Fan Zhang

As the demand for ubiquitous connectivity and high-precision environmental awareness grows, integrated sensing and communication (ISAC) has emerged as a key technology for sixth-generation (6G) wireless networks. Intelligent metasurfaces (IMs) have also been widely adopted in ISAC scenarios due to their efficient, programmable control over electromagnetic waves. This provides a versatile solution that meets the dual-function requirements of next-generation networks. Although reconfigurable intelligent surfaces (RISs) have been extensively studied for manipulating the propagation channel between base and mobile stations, the full potential of IMs in ISAC transceiver design remains under-explored. Against this backdrop, this article explores emerging IM-enabled transceiver designs for ISAC systems. It begins with an overview of representative IM architectures, their unique principles, and their inherent advantages in EM wave manipulation. Next, a unified ISAC framework is established to systematically model the design and derivation of diverse IM-enabled transceiver structures. This lays the foundation for performance optimization, trade-offs, and analysis. The paper then discusses several critical technologies for IM-enabled ISAC transceivers, including dedicated channel modeling, effective channel estimation, tailored beamforming strategies, and dual-functional waveform design.

The integrated sensing and communication (ISAC) has been envisioned as one representative usage scenario of sixth-generation (6G) network. However, the unprecedented characteristics of 6G, especially the doubly dispersive channel, make classical ISAC waveforms rather challenging to guarantee a desirable performance level. The recently proposed affine frequency division multiplexing (AFDM) can attain full diversity even under doubly dispersive effects, thus becoming a competitive candidate for next-generation ISAC waveforms. Relevant investigations are still at an early stage, which involve only straightforward design lacking explicit theoretical analysis. This paper provides an in-depth investigation on AFDM waveform design for ISAC applications. Specifically, the closed-form Crámer-Rao bounds of target detection for AFDM are derived, followed by a demonstration on its merits over existing counterparts. Furthermore, we formulate the ambiguity function of the pilot-assisted AFDM waveform for the first time, revealing conditions for stable sensing performance. To further enhance both the communication and sensing performance of the AFDM waveform, we propose a novel pilot design by exploiting the characteristics of AFDM signals. The proposed design is analytically validated to be capable of optimizing the ambiguity function property and channel estimation accuracy simultaneously as well as overcoming the sensing and channel estimation range limitation originated from the pilot spacing. Numerical results have verified the superiority of the proposed pilot design in terms of dual-functional performance.

Sixth-generation (6G) wireless networks are expected not only to provide high-speed connectivity but also to support reliable sensing capabilities, giving rise to the integrated sensing and communication (ISAC) paradigm. To enable higher data rates and more accurate sensing, terahertz (THz) systems empowered by extremely large multiple-input-multiple-output (XL-MIMO) technology are envisioned as key enablers for future ISAC systems. Owing to the substantial increase in both effective array aperture and carrier frequency, a considerable portion of future ISAC applications is anticipated to fall within the near-field coverage region, instead of the conventional far-field. However, most existing ISAC techniques are designed under the far-field planar wave assumption, struggling to accommodate the unique characteristics of THz near-field propagation. To motivate future research into near-field ISAC research, we systematically investigate the characteristics of THz near-field propagation and explore its potential to facilitate ISAC systems. Specifically, we analyze three fundamental characteristics of THz near-field propagation and review state-of-the-art techniques that exploit these features to boost both communication and sensing performance. To further harness the angular-range coupling effect, we zoom into a particularly interesting approach to near-field sensing based on wavenumber domain. Besides, to exploit the beam squint effect, an ISAC resource allocation framework is introduced to support integrated multi-angle sensing and multi-user communication. Finally, we outline promising directions for future research in this emerging area.

This paper proposes a transmit beamforming strategy for the integrated sensing and communication (ISAC) systems enabled by the novel stacked intelligent metasurface (SIM) architecture, different from conventional single-layer reconfigurable intelligent surface (RIS) by cascading multiple transmissive metasurface layers, where the base station (BS) simultaneously performs downlink communication and radar target detection via fully passive wave domain beamforming, result in the significant reduction in hardware cost and power consumption. To ensure superior dual-function performance simultaneously, we design the multi-layer cascading beamformer by maximizing the sum rate of the users while optimally shaping the normalized beam pattern for detection. A dual-normalized differential gradient descent ( $\rm{D}^{3}$ ) algorithm is further proposed to solve the resulting non-convex multi-objective problem (MOP), where gradient differences and dual normalization are employed to ensure a flexible trade-off between communication and sensing objectives at the gradient level, providing finer control over the optimization process. Numerical results demonstrate the superiority of the proposed beamforming design in terms of balancing communication and sensing performance.

Orthogonal frequency division multiplexing (OFDM) has been widely recognized as the representative waveform for 5G wireless networks, which can directly support sensing/positioning with existing infrastructure. To guarantee superior sensing/positioning accuracy while supporting high-speed communication simultaneously, the dual functions tend to be assigned with different resource elements (REs) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for effective convergence of OFDM-based communication and sensing/positioning, following communication- and sensing-centric criteria, respectively. For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communication according to prior knowledge of the communication channel. The remaining REs are then employed for sensing/positioning, where the sidelobe level and peak-to-average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics for sensing performance improvement. For the sensing-centric design, a ‘locally’ perfect auto-correlation property is ensured for accurate sensing and positioning by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed waveform designs.

Ultra-high-resolution target sensing has emerged as a key enabler for various cutting-edge applications, which can be realized by utilizing the millimeter wave/terahertz frequencies. However, the extremely high operating frequency inevitably leads to significant Doppler shift effects, especially for high-mobility applications, causing the degradation of sensing performance with high false alarm rate. To this end, this paper proposes a parameter design methodology of the well-known constant amplitude zero auto correlation (CAZAC) sequences, which aims at enhancing their resilience to Doppler shifts. Specifically, we suppress the sidelobes incurred by Doppler shifts for the peak-to-sidelobe ratio (PSLR) improvement within the range of interest (RoI) of the radar range profile. The Zadoff-Chu (ZC) sequence, as a representative member in the CAZAC family, is firstly considered. The impacts of its root index on range sidelobes are investigated based on number theory. For an arbitrary-length ZC sequence, a feasible range of the root index is derived to satisfy the requirement of PSLR within the scope of RoI. Furthermore, these design guidelines are extended to a general form of CAZAC sequences, where a low-complexity heuristic algorithm is developed for PSLR improvement. Simulation results demonstrate that under severe Doppler shifts, our proposed methodology could enhance the sensing performance by lowering the false alarm rate while maintaining the same detection rate, compared with its classical counterpart.

In order to avoid frequent millimeter-wave (mmWave) beam training in high-speed scenarios, in this letter, we propose to exploit the neural ordinary differential equation (ODE) to predict the arbitrary-instant optimal beam between the current and next beam training instants. Specifically, long short-term memory (LSTM) network is utilized to model the beam dynamics based on the received signals of previous periodical beam training, and the ODE solver is adopted to learn the derivative of beam variations, so that the optimal beam at arbitrary instant can be predicted by integrating the derivatives. Simulation results demonstrate that our proposed ODE-LSTM assisted methodology could achieve higher beamforming gain over its state-of-the-art counterparts.

Utilizing the densely deployed light-emitting diodes (LEDs) as transmitters, multiple-input-single-output (MISO) becomes an attractive technique to improve the transmission throughput of light-fidelity (LiFi) systems. To fully exploit the benefits of joint transmission of multiple LEDs, beamforming techniques are usually employed to improve the downlink throughput, which requires the knowledge of channel state information (CSI) at transmitter. Since frequent CSI feedback leads to heavy overhead, the optimization of feedback interval, defined as the time period between two adjacent feedbacks, is investigated in this paper, which aims at maximizing the weighted bidirectional transmission throughput. Additionally, a quasi-optimal solution is calculated as a well approximation of the exact optimal solution in order to reduce the computational complexity. Simulation results show that, the proposed feedback interval optimization is capable of improving the weighted bidirectional throughput considerably thanks to the reduced CSI feedback overhead, compared with its conventional counterparts using classical feedback mechanisms. Besides, by employing the obtained quasi-optimal feedback interval, the weighted bidirectional throughput approaches that of the optimal interval with much lower computational complexity.