Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban and non-line-of-sight scenarios, thus limiting their effectiveness in practical deployments. To deal with these challenges, this paper proposes a backscatter device (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in underlying environments for performance enhancement. Specifically, the additional reflective signal paths provided by these ambient devices are exploited to enhance sensing accuracy and communication reliability, respectively. In this system, we define the Pareto boundary characterizing the trade-off between sensing mutual information (SMI) and communication rates to provide fundamental insights for its design. To derive the boundary, we formulate a performance optimization problem within an orthogonal frequency division multiplexing (OFDM) framework, by jointly optimizing time-frequency resource element (RE) allocation, transmit power management, and BD modulation decisions. To tackle the non-convexity of the problem, we decompose it into three subproblems, solved iteratively through a block coordinate descent (BCD) algorithm. Specifically, the RE subproblem is addressed using the successive convex approximation (SCA) method, the power subproblem is solved using an augmented Lagrangian combined water-filling method, and the BD modulation subproblem is tackled using semidefinite relaxation (SDR) methods. Additionally, we demonstrate the generality of the proposed system by showing its adaptability to bistatic ISAC scenarios and MIMO settings. Finally, extensive simulation results validate the effectiveness of the proposed system and its superior performance compared to existing state-of-the-art ISAC schemes.

This work explores the integration of Non-Orthogonal Multiple Access (NOMA) and Orthogonal Delay-Doppler Division Multiplexing (ODDM) within an Integrated Sensing and Communication (ISAC) framework. The proposed system leverages ODDM for high-mobility scenarios and NOMA for efficient resource utilization, thereby enabling enhanced trade-offs between communication and sensing. To achieve a balance between communication and sensing performance, an optimization problem is formulated to maximize the weighted sum of a sensing performance metric and communication throughput by jointly optimizing user grouping, power allocation, and beamforming, which are inherently coupled. To solve this problem efficiently, it is decomposed into three subproblems—user grouping, power allocation, and beamforming—which are then addressed iteratively to improve overall system performance. Simulation results validate the efficacy of the proposed framework under various mobility conditions, demonstrating improved sum-rate and sensing accuracy compared to Orthogonal Multiple Access (OMA) systems. This study offers valuable insights into advanced ISAC architectures, which are critically important for future 6G networks.

Integrated sensing and communication (ISAC) has emerged as a key enabling technology for next-generation wireless networks, seamlessly combining communication and radar sensing functionalities by utilizing shared wireless resources. However, ISAC systems suffer from diminished sensing accuracy and compromised communication reliability in complex propagation environments, particularly in densely obstructed urban or non-line-of-sight scenarios. To cope with these issues, this paper proposes a novel backscatter device (BD)-assisted ISAC system where passive BDs are utilized to concurrently enhance sensing accuracy and communication reliability by providing additional signal-reflecting paths. Furthermore, a joint time-frequency-power resource optimization problem is formulated between sensing mutual information and communication capacity in an orthogonal frequency division multiplexing (OFDM) setting. Then, a Pareto boundary is characterized by solving this dual problem, providing clear insights into fundamental tradeoffs involved between sensing and communication. Specifically, we decompose the formulated optimization problem into two manageable subproblems and iteratively solve them through successive convex approximation (SCA) techniques with a block coordinate descent (BCD) algorithm to address the inherent non-convexity of the problem. Simulation results demonstrate the superior performance of the proposed BD-assisted ISAC system, showing substantial improvements in both sensing and communication metrics compared to state-of-the-art ISAC methods.

Amodal sensing enables the shape reconstruction of occluded objects, facilitating a wide range of sensing applications in complex environments. However, traditional amodal sensing methods based on cameras or LiDAR suffer from privacy issues and performance degradation under poor weather conditions. In this demo, we present a wireless amodal sensing system that leverages mmWave signals to improve robustness and protect privacy. The system first segments the obtained mmWave point clouds into individual object instances, and then reconstructs their complete shapes. Unlike camera and LiDAR-based methods, it is challenging to realize wireless amodal sensing due to the measurement errors caused by wireless channel noise and the data sparsity. To address these challenges, we first design an RCS-enhanced error suppression module to mitigate measurement errors by leveraging the negative correlation between radar cross-section (RCS) values and noise. For the data sparsity, we utilize a modified Transformer architecture to extract diverse geometric features at multiple scales, and incorporate a fine-tuned vision-language model (VLM) to generate semantic features that describe object classes. The geometric and semantic features are finally fused to reconstruct complete object shapes using pre-trained generative models. The effectiveness of the proposed system is demonstrated through extensive experiments in real-world scenarios.

Reconfigurable intelligent surface (RIS) has emerged as a promising solution to enable ultra-massive multiple-input multiple-output (MIMO) for 6G wireless communications. To reduce pilot overhead, multi-resolution codebook-based beam training has been proposed for RIS-aided communication systems. However, the hardware constraints of the RIS such as the limited control capability, mutual coupling, and manufacturing tolerances have not been fully considered, which may result in non-ideal beam patterns and misleading beam search directions, thereby degrading the overall beam training performance. To address this issue, in this paper, we revisit the multi-resolution codebook design and beam training scheme by taking the RIS hardware constraints into account. First, we propose a wide-beam generation method via multi-beam superposition, where the RIS reflection coefficients are optimized to achieve high beam gain with minimal fluctuations. Then, a posterior-based beam training scheme is proposed to adaptively correct erroneous beam search directions, thereby improving the beam training accuracy. We implement a prototype RIS with 32x32 reflective elements and deploy a 26 GHz millimeter-wave RIS-aided wireless communication testbed to experimentally evaluate the proposed scheme. The results indicate that our approach achieves low training overhead and a data rate close to the exhaustive search method.

To satisfy the high data rate requirements, cellular systems will evolve towards the direction of higher carrier frequencies and larger antenna arrays. The conventional phased arrays are hard to fulfill such a vision due to its excessive power consumption induced by numerous phase shifters. To address this issue, Reconfigurable Refractive Surfaces (RRSs) provide a energy efficient solution without relying on phase shifters. With enlarged radiation aperture and increased working frequency, users are more likely to be located in the near field of the RRS. Moreover, the frequency selectivity of the RRS cannot be neglected given the wideband communications enabled by higher frequency bands. These two effects jointly aggravate the beam split problem where the signal strength of different frequency components cannot concentrate on the user, leading to a data rate degradation. In this paper, we study an RRS-based wideband near-field communication system with multiple users. Unlike most existing works, which only considered the beam split effect under near-field conditions, we jointly consider the influence of the frequency selectivity of RRS and near-field conditions on the beam split effect. To mitigate the beam split effect, the time-delay units are introduced in the RRS elements based on which a beamforming scheme is proposed to improve system data rate by jointly optimizing the digital beamformer, the phase shifts of RRS and the time-delay units. Simulation results demonstrate the effectiveness of our proposed scheme.

Wireless transmission is vulnerable to malicious jamming attacks due to the openness of wireless channels, posing a severe threat to wireless communications. Current anti-jamming studies primarily focus on either enhancing desired signals or mitigating jamming, resulting in limited performance. To address this issue, intelligent omni-surface (lOS) is a promising solution. By jointly designing its reflective and refractive properties, the lOS can simultaneously nullify jamming and enhance desired signals. In this paper, we consider an lOS-aided multi-user anti-jamming communication system, aiming to improve desired signals and nullify jamming by optimizing lOS phase shifts and transmit beamforming. However, this is challenging due to the coupled and discrete lOS reflection and refraction phase shifts, the unknown jammer's beamformer, and imperfect jammer-related channel state information. To tackle this, we relax lOS phase shifts to continuous states and optimize with a coupling-aware algorithm using the Cauchy-Schwarz inequality and S-procedure, followed by a local search to recover discrete states. Simulation results show that the proposed scheme significantly improves the sum rate amid jamming attacks.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is expected to play an important role in future sixth generation (6G) networks. Most existing works in this area focus on single-polarized XL-MIMO, where transceivers transmit and receive signals in only one polarization direction, leading to degraded data rates. To improve multiplexing performance, in this paper, we investigate downlink XL-MIMO networks with dual-polarized antennas. However, unlike conventional dual-polarized massive MIMO, the cross-polarization discrimination (XPD) of channels vary across base station antennas in dual-polarized XL-MIMO due to the enlarged antenna aperture, leading to following two challenges. First, conventional near-far field boundary is insufficient as it only accounts for phase differences across array elements while irrespective of XPD differences. Second, existing transmit covariance optimization methods developed for dual-polarized massive MIMO cannot be directly utilized, since they are developed based on uniform XPD and pathloss assumptions. To address these challenges, we model the variations of XPD across antennas, based on which a non-uniform XPD distance is introduced to complement existing near-far field boundary. Based on the new distance criterion, we propose an efficient scheme for optimizing the transmit covariance, which considers the non-uniform XPD and pathloss. Numerical results validate our analysis and demonstrate the effectiveness of the proposed algorithm.

To meet rising data rate demands, cellular systems are expected to evolve towards higher carrier frequencies and larger antenna arrays, but conventional phased arrays face challenges in supporting such a prospection due to their excessive power consumption induced by numerous phase shifters required. Reconfigurable Refractive Surface (RRS) is an energy efficient solution to address this issue without relying on phase shifters. However, the increased radiation aperture size extends the range of the Fresnel region, leading the users to lie in the near-field zone. Moreover, given the wideband communications in higher frequency bands, we cannot ignore the frequency selectivity of the RRS. These two effects collectively exacerbate the beam split issue, where different frequency components fail to converge on the user simultaneously, and finally result in a degradation of the data rate. In this paper, we investigate a RRS-based wideband near-field multi-user communication system. Unlike most existing studies on wideband communications, which consider the beam split effect only with the near-field condition, we study the beam split effect under the influence of both the near-field condition and the frequency selectivity of the RRS. To mitigate the beam split effect, we propose a Delayed-RRS structure, based on which a beamforming scheme is proposed to optimize the user’s data rate. Through theoretical analysis and simulation results, we analyze the influence of the RRS’s frequency selectivity, demonstrate the effectiveness of the proposed beamforming scheme, and reveal the importance of jointly considering the near-field condition and the frequency selectivity of RRS.

Due to the openness of wireless channels, wireless transmission is susceptible to malicious jamming attacks, posing a severe threat to wireless communications. Existing studies on anti-jamming mainly considered enhancing desired signals or mitigating jamming, leading to limited performance. To address this issue, intelligent omni-surface (IOS) is a promising solution, which can simultaneously nullify jamming and enhance desired signals by jointly manipulating its reflective and refractive properties. In this paper, we consider an IOS-aided multi-user anti-jamming communication system. We aim to improve desired signals and nullify jamming by joint IOS phase shifts and transmit beamforming optimization, which is challenging due to the coupled and discrete nature of IOS reflection and refraction phase shifts, unknown jammer’s beamformer, and imperfect jammer-related channel state information. To tackle this, we relax IOS phase shifts to continuous states and develop a coupling-aware algorithm using Cauchy-Schwarz inequality and S-procedure, followed by a local search to recover discrete states. Simulation results show that the proposed scheme significantly improves the sum rate in the presence of jamming attacks.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is expected to be an important technology in future sixth generation (6G) networks. Compared with conventional single-polarized XL-MIMO, where signals are transmitted and received in only one polarization direction, dual-polarized XL-MIMO systems achieve higher data rate by improving multiplexing performances, and thus are the focus of this paper. Due to enlarged aperture, near-field regions become non-negligible in XL-MIMO communications, necessitating accurate near-far field boundary characterizations. However, existing boundaries developed for single-polarized systems only consider phase or power differences across array elements while irrespective of cross-polarization discrimination (XPD) variances in dual-polarized XL-MIMO systems, deteriorating transmit covariance optimization performances. In this paper, we revisit near-far field boundaries for dual-polarized XL-MIMO systems by taking XPD differences into account, which faces the following challenge. Unlike existing near-far field boundaries, which only need to consider co-polarized channel components, deriving boundaries for dual-polarized XL-MIMO systems requires modeling joint effects of co-polarized and cross-polarized components. To address this issue, we model XPD variations across antennas and introduce a non-uniform XPD distance to complement existing near-far field boundaries. Based on the new distance criterion, we propose an efficient scheme to optimize transmit covariance. Numerical results validate our analysis and demonstrate the proposed algorithm’s effectiveness.

Holographic Multiple-Input Multiple-Output (HMIMO), which densely integrates numerous antennas into a limited space, is anticipated to provide higher rates for future 6G wireless communications. The increase in antenna aperture size makes the near-field region enlarge, causing some users to be located in the near-field region. Thus, we are facing a hybrid near-field and far-field communication problem, where conventional far-field modeling methods may not work well. In this paper, we propose a near-far field channel model that does not presuppose whether each path is near-field or far-field, different from the existing work requiring the ratio of the number of near-field paths to that of far-field paths as prior knowledge. However, this gives rise to a new challenge for accurately modeling the channel, as conventional methods of obtaining channel model parameters are not applicable to this model. Therefore, we propose a new method, Expectation-Maximization (EM)-based Near-Far Field Channel Modeling, to obtain channel model parameters, which considers whether each path is near-field or far-field as a hidden variable, and optimizes the hidden variables and channel model parameters through an alternating iteration method. Simulation results show that our method is superior to conventional near-field and far- field algorithms in fitting the near-far field channel in terms of outage probability.

Holographic MIMO communications, enabled by large-scale antenna arrays with quasi-continuous apertures, are potential technology for spectrum efficiency improvement. However, the increased antenna aperture size extends the range of the Fresnel region, leading to a hybrid near-far field communication mode. The users and scatterers randomly lie in near-field and far-field zones, and thus, conventional far-field-only and near-field-only channel estimation methods may not work. To tackle this challenge, we demonstrate the existence of the power diffusion (PD) effect, which leads to a mismatch between the hybrid-field channel and existing channel estimation methods. Specifically, in far-field and near-field transform domains, the power of one channel path may diffuse to other positions, thus generating fake paths. This renders the conventional techniques unable to detect those real paths. We propose a PD-aware orthogonal matching pursuit (PD-OMP) algorithm to eliminate the influence of the PD effect by identifying the PD range, within which the path power diffuses to other positions. PD-OMP fits a general case without prior knowledge of respective numbers of near-field and far-field paths and the user’s location. Simulation results show that PD-OMP can accurately estimate the channel when antenna spacing is below half wavelength and outperform current state-of-the-art hybrid-field channel estimation methods.

Holographic multiple-input-multiple output (HMIMO) technology, which is enabled by large-scale antenna arrays with quasi-continuous apertures, is expected to be an important technology in the forthcoming 6G wireless network. Reconfigurable intelligent surface (RIS)-based antennas provide an energy-efficient solution for implementing HMIMO. Most existing works in this area focus on single-polarized RIS-enabled HMIMO, where the RIS can only reflect signals in one polarization towards users and signals in the other polarization cannot be received by intended users, leading to degraded data rate. To improve multiplexing performance, in this paper, we consider a dual-polarized RIS-enabled single-user HMIMO network, aiming to optimize power allocations across polarizations and analyze corresponding maximum system capacity. However, due to interference between different polarizations, the dual-polarized system cannot be simply decomposed into two independent single-polarized ones. Therefore, existing methods developed for the single-polarized system cannot be directly applied, which makes the optimization and analysis of the dual-polarized system challenging. To cope with this issue, we derive an asymptotically tight upper bound on the ergodic capacity, based on which the power allocations across two polarizations are optimized. Potential gains achievable with such dual-polarized RIS are analyzed. Numerical results verify our analysis.

Holographic massive-input-massive-output (HMIMO) is expected to play an important role in 6G, which integrates numerous antennas or reconfigurable elements into a compact surface to form a continuous aperture. However, it is not energy efficient to implement the HMIMO with conventional phased arrays, since hundreds of energy-intensive phase shifters are required, leading to inevitably huge power consumption and degraded energy efficiency. Compared with the phased array, metasurface-based antennas, also referred to as reconfigurable refractive surface (RRS), can significantly improve the energy efficiency, since they are free of those energy-hungry phase shifters. In this paper, we consider an RRS-enabled multi-user HMIMO system, where the energy efficiency of the system is maximized by optimizing the size of the RRS. However, different from the traditional metasurfaces that locate far from the base station (BS) and work as relays, the RRS is much closer to the BS such that the BS antennas cannot be assumed to locate in the far field of the RRS. Therefore, it is challenging to maximize the energy efficiency of the RRS-aided system. To cope with this issue, the capacity and power consumption of this system are analyzed first, based on which the energy efficiency is maximized by optimizing the number of RRS elements. The maximized energy efficiency is then compared against that obtained by the phased array. Through theoretical analysis and simulations, we verify that the RRS is a more energy efficient solution to HMIMO than the phased array when the power consumption per RRS element is lower than a derived closed-form threshold.

To realize holographic communications, a potential technology for spectrum efficiency improvement in the future sixth-generation (6G) network, antenna arrays inlaid with numerous antenna elements will be deployed. However, the increase in antenna aperture size makes some users lie in the Fresnel region, leading to the hybrid near-field and far-field communication mode, where the conventional far-field channel estimation methods no longer work well. To tackle the above challenge, this paper considers channel estimation in a hybrid-field multipath environment, where each user and each scatterer can be in either the far-field or the near-field region. First, a joint angular-polar domain channel transform is designed to capture the hybrid-field channel's near-field and far-field features. We then analyze the power diffusion effect in the hybrid-field channel, which indicates that the power corresponding to one near-field (far-field) path component of the multipath channel may spread to far-field (near-field) paths and causes estimation error. We design a novel power-diffusion-based orthogonal matching pursuit channel estimation algorithm (PD-OMP). It can eliminate the prior knowledge requirement of path numbers in the far field and near field, which is a must in other OMP-based channel estimation algorithms. Simulation results show that PD-OMP outperforms current hybrid-field channel estimation methods.

Intelligent omni-directional surfaces (IOS), which can simultaneously reflect and refract incident signals, are considered as a promising solution for enhancing communication quality. To conduct joint beamforming of the BS and IOS, beam training is introduced such that perfect channel state information is not required anymore. However, the propagation environment is usually dynamically varying in practice, leading to frequent beam training procedures and huge training overhead. In this paper, we propose a transfer learning based beam training scheme for the IOS-assisted multi-user system to adapt to the dynamically changing propagation environment. We first build on an offline phase to train a beam prediction model that outputs the optimal beam with the highest data rate given only the received power of a small number of beams as the input. Then a transfer learning based method is developed such that the above beam prediction model can be updated to adapt to the dynamic environment rapidly. Simulation results demonstrate that the proposed scheme outperforms the existing beam training schemes in dynamic environments in terms of the convergence speed and the sum rate.

Holographic Multiple Input Multiple Output (HMIMO), which integrates massive antenna elements into a compact space, has been considered as a promising enabling technique for future wireless networks. For the HMIMO implemented by traditional phased arrays, the system capacity is insufficient to satisfy the requirement of future networks since the phased array requires energy-consuming phase shifters. Compared with the phased array, reconfigurable refractive surface (RRS), which is free of phase shifters, can significantly improve the system capacity given the same power budget. In this paper, we consider a multi-user RRS-based HMIMO system. Unlike traditional metasurfaces working as passive relays, the RRS is used as transmit antennas, indicating that the RRS is much closer to the feeds. Therefore, the far-field approximation no longer holds, urging a new performance analysis framework. To address the above challenge, we first derive the system capacity, which is then compared against that obtained by the phased array. Simulation results verify our analysis and show that the RRS can bring higher system capacity.

The intelligent omni-surface (IOS) is a dynamic metasurface that has recently been proposed to achieve full-dimensional communications by realizing the dual function of anomalous reflection and anomalous refraction. Existing research works provide only simplified models for the reflection and refraction responses of the IOS, which do not explicitly depend on the physical structure of the IOS and the angle of incidence of the electromagnetic (EM) waves. Therefore, the available reflection-refraction models are insufficient to characterize the performance of full-dimensional communications. In this paper, we propose a complete and detailed circuit-based reflection-refraction model for the IOS, which is formulated in terms of the physical structure and equivalent circuits of the IOS elements, as well as we validate it with the aid of full-wave EM simulations. Based on the proposed circuit-based model for the IOS, we analyze the asymmetry between the reflection and transmission coefficients. Moreover, the proposed circuit-based model is utilized for optimizing the hybrid beamforming of IOS-assisted networks and hence improving the system performance. To verify the circuit-based model, the theoretical findings, and to evaluate the performance of full-dimensional beamforming, we implement a prototype of IOS and deploy an IOS-assisted wireless communication testbed to experimentally measure the beam patterns and to quantify the achievable rate. The obtained experimental results validate the theoretical findings and the accuracy of the proposed circuit-based reflection-refraction model for IOSs.

In this paper, we consider a single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-and-forward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming to improve the throughput while guaranteeing the user fairness, we jointly optimize the communication mode, subchannel allocation, power allocation, and UAV trajectory, which is an NP-hard problem. To design the UAV trajectory and resource allocation efficiently, we first decompose the problem into three subproblems, i.e., mode selection and subchannel allocation, trajectory optimization, and power allocation, and then solve these subproblems iteratively. Simulation results show that the proposed algorithm outperforms the random algorithm and the cellular scheme.

In this paper, we consider a single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-and-forward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming to improve the throughput while guaranteeing user fairness, we formulate a joint mode selection, subchannel allocation, trajectory optimization, and power allocation problem, which is NP-hard. To solve the problem efficiently, we first decompose it into three subproblems, i.e., mode selection and subchannel allocation, trajectory optimization, and power allocation. Then we propose a joint mode selection and subchannel allocation, trajectory optimization, and power allocation (JMS-T-P) algorithm where these subproblems are solved iteratively. Simulation results show that the JMS- T-P algorithm outperforms the random algorithm and the cellular scheme.