveraging the characteristic of MA that can increase the spatial diversity gain, the performance of the system can be enhanced. We formulate a problem of minimizing the total transmit power consumption via jointly optimizing the discrete position of MA elements, beamforming vectors, sensing signal covariance matrix and user transmit power. Given the significant coupling of optimization variables, the formulated problem presents a non-convex optimization challenge that poses difficulties for direct resolution. To address this challenging issue, the discrete binary particle swarm optimization (BPSO) algorithm framework is employed to solve the formulated problem. Specifically, the discrete positions of MA elements are first obtained by iteratively solving the fitness function. The difference-of-convex (DC) programming and successive convex approximation (SCA) are used to handle non-convex and rank-1 terms in the fitness function. Once the BPSO iteration is complete, the discrete positions of MA elements can be determined, and we can obtain the solutions for beamforming vectors, sensing signal covariance matrix and user transmit power. Numerical results demonstrate the superiority of the proposed system in reducing the total transmit power consumption compared with fixed antenna arrays.