Fluid antenna is a promising wireless communication technology that enhances communication rate by changing the antenna positions. This article proposes a new communication system that combines multiple-input single-output (MISO) fluid antennas with traditional fixed-position antennas, utilizing antenna position optimization to improve energy harvesting efficiency. In this model, we consider simultaneous wireless information and power transfer (SWIPT) which transmits identical signals from the base station to both information receiver (IR) and energy receiver (ER). We strive to enhance the power delivered to the ER by fine-tuning the positions of transmit and receive fluid antennas, along with optimizing the transmit covariance matrix, subject to a given minimum signal-to-interference-plus-noise ratio (SINR) constraint at the IR. Simulation results indicate that fluid antenna systems significantly enhance the energy harvesting efficiency of the ER compared to traditional fixed-position antennas.
Air-sea interface fluxes significantly impact the reliability and efficiency of maritime communication. Compared to sparse in-situ ocean observations, satellite remote sensing data offers broader coverage and extended temporal span. This study utilizes COARE V3.5 algorithm to calculate momentum flux, sensible heat flux, and latent heat flux at the air-sea interface, based on satellite synthetic aperture radar (SAR) wind speed data, reanalysis data, and buoy measurements, combined with neural network methods. Findings indicate that SAR wind speed data corrected via neural networks show improved consistency with buoy-measured wind speeds in flux calculations. Specifically, the bias in friction velocity decreased from -0.03 m/s to 0.01 m/s, wind stress bias from -0.03 N/m^2 to 0.00 N/m^2, drag coefficient bias from -0.29 to -0.21, latent heat flux bias from -8.32 W/m^2 to 5.41 W/m^2, and sensible heat flux bias from 0.67 W/m^2 to 0.06 W/m^2. Results suggest that the neural network-corrected SAR wind speed data can provide more reliable environmental data for maritime communication.
This paper studies energy-efficient hybrid beamforming architectures and its algorithm design in millimeter-wave communication systems, aiming to address the challenges faced by existing hybrid beamforming due to low hardware flexibility and high power consumption. To solve the problems of existing hybrid beamforming, a novel energy-efficient hybrid beamforming architecture is proposed, where radio-frequency (RF) switch networks are introduced at the front and rear ends of the phase shifter network, enabling dynamic connections between the RF chains and the phase shifter array as well as the antenna array. The system model of the proposed architecture is established, including digital precoding and analog precoding processes, and the practical hardware limitations such as quantization errors of the digital-to-analog converter (DAC) and phase shifter resolution. In order to maximize the energy efficiency, this paper derives an energy efficiency model including spectral efficiency and system power consumption, and a hybrid precoding algorithm is proposed based on block coordinate descent to iteratively optimize the digital precoding matrix, analog precoding matrix, and DAC resolution. Simulation results under the NYUSIM-generated millimeter-wave channels show that the proposed hybrid beamforming architecture and precoding algorithm have higher energy efficiency than existing representative architectures and precoding algorithms under complete and partial channel state information, while the loss of spectral efficiency compared to fully connected architecture is less than 20
In large-scale multiple-input multiple-output (U-MIMO) orthogonal frequency division multiplexing (OFDM) systems, the frequency domain channel exhibits non-stationarity, exacerbated by limited pilot positions which complicate channel estimation. This letter extends single-band channel estimation to multi-band scenarios, focusing on frequency domain non-stationary sub-6 GHz - millimeter-wave(mmWave) systems. A novel framework leveraging a multi-modal attention mechanism is proposed: it employs a multi-modal input network to extract sub-6 GHz channel details and fine-tune with limited mmWave data. An adaptive attention mechanism dynamically adjusts information weights, facilitating effective feature fusion for mmWave channel estimation. The algorithm's reliability and effectiveness in non-stationary channel estimation are validated through theoretical analysis and numerical simulations in adaptive frequency domains.
Near-field propagation, particularly that enabled by reconfigurable intelligent surfaces (RIS), has emerged as a promising research topic in recent years. However, a comprehensive literature review on RIS-based near-field technologies is still lacking. This article aims to fill this gap by providing a brief overview of near-field concepts and a systematic survey of the state-of-the-art RIS-based near-field technologies. The focus is on three key aspects: the construction of ubiquitous near-field wireless propagation environments using RIS, the enabling of new near-field paradigms for 6G networks through RIS, and the challenges faced by RIS-based near-field technologies. This technical review intends to facilitate the development and innovation of RIS-based near-field technologies.
