Integrated sensing and communication (ISAC) is a new paradigm for efficiently combining sensing and communication functionalities by leveraging shared hardware and radio resources. Despite its promise, ISAC yields conflicting beamforming goals and competition over the same resources. Movable antennas enable effective exploitation of spatial degrees of freedom through dynamic position/orientation control, thereby enhancing the performance of ISAC systems. This paper proposes a movable antenna framework for ISAC in air sea ground networks. A multi-objective optimization problem is formulated with the objectives of maximizing the communication rate of a set of aerial, sea, and ground devices and the sensing rate of a set of targets. The location and orientation of the antenna sub-arrays, as well as the transmit/receive beamforming, are optimized under practical constraints on the movable antennas'location and orientation. A solution is developed based on a $K$-means clustering approach to optimize the sub-arrays'orientation and a particle swarm optimization to place the sub-arrays in optimized locations. The transmit and receive beamforming are designed using a successive convex approximation and a generalized eigenvector method, respectively. Simulation results illustrate that the developed movable antenna framework improves the ISAC objective and provides a remarkable trade-off between the communication data rate and the targets'sensing rate when compared with the conventional stationary antenna array scenario.
Movable antennas (MAs) can reshape the propagation environment by continuously adjusting their spatial positions compared with fixed-position antennas (FPAs). Leveraging this advantage, we develop an alternating optimization framework for covert integrated sensing and communication (ISAC) systems. This framework jointl...
Dan Deng, Xing-Wang Li, Shu-Ping Dang et al.· IEEE Transactions on Informa...· 0 citations
Functional analysis of the SPEB's moment structure proves that the optimal antenna distributions can be realized by a centro-symmetric structure, and shows that the worst-case target is located at the array broadside on the Rayleigh boundary.
An alternating optimization (AO) framework is proposed, employing techniques including discrete binary particle swarm optimization (BPSO), successive convex approximation (SCA) and difference-of-convex (DC) programming to transform the optimization problem into convex subproblems.
Zhen-Dong Li, Min Zhu, Zhou Su et al.· 0 citations
As a core infrastructure of modern information society, cellular networks have long supported terrestrial communications and are now being extended to low-altitude connectivity with the growth of the low-altitude economy. However, the increasing number of aerial vehicles poses new challenges to airspace supervision and...
Zhi-Yong Feng, Lin Wang, Kan Yu et al.· Communications Engineer· 0 citations
Integrated sensing and communication (ISAC) enables simultaneous wireless communication and environmental sensing in unmanned aerial vehicle (UAV) networks. However, practical joint sensing-communication deployments are fundamentally constrained by limited physical apertures and a finite number of radio-frequency (RF)...
Yu-Xiao Liu, Jing Zhang, Jia-Yi Sun et al.· IEEE International Symposium...· 0 citations
Movable antenna (MA) has emerged as a promising technology to unlock spatial degrees of freedom for wireless sensing systems. However, optimizing MA positions for multi-target sensing remains challenging due to the complicated coupling between the array geometry and unknown target directions, as well as severe sidelobe...
Cheng-Zhi Ye, Ruo-Yu Zhang, Li-Peng Zhu et al.· 0 citations
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