The radio frequency (RF) fingerprint technique is a robust method for security enhancement of the physical layer by leveraging the unique RF imperfections inherent in various wireless devices. Among these imperfections, the carrier frequency offset (CFO) stands out as a primary RF fingerprint (RFF) of the transmitter, offering the potential to distinguish among different transmitters. However, accurately estimating CFO in time-varying channels poses significant challenges due to multipath effects and Doppler shifts. In this paper, we focus on estimating CFO for wireless device identification in the orthogonal frequency division multiplexing (OFDM) communication system. To achieve precise CFO estimation under time-varying channels, we propose a frequency domain correlation and spline interpolation (FCSI) algorithm. This approach utilizes pilots distributed across different subcarriers to correlate with prior local sequences, facilitating accurate CFO estimation. Classification is then performed based on the Euclidean distance between the prior RFF and the tested RFF dataset. Simulation results demonstrate that the proposed M-consecutive average method effectively reduces the classification error rate in the challenging high-frequency (HF) skywave channel environment.

Cell-free (CF) multiple-input multiple-output (MIMO) is a promising technique to enable the vision of ubiquitous wireless connectivity for next-generation network communications. Compared to traditional co-located massive MIMO, CF MIMO allows geographically distributed access points (APs) to serve all users on the same time-frequency resource with spatial multiplexing techniques, resulting in better performance in terms of both spectral efficiency and coverage enhancement. However, the performance gain is achieved at the expense of deploying more APs with high cost and power consumption. To address this issue, the recently proposed reconfigurable intelligent surface (RIS) technique stands out with its unique advantages of low cost, low energy consumption and programmability. In this paper, we provide an overview of RIS-assisted CF MIMO and its interaction with advanced optimization designs and novel applications. Particularly, recent studies on typical performance metrics such as energy efficiency (EE) and spectral efficiency (SE) are surveyed. Besides, the application of RIS-assisted CF MIMO techniques in various future communication systems is also envisioned. Additionally, we briefly discuss the technical challenges and open problems for this area to inspire research direction and fully exploit its potential in meeting the demands of future wireless communication systems.

Degree of freedom (DOF) is a key indicator for spatial multiplexing layers of a wireless channel. Traditionally, the channel of a multiple-input multiple-output (MIMO) half-wavelength dipole array has a DOF that equals the antenna number. However, recent studies suggest that the DOF could be less than the antenna number when strong mutual coupling is considered. We utilize a mutual-coupling-compliant channel model to investigate the DOF of the holographic MIMO (HMIMO) channel and give a upper bound of the DOF with strong mutual coupling. Our numerical simulations demonstrate that a dense array can support more DOF per unit aperture as compared with a half-wavelength MIMO system.

As a critical candidate technology for 5G-advanced and 6G, reconfigurable intelligent surfaces (RIS) have received extensive attention from academia and industry. RIS has the promising features of passiveness, reconfigurable ability, and low cost. RIS channel estimation faces the challenges of high matrix dimension, passive estimation, and spatial-wideband effect. In this article, we analyze the impact of the spatial-wideband effect on the RIS channel to account for the propagation delay across RIS elements and estimate sparse channel parameters such as angle and gain through a super-resolution compressive sensing (CS) algorithm. The simulation results explore the influence of the spatial-wideband effect on the RIS channel and verify the effectiveness of the proposed algorithm.