Beamforming for Movable and Rotatable Antenna Enabled Multi-User Communications

In the development of wireless communication technology, multiple-input multiple-output (MIMO) technology has emerged as a key enabler, significantly enhancing the capacity of communication systems. However, traditional MIMO systems, which rely on fixed-position antennas (FPAs) with spacing limitations, cannot fully exploit the channel variations in the continuous spatial domain, thus limiting the system's spatial multiplexing performance and diversity. To address these limitations, movable antennas (MAs) have been introduced, offering a breakthrough in signal processing and spatial multiplexing by overcoming the constraints of FPA-based systems. Furthermore, this paper extends the functionality of MAs by introducing movable rotatable antennas (MRAs), which enhance the system's ability to optimize performance in the spatial domain by adding rotational degrees of freedom. By incorporating a dynamic precoding framework based on both antenna position and rotation angle optimization, and employing the zero-forcing (ZF) precoding method, this paper proposes an efficient optimization approach aimed at improving signal quality, mitigating interference, and solving the non-linear, constrained optimization problem using the sequential quadratic programming (SQP) algorithm. This approach effectively enhances the communication system's performance.

Flexible Intelligent Metasurface-Aided Wireless Communications: Architecture and Performance

Typical reconfigurable intelligent surface (RIS) implementations include metasurfaces with almost passive unit elements capable of reflecting their incident waves in controllable ways, enhancing wireless communications in a cost-effective manner. In this paper, we advance the concept of intelligent metasurfaces by introducing a flexible array geometry, termed flexible intelligent metasurface (FIM), which supports both element movement (EM) and passive beamforming (PBF). In particular, based on the single-input single-output (SISO) system setup, we first compare three modes of FIM, namely, EM-only, PBF-only, and EM-PBF, in terms of received signal power under different FIM and channel setups. The PBF-only mode, which only adjusts the reflect phase, is shown to be less effective than the EM-only mode in enhancing received signal strength. In a multi-element, multi-path scenario, the EM-only mode improves the received signal power by 125% compared to the PBF-only mode. The EM-PBF mode, which optimizes both element positions and phases, further enhances performance. Additionally, we investigate the channel estimation problem for FIM systems by designing a protocol that gathers EM and PBF measurements, enabling the formulation of a compressive sensing problem for joint cascaded and direct channel estimation. We then propose a sparse recovery algorithm called clustering mean-field variational sparse Bayesian learning, which enhances estimation performance while maintaining low complexity.

Modeling and Optimization for Flexible Cylindrical Arrays-Enabled Wireless Communications

Flexible-geometry arrays have garnered much attention in wireless communications, which dynamically adjust wireless channels to improve the system performance. In this paper, we propose a novel flexible-geometry array for a $360^\circ$ coverage, named flxible cylindrical array (FCLA), comprised of multiple flexible circular arrays (FCAs). The elements in each FCA can revolve around the circle track to change their horizontal positions, and the FCAs can move along the vertical axis to change the elements' heights. Considering that horizontal revolving can change the antenna orientation, we adopt both the omni-directional and the directional antenna patterns. Based on the regularized zero-forcing (RZF) precoding scheme, we formulate a particular compressive sensing (CS) problem incorporating joint precoding and antenna position optimization, and propose two effective methods, namely FCLA-J and FCLA-A, to solve it. Specifically, the first method involves jointly optimizing the element's revolving angle, height, and precoding coefficient within a single CS framework. The second method decouples the CS problem into two subproblems by utilizing an alternative sparse optimization approach for the revolving angle and height, thereby reducing time complexity. Simulation results reveal that, when utilizing directional radiation patterns, FCLA-J and FCLA-A achieve substantial performance improvements of 43.32\% and 25.42\%, respectively, compared to uniform cylindrical arrays (UCLAs) with RZF precoding.

Flexible Cylindrical Arrays with Movable Antennas for MISO System: Beamforming and Position Optimization

As wireless communication advances toward the 6G era, the demand for ultra-reliable, high-speed, and ubiquitous connectivity is driving the exploration of new degrees-of-freedom (DoFs) in communication systems. Among the key enabling technologies, Movable Antennas (MAs) integrated into Flexible Cylindrical Arrays (FCLA) have shown great potential in optimizing wireless communication by providing spatial flexibility. This paper proposes an innovative optimization framework that leverages the dynamic mobility of FCLAs to improve communication rates and overall system performance. By employing Fractional Programming (FP) for alternating optimization of beamforming and antenna positions, the system enhances throughput and resource utilization. Additionally, a novel Constrained Grid Search-Based Adaptive Moment Estimation Algorithm (CGS-Adam) is introduced to optimize antenna positions while adhering to antenna spacing constraints. Extensive simulations validate that the proposed system, utilizing movable antennas, significantly outperforms traditional fixed antenna optimization, achieving up to a 31\% performance gain in general scenarios. The integration of FCLAs in wireless networks represents a promising solution for future 6G systems, offering improved coverage, energy efficiency, and flexibility.

Power Source Allocation for RIS-aided Integrating Sensing, Communication, and Power Transfer Systems Based on NOMA

This paper proposes a novel communication system framework based on a reconfigurable intelligent surface (RIS)-aided integrated sensing, communication, and power transmission (ISCPT) communication system. RIS is used to improve transmission efficiency and sensing accuracy. In addition, non-orthogonal multiple access (NOMA) technology is incorporated in RIS-aided ISCPT systems to boost the spectrum utilization efficiency of RIS-aided ISCPT systems. We consider the power minimization problem of the RIS-aided ISCPT-NOMA system. Power minimization is achieved by jointly optimizing the RIS phase shift, decoding order, power splitting (PS) factor, and transmit beamforming while satisfying quality of service (QoS), radar target sensing accuracy, and energy harvesting constraints. Since the objective function and constraints in the optimization problem are non-convex, the problem is an NP-hard problem. To solve the non-convex problem, this paper proposes a block coordinate descent (BCD) algorithm. Specifically, the non-convex problem is divided into four sub-problems: i.e. the transmit beamforming, RIS phase shift, decoding order and PS factor optimization subproblems. We employ semidefinite relaxation (SDR) and successive convex approximation (SCA) techniques to address the transmit beamforming optimization sub-problem. Subsequently, we leverage the alternating direction method of multipliers (ADMM) algorithm to solve the RIS phase shift optimization problem. As for the decoding order optimization, we provide a closed-form expression. For the PS factor optimization problem, the SCA algorithm is proposed. Simulation results illustrate the effectiveness of our proposed algorithm and highlight the balanced performance achieved across sensing, communication, and power transfer.

Robust Beamforming Design for Near-Field DMA-NOMA mmWave Communications With Imperfect Position Information

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. Facing with the multi-variable coupled non-convex problems, 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.

Movable Antenna Enabled ISAC Beamforming Design for Low-Altitude Airborne Vehicles

In mobile systems with low-altitude vehicles, integrated sensing and communication (ISAC) is considered an effective approach to increase the transmission rate due to limited spectrum resources. To further improve the ISAC performance, this paper proposes a novel method called integrated sensing and communication-movable antenna (ISAC-MA) to optimize the antenna's position. Our goal is to support low-space vehicles by optimizing radar and communication joint beamforming and antenna position in the presence of clutter. This scheme not only guarantees the required signal-to-noise ratio (SNR) for sensing but also further improves the SNR for communication. A successive convex approximation (SCA)-based block coordinate descent (BCD) algorithm is proposed to maximize communication capacity under the condition of sensing SNR. Numerical results show that, compared with the traditional ISAC system and various benchmark schemes, the proposed ISAC-MA system can achieve higher communication capacity under the same sensing SNR constraints.

Delay Minimization for Movable Antennas-Enabled Anti-Jamming Communications With Mobile Edge Computing

In future 6G networks, anti-jamming will become a critical challenge, particularly with the development of intelligent jammers that can initiate malicious interference, posing a significant security threat to communication transmission. Additionally, 6G networks have introduced mobile edge computing (MEC) technology to reduce system delay for edge user equipment (UEs). Thus, one of the key challenges in wireless communications is minimizing the system delay while mitigating interference and improving the communication rate. However, the current fixed-position antenna (FPA) techniques have limited degrees of freedom (DoF) and high power consumption, making them inadequate for communication in highly interfering environments. To address these challenges, this paper proposes a novel MEC anti-jamming communication architecture supported by mobile antenna (MA) technology. The core of the MA technique lies in optimizing the position of the antennas to increase DoF. The increase in DoF enhances the system's anti-jamming capabilities and reduces system delay. In this study, our goal is to reduce system delay while ensuring communication security and computational requirements. We design the position of MAs for UEs and the base station (BS), optimize the transmit beamforming at the UEs and the receive beamforming at the BS, and adjust the offloading rates and resource allocation for computation tasks at the MEC server. Since the optimization problem is a non-convex multi-variable coupled problem, we propose an algorithm based on penalty dual decomposition (PDD) combined with successive convex approximation (SCA). The simulation results demonstrate that the proposed MA architecture and the corresponding schemes offer superior anti-jamming capabilities and reduce the system delay compared to FPA.

Movable Antenna-Enhanced Wireless Communications: General Architectures and Implementation Methods

Movable antennas (MAs), traditionally explored in antenna design, have recently garnered significant attention in wireless communications due to their ability to dynamically adjust the antenna positions to changes in the propagation environment. However, previous research has primarily focused on characterizing the performance limits of various MA-assisted wireless communication systems, with less emphasis on their practical implementation. To address this gap, in this article, we propose several general MA architectures that extend existing designs by varying several key aspects to cater to different application scenarios and tradeoffs between cost and performance. Additionally, we draw from fields such as antenna design and mechanical control to provide an overview of candidate implementation methods for the proposed MA architectures, utilizing either direct mechanical or equivalent electronic control. Simulation results are finally presented to support our discussion.

Extremely Large-Scale Dynamic Metasurface Antennas (XL-DMAs): Near-Field Modeling and Channel Estimation

Dynamic metasurface antennas (DMAs) represent a novel transceiver array architecture for extremely large-scale (XL) communications, offering the advantages of reduced power consumption and lower hardware costs compared to conventional arrays. This paper focuses on near-field channel estimation for XL-DMAs. We begin by analyzing the near-field characteristics of uniform planar arrays (UPAs) and introducing the Oblong Approx. model. This model decouples elevation-azimuth (EL-AZ) parameters for XL-DMAs, providing an effective means to characterize the near-field effect. It offers simpler mathematical expressions than the second-order Taylor expansion model, all while maintaining negligible model errors for oblong-shaped arrays. Building on the Oblong Approx. model, we propose an EL-AZ-decoupled estimation framework that involves near- and far-field parameter estimation for AZ/EL and EL/AZ directions, respectively. The former is formulated as a distributed compressive sensing problem, addressed using the proposed off-grid distributed orthogonal least squares algorithm, while the latter involves a straightforward parallelizable search. Crucially, we illustrate the viability of decoupled EL-AZ estimation for near-field UPAs, exhibiting commendable performance and linear complexity correlated with the number of metasurface elements. Moreover, we design an measurement matrix optimization method with the Lorentzian constraint on DMAs and highlight the estimation performance degradation resulting from this constraint.