LLM-Empowered Near-Field Communications for Low-Altitude Economy

The low-altitude economy (LAE) has recently received widespread attention from both academia and industry. To facilitate and support the successful implementation of the LAE, we fortunately find that the LAE and near-field communications in extremely large-scale MIMO (XL-MIMO) systems are a natural combination. Specifically, the LAE can utilize the near-field beamfocusing characteristic to accurately focus the beam energy to the positions of different unmanned aerial vehicles, and utilize the new distance dimension to further enhance the entire spectrum efficiency. However, most existing works on near-field communications only consider the ideal scenario in a horizontal plane and how to efficiently achieve near-field communications for LAE is still a blank in the literature and faces several challenges. To fill in this blank, inspired by the powerful large language models (LLM) which can act as a general wireless communications optimization solver, in this paper, we first apply LLM to solve the spectrum efficiency maximization problem of near-field communications for LAE. Specifically, our proposed LLM-based scheme can accurately distinguish far-field and near-field users and achieve joint optimization of precoding and power allocation through elaborately designing adapters and finetuning the pretrained GPT-2. Simulation results substantiate the efficacy and excellence of our proposed scheme compared to the existing benchmark schemes.