Movable Antenna Enhanced Multi-Region Beam Coverage: A Multi-Notch-Filter-Inspired Design

Movable antenna (MA) has emerged as a promising technology to enhance wireless communication performance by exploiting the new degree of freedom (DoF) via antenna position optimization. In this letter, we investigate the MA-enhanced wide beam coverage over multiple subregions in the spatial domain. Specifically, we aim to maximize the minimum beam gain over the desired subregions by jointly optimizing the transmit beamforming and antenna position vector (APV). Although this problem is non-convex, we propose an efficient algorithm to solve it by leveraging the similarity between the considered multi-region coverage and classical multi-notch filter (MNF) design. In particular, we construct a spatial MNF-based transmit beamforming vector by assuming a continuous amplitude and phase-shift profile within the antenna movement region. Based on this continuous profile, we propose a sequential update algorithm to select an optimal subset of MA positions for multi-region coverage, jointly with a Gibbs sampling (GS) procedure to avoid undesired local optimum. Numerical results show that our proposed algorithm can significantly outperform conventional fixed position antennas (FPAs) and achieve a comparable performance to the alternating optimization (AO) algorithm with dramatically lower complexity.

Rotatable Antenna Array-Enhanced Null Steering: Performance Analysis and Optimization

Conventional fixed-orientation antenna (FOA) arrays offer limited degrees of freedom (DoF) for flexible beamforming such as null steering. To address this limitation, we propose a new rotatable antenna array (RAA) architecture in this paper, which enables three-dimensional (3D) rotational control of an antenna array to provide enhanced spatial flexibility for null steering. To characterize its performance, we aim to jointly optimize the 3D rotational angles of the RAA, to maximize the beam gain over a given desired direction, while nulling those over multiple interference directions under zero-forcing (ZF) beamforming. However, this problem is non-convex and challenging to tackle due to the highly nonlinear expression of the beam gain in terms of the rotational angles. To gain insights, we first examine several special cases including both isotropic and directional antenna radiation patterns, deriving the conditions under which full beam gain can be achieved over the desired direction while meeting the nulling constraints for interference directions. These conditions clearly indicate that compared with FOA arrays, RAAs can significantly relax the angular separation requirement for achieving effective null steering. For other general cases, we propose a sequential update algorithm, that iteratively refines the 3D rotational angles by discretizing the 3D angular search space. To avoid undesired local optimum, a Gibbs sampling (GS) procedure is also employed between two consecutive rounds of sequential update for solution exploration. Simulation results verify our analytical results and show superior null-steering performance of RAAs to FOA arrays.

Position Optimization for Two-layer Movable Antenna Systems

Movable antenna (MA) is a promising technology for improving the performance of wireless communication systems by providing new degrees-of-freedom (DoFs) in antenna position optimization. However, existing works on MA systems have mostly considered element-wise single-layer MA (SL-MA) arrays, where all the MAs move within the given movable region, hence inevitably incurring high control complexity and hardware cost in practice. To address this issue, we propose in this letter a new two-layer MA array (TL-MA), where the positions of MAs are jointly determined by the large-scale movement of multiple subarrays and the small-scale fine-tuning of per-subarray MAs. In particular, an optimization problem is formulated to maximize the sum-rate of the TL-MA-aided communication system by jointly optimizing the subarray-positions, per-subarray (relative) MA positions, and receive beamforming. To solve this non-convex problem, we propose an alternating optimization (AO)-based particle swarm optimization (PSO) algorithm, which alternately optimizes the positions of subarrays and per-subarray MAs, given the optimal receive beamforming. Numerical results verify that the proposed TL-MA significantly reduces the sum-displacement of MA motors (i.e., the total moving distances of all motors) of element-wise SL-MA, while achieving comparable rate performance.

Performance Optimization for Movable Antenna Enhanced MISO-OFDM Systems

Movable antenna (MA) technology offers a flexible approach to enhancing wireless channel conditions by adjusting antenna positions within a designated region. While most existing works focus on narrowband MA systems, this paper investigates MA position optimization for an MA-enhanced multiple-input single-output (MISO) orthogonal frequency-division multiplexing (OFDM) system. This problem appears to be particularly challenging due to the frequency-flat nature of MA positioning, which should accommodate the channel conditions across different subcarriers. To overcome this challenge, we discretize the movement region into a multitude of sampling points, thereby converting the continuous position optimization problem into a discrete point selection problem. Although this problem is combinatorial, we develop an efficient partial enumeration algorithm to find the optimal solution using a branch-and-bound framework, where a graph-theoretic method is incorporated to effectively prune suboptimal solutions. In the low signal-to-noise ratio (SNR) regime, a simplified graph-based algorithm is also proposed to obtain the optimal MA positions without the need for enumeration. Simulation results reveal that the proposed algorithm outperforms conventional fixed-position antennas (FPAs), while narrowband-based antenna position optimization can achieve near-optimal performance.

Frequency-switching Array Enhanced Physical-Layer Security in Terahertz Bands: A Movable Antenna Perspective

In this paper, we propose a new frequency-switching array (FSA) enhanced physical-layer security (PLS) system in terahertz bands, where the carrier frequency can be flexibly switched and small frequency offsets can be imposed on each antenna at Alice, so as to eliminate information wiretapping by undesired eavesdroppers. First, we analytically show that by flexibly controlling the carrier frequency parameters, FSAs can effectively form uniform/non-uniform sparse arrays, hence resembling movable antennas (MAs) in the control of inter-antenna spacing and providing additional degree-of-freedom (DoF) in the beam control. Although the proposed FSA experiences additional path-gain attenuation in the received signals, it can overcome several hardware and signal processing issues incurred by MAs, such as limited positioning accuracy, considerable response latency, and demanding hardware and energy cost. To shed useful insights, we first consider a secrecy-guaranteed problem with a null-steering constraint for which maximum ratio transmission (MRT) beamformer is considered at Alice and the frequency offsets are set as uniform frequency increment. Interestingly, it is shown that the proposed FSA can flexibly realize null-steering over Eve in both the angular domain (by tuning carrier frequency) and range domain (by controlling per-antenna frequency offset), thereby achieving improved PLS performance. Then, for the general case, we propose an efficient algorithm to solve the formulated non-convex problem by using the block coordinate descent (BCD) and projected gradient ascent (PGA) techniques. Finally, numerical results demonstrate the convergence of the proposed optimization algorithm and its superiority over fixed-position arrays (FPAs) in terms of secrecy-rate performance.

Integrating Movable Antennas and Intelligent Reflecting Surfaces (MA-IRS): Fundamentals, Practical Solutions, and Opportunities

Movable antennas (MAs) and intelligent reflecting surfaces (IRSs) enable active antenna repositioning and passive phase-shift tuning for channel reconfiguration, respectively. Integrating MAs and IRSs boosts spatial degrees of freedom, significantly enhancing wireless network capacity, coverage, and reliability. In this article, we first present the fundamentals of MA-IRS integration, involving clarifying the key design issues, revealing performance gain, and identifying the conditions where MA-IRS synergy persists. Then, we examine practical challenges and propose pragmatic design solutions, including optimization schemes, hardware architectures, deployment strategies, and robust designs for hardware impairments and mobility management. In addition, we highlight how MA-IRS architectures uniquely support advanced integrated sensing and communication, enhancing sensing performance and dual-functional flexibility. Overall, MA-IRS integration emerges as a compelling approach toward next-generation reconfigurable wireless systems.

CNN-Based Channel Map Estimation for Movable Antenna Systems

Movable antenna (MA) has attracted increasing attention in wireless communications due to its capability of wireless channel reconfiguration through local antenna movement within a confined region at the transmitter/receiver. However, to determine the optimal antenna positions, channel state information (CSI) within the entire region, termed small-scale channel map, is required, which poses a significant challenge due to the unaffordable overhead for exhaustive channel estimation at all positions. To tackle this challenge, in this paper, we propose a new convolutional neural network (CNN)-based estimation scheme to reconstruct the small-scale channel map within a three-dimensional (3D) movement region. Specifically, we first collect a set of CSI measurements corresponding to a subset of MA positions and different receiver locations offline to comprehensively capture the environmental features. Subsequently, we train a CNN using the collected data, which is then used to reconstruct the full channel map during real-time transmission only based on a finite number of channel measurements taken at several selected MA positions within the 3D movement region. Numerical results demonstrate that our proposed scheme can accurately reconstruct the small-scale channel map and outperforms other benchmark schemes.

A Derivative-Free Position Optimization Approach for Movable Antenna Multi-User Communication Systems

Movable antennas (MAs) have emerged as a disruptive technology in wireless communications for enhancing spatial degrees of freedom through continuous antenna repositioning within predefined regions, thereby creating favorable channel propagation conditions. In this paper, we study the problem of position optimization for MA-enabled multi-user MISO systems, where a base station (BS), equipped with multiple MAs, communicates with multiple users each equipped with a single fixed-position antenna (FPA). To circumvent the difficulty of acquiring the channel state information (CSI) from the transmitter to the receiver over the entire movable region, we propose a derivative-free approach for MA position optimization. The basic idea is to treat position optimization as a closed-box optimization problem and calculate the gradient of the unknown objective function using zeroth-order (ZO) gradient approximation techniques. Specifically, the proposed method does not need to explicitly estimate the global CSI. Instead, it adaptively refines its next movement based on previous measurements such that it eventually converges to an optimum or stationary solution. Simulation results show that the proposed derivative-free approach is able to achieve higher sample and computational efficiencies than the CSI estimation-based position optimization approach, particularly for challenging scenarios where the number of multi-path components (MPCs) is large or the number of pilot signals is limited.

UAV-Enabled Passive 6DMA for ISAC: Joint Location, Orientation, and Reflection Optimization

Improving the fundamental performance trade-off in integrated sensing and communication (ISAC) systems has been deemed as one of the most significant challenges. To address it, we propose in this letter a novel ISAC system that leverages an unmanned aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) and the UAV's maneuverability in six-dimensional (6D) space, i.e., three-dimensional (3D) location and 3D rotation, thus referred to as passive 6D movable antenna (6DMA). We aim to maximize the signal-to-noise ratio (SNR) for sensing a single target while ensuring a minimum SNR at a communication user equipment (UE), by jointly optimizing the transmit beamforming at the ISAC base station (BS), the 3D location and orientation as well as the reflection coefficients of the IRS. To solve this challenging non-convex optimization problem, we propose a two-stage approach. In the first stage, we aim to optimize the IRS's 3D location, 3D orientation, and reflection coefficients to enhance both the channel correlations and power gains for sensing and communication. Given their optimized parameters, the optimal transmit beamforming at the ISAC BS is derived in closed form. Simulation results demonstrate that the proposed passive 6DMA-enabled ISAC system significantly improves the sensing and communication trade-off by simultaneously enhancing channel correlations and power gains, and outperforms other baseline schemes.

Robust Movable-Antenna Position Optimization with Imperfect CSI for MISO Systems

Movable antenna (MA) technology has emerged as a promising solution for reconfiguring wireless channel conditions through local antenna movement within confined regions. Unlike previous works assuming perfect channel state information (CSI), this letter addresses the robust MA position optimization problem under imperfect CSI conditions for a multiple-input single-output (MISO) MA system. Specifically, we consider two types of CSI errors: norm-bounded and randomly distributed errors, aiming to maximize the worst-case and non-outage received signal power, respectively. For norm-bounded CSI errors, we derive the worst-case received signal power in closed-form. For randomly distributed CSI errors, due to the intractability of the probabilistic constraints, we apply the Bernstein-type inequality to obtain a closed-form lower bound for the non-outage received signal power. Based on these results, we show the optimality of the maximum-ratio transmission for imperfect CSI in both scenarios and employ a graph-based algorithm to obtain the optimal MA positions. Numerical results show that our proposed scheme can even outperform other benchmark schemes implemented under perfect CSI conditions.