Low-altitude unmanned aerial vehicle (UAV) swarms are expected to play important role for future intelligent aerial systems due to their great potential to cooperatively accomplish complicated missions effectively. However, there are important challenges to be addressed to enable their efficient operation: the large-scale nature of swarms usually leads to excessive spectrum consumption, and ultra-low-cost requirements for individual UAVs renders it necessary to develop cost-effective communication modules. In addition, the densely located swarm UAVs require high resolution for localization and sensing. In order to address the above challenges and simultaneously achieve spectrum- and energy-efficient communication and accurate sensing, we investigate low-altitude UAV swarm with integrated super-resolution sensing and symbiotic communication technology. Specifically, one leading UAV may act as a primary transmitter (PT) to transmit communication signals to the base station (BS), and the remaining nearby UAVs in the swarm act as passive backscatter devices (BDs), which can modulate their information by efficiently backscattering the radio frequency (RF) signals from the PT without consuming extra spectrum or power. In addition, to achieve efficient three-dimensional (3D) super-resolution sensing for the densely located UAV swarm, 3D sparse multiple-input multiple-output (MIMO) technology and super-resolution signal processing algorithms are further exploited, where both L-shaped nested array (LNA) and planar nested arrays (PNA) are considered at the BS. To evaluate the communication and sensing performance for the UAV-symbiotic radio (SR) system, the achievable rates of UAV swarm are derived and the beam patterns of sparse LNA, PNA and the benchmarking compact uniform planar array (UPA) are compared. Furthermore, efficient channel estimation methods assisted by super-resolution sensing are proposed. Simulation results are provided to demonstrate that 3D sparse MIMO with both LNA and PNA can provide significantly better sensing and communication performance than conventional compact MIMO for UAV-SR systems.

Movable antenna (MA), which can flexibly change the position of antenna in three-dimensional (3D) continuous space, is an emerging technology for achieving full spatial performance gains. In this paper, a prototype of MA communication system with ultra-accurate movement control is presented to verify the performance gain of MA in practical environments. The prototype utilizes the feedback control to ensure that each power measurement is performed after the MA moves to a designated position. The system operates at 3.5 GHz or 27.5 GHz, where the MA moves along a one-dimensional horizontal line with a step size of 0.01λ and in a two-dimensional square region with a step size of 0.05λ, respectively, with λ denoting the signal wavelength. The scenario with mixed line-of-sight (LoS) and non-LoS (NLoS) links is considered. Extensive experimental results are obtained with the designed prototype and compared with the simulation results, which validate the great potential of MA technology in improving wireless communication performance. For example, the maximum variation of measured power in the considered scenario reaches over 40 dB and 23 dB at 3.5 GHz and 27.5 GHz, respectively, thanks to the flexible antenna movement. In addition, experimental results indicate that the power gain of MA system relies on the estimated path state information (PSI), including the number of paths, their elevation and azimuth angles of arrival (AoAs), as well as the complex gain of each path.

Integrated sensing and communications (ISAC) is a pivotal technology for low-altitude unmanned aerial vehicle (UAV) swarm. As the sensing targets for UAV swarm systems are usually densely located, the conventional compact multi-input multi-output (MIMO) with half-wavelength antenna spacing usually leads to prohibitive hardware, energy and signal processing costs when large array aperture is needed to achieve fine spatial resolution. By relaxing the traditional half-wavelength spacing constraint, sparse MIMO may achieve a larger array aperture without having to increase the number of antenna elements or radio frequency (RF) chains, which improves spatial resolution for both communication and sensing. Besides, sparse MIMO may also result in a larger near-field region. Therefore, in this paper, we study the near-field sparse MIMO bistatic orthogonal frequency division multiplexing (OFDM)-ISAC for low-altitude UAV swarm systems and propose the framework that utilizes physical array for communication while virtual array for sensing. The proposed method forms virtual arrays at the ISAC transmitter and sensing receiver simultaneously, while eliminating the angle and range coupling effect. As a result, high-resolution angle estimation based on virtual array is achieved. Simulation results demonstrate that sparse MIMO simultaneously improves communication sum rates and sensing resolution compared to the conventional compact MIMO.