optimize antenna positions

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.

To address coverage imbalance, capacity limitations, and load heterogeneity in 3D heterogeneous cellular networks serving both ground users (GUEs) and unmanned aerial vehicles (UAVs), this paper pioneers the integration of quantization theory into base station (BS) deployment optimization, establishing a deterministic node modeling-based joint optimization framework. Methodologically, it unifies 3D channel modeling, nonlinear optimization, and a co-design algorithm jointly optimizing BS locations, antenna orientations, and radiation parameters. Departing from conventional single-dimensional optimization paradigms, our approach achieves the first holistic performance trade-off between GUEs and UAVs: UAV average capacity increases by 42%, while GUE performance degradation remains below 3%. This outperforms antenna-only tuning schemes significantly. The work provides a theoretically grounded and empirically verifiable methodology for 3D air-ground integrated network deployment.

This study addresses the hitherto underexplored problem of antenna placement optimization in clamp-mounted antenna systems. We propose the first analytical optimization framework, deriving closed-form solutions for the optimal antenna positions under both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) paradigms. Methodologically, we integrate rigorous mathematical modeling, asymptotic performance analysis, and multi-access channel characterization to uncover the fundamental geometry–performance mapping. Key results reveal that, under NOMA and greedy OMA, the optimal antenna position asymptotically approaches the nearest user to the waveguide; in contrast, for user-fairness-oriented OMA, the optimal position is independent of user distances. This work fills a critical theoretical gap in the analytical design of clamp-mounted antenna placement and, for the first time, rigorously establishes the decisive influence of multiple access strategy on spatial deployment principles.

This work addresses the challenge of maintaining continuous and reliable communication in low-altitude three-dimensional dynamic environments, where conventional fixed antennas suffer from limited mechanical degrees of freedom and static structures. To overcome these constraints, the paper proposes a mobile antenna architecture that jointly optimizes the three-dimensional antenna placement and beamforming matrix to maximize the received signal-to-noise ratio within a discretized voxel space. It is the first to introduce mobile antennas for enhancing low-altitude communication coverage, integrating mechanical tilt adjustment to enable flexible electromagnetic radiation pattern reconfiguration and transcend traditional deployment limitations. A hybrid particle swarm optimization and simulated annealing algorithm is employed to efficiently solve the resulting non-convex, high-dimensional optimization problem, achieving coverage improvements of 26.8% and 29.65% in airspace below 300 m and 600 m, respectively—significantly outperforming fixed-antenna solutions.

This study addresses the challenge of optimizing wireless transmitter placement in complex urban environments by proposing a mathematical optimization framework that integrates high-fidelity 3D city maps, electromagnetic properties of building materials, and ray-tracing-based channel modeling. The authors formulate a network quality functional that jointly accounts for coverage performance and deployment cost, and for the first time combine realistic urban environmental details with submodular optimization theory to develop an interference-aware placement algorithm (IA-SPA) with provable performance guarantees. IA-SPA supports both greenfield and incremental deployment scenarios. Realistic simulations in San Francisco and Florence demonstrate that, using the same number of transmitters as existing cellular deployments, the proposed approach achieves approximately a 2× improvement in average user throughput and a 2–8× gain for cell-edge users.

This work addresses the joint design of antenna placement and transceiver beamforming for a mobile-antenna (MA)-assisted monostatic full-duplex integrated sensing and communication (FD-ISAC) system, under near-field self-interference channel modeling, to maximize the weighted sum of communication capacity and sensing mutual information. Methodologically, it innovatively incorporates antenna physical position as an optimization variable into the FD-ISAC joint framework for the first time; proposes a coarse–fine two-stage search algorithm to efficiently solve the non-convex position subproblem; and derives a closed-form beamformer solution via KKT conditions, integrated with fractional programming and alternating optimization to ensure global convergence. Results demonstrate that, compared to fixed-antenna benchmarks, the proposed approach achieves significant gains in both communication and sensing performance under strong self-interference suppression, thereby enhancing overall system reliability and integrated effectiveness.

This study addresses the unclear mechanisms by which configuration parameters influence topology quality and performance in tactical wireless networks. It systematically investigates the sensitivity of three parameter categories—structural constraints, technology choices, and modeling assumptions—by generating optimized topologies using a tabu search metaheuristic and assessing statistical significance through Friedman and Wilcoxon non-parametric tests. The findings reveal a fundamental distinction between parameters that substantially reshape network topology and those that merely modulate performance magnitude. Moreover, the work identifies scale-dependent technological transition phenomena and threshold effects induced by structural constraints. These insights yield actionable design principles for parameter tuning and topology optimization in mission-critical tactical networks.

This work addresses the fundamental trade-off between antenna mobility and effective throughput in multiuser downlink systems with reconfigurable mobile antennas, where antenna movement improves channel conditions but incurs time overhead and Doppler-induced distortion. To maximize throughput, the paper jointly optimizes the duration and trajectory of antenna motion, proposing a fitting method based on a small number of sampled channel realizations to derive a closed-form expression for achievable rates. A closed-form threshold on the maximum antenna velocity is further established: below this threshold, the optimal strategy is to remain stationary. The proposed approach combines one-dimensional search with non-convex optimization and is validated in a two-antenna, two-user scenario, demonstrating significant throughput gains and substantially reduced computational complexity.

This work addresses the high computational complexity arising from discrete antenna placement in reconfigurable antenna systems by formulating, for the first time, the antenna layout optimization in multi-user MIMO uplink communications as a monotone submodular maximization problem subject to 2-system constraints. Leveraging submodular optimization theory, the authors propose a low-complexity algorithm that integrates distance-constrained search with robustness analysis, guaranteeing a theoretical performance bound of at least one-third under both perfect and imperfect channel state information. Experimental results demonstrate that the proposed method achieves over 90% of the optimal mutual information gain while accelerating computation by 34.4× compared to the branch-and-bound approach, substantially reducing complexity and exhibiting strong robustness against channel estimation errors.

This work addresses the limitation of fixed antenna arrays in near-field wireless sensing, where constrained spatial degrees of freedom hinder minimization of worst-case localization error. The study investigates optimal placement strategies for movable antennas by minimizing the worst-case squared position error bound (SPEB). Theoretical analysis establishes the optimality of center-symmetric configurations and reduces the worst-case source localization problem to the Rayleigh limit along the array broadside. By integrating the method of moments with the Richter–Tchakaloff theorem, the authors derive a closed-form optimal solution supported on only three points, which satisfies minimum inter-element spacing constraints while enabling highly efficient computation. The proposed scheme achieves performance nearly matching exhaustive search with negligible computational overhead, significantly outperforming conventional fixed-array approaches.

This work addresses the challenge of achieving rate fairness among multiple users in uplink communications, where the geometric configuration of antenna arrays typically entails inherent trade-offs. To overcome this limitation, the paper proposes optimizing the trajectory of movable base station antennas to maximize the minimum achievable rate across all users within a finite time horizon. Theoretical analysis demonstrates that the optimal antenna placement can be selected from a finite set of configurations. Building on this insight, the authors first solve the continuous-time optimization problem under an idealized unlimited-velocity assumption using Lagrangian duality, and then develop a heuristic trajectory algorithm tailored to practical velocity constraints. Numerical results show that the proposed approach significantly outperforms existing benchmarks and offers marked advantages in ensuring communication fairness among users.