Extremely large antenna arrays (ELAAs) operating in high-frequency bands have spurred the development of near-field communication, driving advancements in beam training and signal processing design. In this work, we present a low-complexity near-field beam training scheme that fully utilize the conventional discrete Fourier transform (DFT) codebook designed for far-field users. We begin by analyzing the received beam pattern in the near field and derive closed-form expressions for the beam width and central gain. These analytical results enable the definition of an angle-dependent, modified Rayleigh distance, which effectively distinguishes near-field and far-field user regimes. Building on the analysis, we develop a direct and computationally efficient method to estimate user distance, with a complexity ofO(1), and further improve its accuracy through a simple refinement. Simulation results demonstrate significant gains in both single- and multi-user settings, with up to 2.38 dB SNR improvement over exhaustive search. To further enhance estimation accuracy, we additionally propose a maximum likelihood estimation (MLE) based refinement method, leveraging the Rician distribution of signal amplitudes and achieving accuracy close to the Cramér–Rao bound (CRB). Simulation shows the single-user and multi-user achievable rates can both approach those obtained with ideal channel state information.

This paper proposes an adaptive near-field beam training method to enhance performance in multi-user and multipath environments. The approach identifies multiple strongest beams through beam sweeping and linearly combines their received signals—capturing both amplitude and phase—for improved channel estimation. Two codebooks are considered: the conventional DFT codebook and a near-field codebook that samples both angular and distance domains. As the near-field basis functions are generally non-orthogonal and often over-complete, we exploit sparsity in the solution using LASSO-based linear regression, which can also suppress noise. Simulation results show that the near-field codebook reduces feedback overhead by up to 95% compared to the DFT codebook. The proposed LASSO regression method also maintains robustness under varying noise levels, particularly in low SNR regions. Furthermore, an off-grid refinement scheme is introduced to enhance accuracy especially when the codebook sampling is coarse, improving reconstruction accuracy by 69.4%.

Extended ambiguity performance (EAP), which includes all grating lobes and side peaks, indicates the maximum detectable region without undesired peaks for target parameter estimation and is critical to radar sensor design. Driven by EAP requirements of bi-static sensing, we propose design criteria for orthogonal frequency division multiplexing (OFDM) reference signal (RS) patterns. The design not only improves EAP in both time delay and Doppler shift domains under different types of sensing algorithms, but also reduces resource overhead for integrated communication and sensing. With minimal modifications of post-FFT processing for current RS patterns, guard interval is extended beyond conventional cyclic prefix (CP), while maintaining intersymbol-interference-(ISI)-free delay estimation. For standard-resolution sensing algorithms, a staggering offset of a linear slope that is relatively prime to the RS comb size is suggested. As for super-resolution sensing algorithms, necessary and sufficient conditions of comb RS staggering offsets, plus new patterns synthesized therefrom, are derived for the corresponding achievable EAP. Furthermore, we generalize the RS pattern design criterion for super-resolution sensing algorithms to irregular forms, which minimizes number of resource elements (REs) for associated algorithms to eliminate all side peaks. Starting from staggered comb pattern in current positioning RS, our generalized design eventually removes any regular form for ultimate flexibility. Overall, the proposed techniques are promising to extend the ISI- and ambiguity-free range of distance and speed estimates for radar sensing.

This paper discusses a monostatic multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system, in which the ISAC transmitter sends unified orthogonal frequency division multiplexing (OFDM) signals to serve both communication and sensing purposes simultaneously. In this system, the ISAC transmitter and radar receiver are collocated, utilizing the recent 3GPP Release 18 cyclic-shift-based transmit antenna port virtualization technique to en-able large virtual arrays for MIMO operations. The radar receiver and the data communication receiver share knowledge of the transmit antenna ports for virtual array construction and demodulation, respectively. We investigate the impact of the cyclic shift port virtualization on the communication link and address the resulting frequency selectivity in the communication receiver by introducing precoder selection criteria. This selection aims to minimize capacity loss in the direction of the targeted communication receiver. We conduct a performance analysis considering the optimum capacity loss induced by the cyclic shift code. Additionally, a fast Fourier transform (FFT)-based method is employed for the 4D imaging of sensing targets. Numerical examples show that the proposed monostatic MIMO ISAC system has significant potential for future sixth generation (6G) applications.

Ambiguity performance is a critical criterion in radar sensor design, which indicates the ambiguities arising from multiple targets estimation and detection. We considered a re-quirement-driven selection of OFDM reference signal (RS) patterns based on ambiguity performances for bi-static sensing in integrated communication and sensing with minor modifications of current RSs. An RS pattern with a staggering offset of a linear slope that is relatively prime to the RS comb size is suggested for standard-resolution sensing algorithms to obtain the best ambiguity performances. Moreover, an extended guard interval design is proposed to increase the maximum time delay, that is inter-symbol interference (ISI) free using post-FFT sensing algorithms. The proposed techniques are promising to extend the distance and speed without ambiguities and ISI for sensing.