optimize antenna orientation

Dynamic adaptation to channel spatial characteristics remains a critical challenge in 6G wireless networks. Method: This paper proposes Six-Dimensional Movable Antenna (6DMA) technology, enabling flexible spatial reconfiguration by jointly controlling antenna position in three dimensions and orientation about three orthogonal axes. We establish, for the first time, a rigorous 6DMA spatial channel model integrated with practical hardware constraints; systematically analyze and optimize two simplified architectures—rotation-only and displacement-only; introduce a communication-and-sensing co-design paradigm; and develop and experimentally validate multiple prototype systems. Results: Experimental results demonstrate that 6DMA significantly enhances channel gain and localization accuracy. It offers a novel, cost-effective pathway toward spectrally efficient and highly adaptable 6G air interfaces, bridging theoretical flexibility with implementable hardware design.

This work addresses integrated sensing and communication (ISAC) systems with rotatable antennas, aiming to maximize the minimum echo signal power over an extended sensing region while satisfying multiuser communication rate requirements. To this end, the paper establishes a joint optimization framework for antenna pointing direction, communication beamforming, and sensing signal covariance matrix. A closed-form optimal solution is derived for the single-user point-target scenario, and an efficient alternating optimization algorithm is developed for the multiuser extended-target case. Simulation results demonstrate that the proposed approach significantly outperforms baseline schemes employing fixed antenna orientations or optimizing only array-level rotation, thereby achieving substantial improvements in both sensing and communication performance.

To address the challenge of jointly enhancing communication rate and target angle estimation accuracy in Integrated Sensing and Communication (ISAC) systems, this paper introduces, for the first time, physical rotation of the base station antenna array as a controllable degree of freedom. We propose a multi-objective optimization framework jointly designing beamforming and array rotation angle. By modeling the equivalent aperture extension effect and rotation-induced gain arising from antenna rotation, we formulate an optimization problem that minimizes the Cramér–Rao Bound (CRB) for angle estimation while maximizing the multi-user sum rate. The problem is solved via a block coordinate descent algorithm combined with one-dimensional search. Experimental results demonstrate that, compared to fixed-antenna baselines, the proposed method significantly improves both sum rate and angle estimation accuracy in joint sensing-communication scenarios. Furthermore, ablation studies in pure communication and pure sensing sub-scenarios separately validate the effectiveness of rotation gain and equivalent aperture extension.

This work proposes a cross-linked (CL) rotatable antenna array architecture to address the scalability limitations of conventional rotatable antenna systems, whose hardware cost and control complexity grow linearly with the number of antennas. By jointly coordinating the three-dimensional orientations of multiple antennas, the CL architecture enhances spatial degrees of freedom while reducing hardware overhead. Two novel rotation schemes—element-level and panel-level—are introduced, and a joint optimization framework is developed to co-design base station receive beamforming and discrete rotation angles, solved via alternating optimization, MMSE beamforming, and a genetic algorithm. Simulations demonstrate that, under appropriate row–column grouping, the CL architecture achieves performance close to that of a fully flexible system; notably, the element-level scheme outperforms the panel-level counterpart by 25% and surpasses traditional fixed-antenna arrays by 128%.

This work addresses the performance degradation of base station antennas caused by physical blockages and the angular mismatch arising when intelligent reflecting surfaces (IRSs) are deployed in antenna sidelobe regions. To overcome these challenges, the paper proposes a novel co-design framework that integrates a rotatable base station antenna with an IRS, jointly optimizing the three-dimensional antenna orientation, receive beamforming, and IRS phase shifts to maximize the sum rate of a multiuser uplink system. An efficient alternating optimization algorithm is developed, combining projected gradient ascent, closed-form beamforming solutions, and fractional programming techniques to tackle the resulting non-convex problem. Simulation results demonstrate that, under large angular mismatch conditions, the proposed scheme significantly outperforms fixed-antenna systems in terms of sum rate, thereby enhancing wireless coverage and communication quality.

This paper addresses the minimum SINR maximization problem in multi-user, multipath wireless communication systems. To exploit orientation as a novel spatial degree of freedom, we propose a joint optimization framework based on reconfigurable rotatable antennas (RAs). First, we develop a lightweight three-dimensional geometric channel model for RAs, avoiding computationally intensive modeling of high-dimensional movable antennas. Second, we derive a closed-form solution for the optimal antenna orientation in single-user scenarios. Third, we design an alternating optimization algorithm tailored to multi-user, multipath environments, jointly optimizing receive beamforming (maximum-ratio combining, MRC) and the 3D antenna pointing angles to maximize the minimum SINR across users. Simulation results demonstrate that the proposed scheme significantly outperforms benchmarks—including fixed antennas, randomly oriented antennas, and location-based fluid antenna systems—in terms of minimum SINR, thereby validating both the effectiveness and practicality of orientation as an additional spatial resource.

This work addresses the limited adaptability of conventional MIMO systems, whose fixed antenna arrays hinder dynamic optimization of channel capacity in varying propagation environments. For the first time, the physical orientation of antennas is introduced as a new degree of freedom, enabling the development of a channel model with rotatable antennas. The authors jointly optimize the transmit covariance matrix and the antenna pointing directions at both transmitter and receiver under a spherical cap constraint to maximize channel capacity. To tackle this non-convex problem, an alternating optimization algorithm is proposed, integrating eigenmode transmission, water-filling, and Riemannian Frank–Wolfe methods. A simplified design is also derived for the low-SNR regime. Numerical results demonstrate that the proposed approach significantly outperforms fixed-orientation baselines, confirming the efficacy and potential of orientation-aware channel reconfiguration.

This work addresses the beam misalignment and array gain degradation caused by the high-speed motion of low Earth orbit (LEO) satellites with fixed antenna beams. To mitigate these issues, the paper proposes a rotatable antenna architecture that deploys directionally adjustable beamforming arrays at both the satellite and ground terminals. Leveraging the rank-one property of line-of-sight (LoS) channels, the mainlobe orientation is treated as an additional spatial degree of freedom and jointly optimized with beamforming weights. An efficient, low-complexity alignment strategy—decoupling beam steering from weight optimization—and orbit-prediction-driven channel estimation enable low-overhead beam tracking. Simulation results demonstrate that the proposed scheme significantly outperforms baseline approaches with fixed or randomly oriented mainlobes in terms of achievable rate and robustness against angular perturbations.

This study addresses the challenge that fixed antennas in Space-Air-Ground Integrated Networks (SAGIN) struggle to accommodate heterogeneous mobility and dynamic beam alignment. To this end, we propose a novel network architecture empowered by rotatable antennas (RAs). By leveraging mechanical or electronic beam steering to complement platform mobility, and integrating channel prediction tracking with joint directional control and resource management algorithms, the proposed framework achieves cross-segment cooperative sensing and wide-area dynamic directional transmission. Prototype validation and simulation results demonstrate that this approach effectively enhances SAGIN coverage capability and overall system performance, confirming both the feasibility of RA technology and its substantial gains in complex, highly dynamic scenarios.

This study addresses the unclear energy efficiency comparison between non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) in reconfigurable antenna-assisted multiuser systems. It presents the first systematic evaluation of their performance in minimizing transmit power under user rate requirements and antenna rotation constraints. The authors formulate a non-convex optimization problem incorporating directional antenna gain and jointly optimize antenna orientation and power allocation, solving it via particle swarm optimization (PSO). Theoretical analysis and simulations demonstrate that reconfigurable antennas significantly reduce transmit power. Furthermore, NOMA outperforms OMA under asymmetric user distributions, yet may underperform time-division multiple access (TDMA)—a representative OMA scheme—when users are symmetrically located, thereby revealing NOMA’s performance boundaries and strong dependence on deployment geometry.

This work addresses the challenge of enhancing wireless communication performance while simultaneously reducing the number of radio frequency (RF) chains and active antennas. To this end, the authors propose a mechanical beamforming architecture based on a three-dimensional passively coupled rotatable coupler. The system operates without additional RF chains; leveraging multiport circuit theory, they formulate a channel model and cast the coupler rotation optimization as a constrained non-convex problem. An efficient solution is achieved by integrating a spherical cap conditional gradient algorithm with the cross-entropy method. Simulation results demonstrate that the proposed approach significantly outperforms existing benchmarks in terms of received signal-to-noise ratio and overall communication performance, all while substantially lowering hardware complexity.