In future 6G wireless communications, extremely large-scale antenna arrays and high-frequency bandwidths are expected to play a crucial role. Their utilization increases the likelihood that communication devices operate in the near-field region, where traditional transceiver architectures suffer from broadband beam-splitting effects that signifcantly degrade communication performance. To allieviate this issue, this paper studies the utilization of unique frequency selective properties of dynamic metasurface antennas (DMAs) in near-field uplink transmission. Specifically, we aim to configure the tunable parameters of the DMA to maximize the average beamforming gain across all subcarriers. Morever, we propose a deep reinforcement learning framework to efficiently learn the optimal tunable parameters of the DMA. Simulation results demonstrate that the proposed scheme achieves better beamforming gain performance over all subcarriers compared with existing benchmarks.

The integrated sensing and wireless power transfer (ISWPT) technology, in which the radar sensing and wireless power transfer functionalities are implemented using the same hardware platform, has been recently proposed. In this paper, we consider a near-field ISWPT system where one hybrid transmitter deploys extremely large-scale antenna arrays, and multiple energy receivers are located in the near-field region of the transmitter. Under such a new scenario, we study radar sensing and wireless power transfer performance trade-offs by optimizing the transmit beamforming vectors. In particular, we consider the transmit beampattern matching and max-min beampattern gain design metrics. For each radar performance metric, we aim to achieve the best performance of radar sensing, while guaranteeing the requirement of wireless power transfer. The corresponding beamforming design problems are non-convex, and the semi-definite relaxation (SDR) approach is applied to solve them globally optimally. Finally, numerical results verify the effectiveness of our proposed solutions.