In pursuit of real-time sensing within ultra-short duration, conventional sensing algorithms are gradually failing to fulfill the stringent latency demands. Specifically, traditional subspace-based methods such as multiple signal classification (MUSIC) are hindered by their need for an extensive number of snapshots to accumulate the rank of the spatial covariance matrix (SCM), resulting in poor real-time performance. Moreover, advanced techniques like compressed sensing and machine learning are constrained by requirements for high signal sparsity or suffer from limited generality. To handle these challenges, this paper proposes an innovative extension of subspace theory tailored to insufficient-snapshot scenarios, leveraging the concept of spatio-temporal exchangeability. Based on the defined spatio-temporal correlation predicated on the space translation invariance characteristic of uniform linear arrays, we engineer a pseudo SCM that inherently possesses sufficient rank. This methodology not only resolves the rank-deficiency issue but also fully exploits the array aperture and significantly reduces the noise level. Simulation results are presented, substantiating the feasibility and enhanced performance of the proposed algorithms, marking a significant advancement over existing methodologies.

As mobile networks progress towards a unified framework for integrated sensing and communication (ISAC), it is foreseeable to introduce new privacy concerns, particularly the potential exposure of position information to unintended receivers. In other words, the scope of physical-layer security (PLS) needs to be expanded to encompass both communication and sensing privacy. Therefore, in contrast to conventional PLS schemes that focus predominantly on preventing eavesdropping, this paper proposes a novel physical-layer privacy (PLP) design within ISAC frameworks, in order to guarantee the secrecy of data transmission while obscuring transmitter’s directional information. Specifically, we introduce a metric termed angular-domain peak-to-average ratio (ADPAR) to assess sensing resistance (SR) performance. Subsequently, three fundamental optimization problems are formulated under such ADPAR constraints to enhance communication secrecy, depending upon the integrity of illegitimate channel state information. These problems are then tackled using advanced strategies such as null-space projection and the cooperation with artificial noise. Additionally, closed-form solutions are further derived in a few specific cases by leveraging singular value decomposition (SVD) and generalized SVD. Finally, simulation results affirm the effectiveness of our design in safeguarding the twofold privacy within ISAC networks.

Reconfigurable intelligent surface (RIS), a large array of passive scattering elements, is able to control the properties of electromagnetic waves, thereby enhancing the channel capacity, reducing the bit error rate, and enabling novel signal modulation methods. However, the promising gain of RIS depends on the precision of channel estimation. In this paper, we propose a three-step channel reconstruction framework to improve the channel estimation accuracy inspired by the concept of integrated sensing and communication scenario. Firstly, based on the coarse channel state information (CSI), the proposed dual one-dimensional multiple signal classification (D1D-MUSIC) algorithm improves the localization precision with a reduced complexity. Secondly, expectation maximization-based refined estimation (EMRE) algorithms are proposed to refine the CSI and estimate channel statistical properties (CSP), i.e., the shadow fading, the power of line-of-sight paths, and that of non-line-of-sight components. Thirdly, a gradient descent-based pilot optimization (GDPO) algorithm is further derived to improve the channel estimation precision on the basis of estimated CSPs. Finally, simulation results demonstrate that the developed D1D-MUSIC algorithm has lower localization error and complexity compared with conventional two-dimensional MUSIC algorithm. Moreover, the EMRE algorithms achieve the identical normalized mean square error (NMSE) performances as the ideal minimum mean square error estimator, while possessing robust resistance to the channel model mismatch. Furthermore, the developed GDPO technique is capable of providing an over 11 dB signal-to-noise ratio gain for channel estimation performance at NMSE$\bf = 10^{-2}$.

As a revolutionary technology for future wireless communications, reconfigurable intelligent surface (RIS), characterized by an efficient way of manipulating wireless signals, has been widely investigated in recent years toward enhancing signal quality, energy efficiency, throughput, and so on. However, in RIS-assisted Internet of Things (IoT), a new issue as multipath separation emerges, especially, when deploying multiple RISs to assist communication, since the devices may have limited signal processing capabilities. For alleviating this problem, we conceive a novel RIS-enabled code-division multiple access (CDMA) structure, where each RIS holds a specified time-varying coefficient to tag the channel. Moreover, multipath extraction is further considered, including a practical channel estimation approach along with theoretical derivations in terms of Cramér–Rao lower bound, mean-square error, as well as ergodic channel capacity. Simulation results corroborate the feasibility of the conceived RIS-CDMA structure and the effectiveness of the proposed multipath extraction approach.

Recently, communications with the aid of reconfigurable intelligent surface (RIS), which operates with the aim of enhancing the system communication performance, have aroused extensive researches. Furthermore, the use of RIS for positioning has been considered. Therefore, we focus on a practical structure of indoor positioning assisted by distributed RISs through utilizing their ability to manipulate multipath signals, through the developed quasi-static and dynamic modes. Specifically, in the quasi-static mode, for reducing the implementation cost, the reflection coefficients for each RIS are preset and remain constant. In the dynamic mode, the reflection coefficients can be timely updated with a two-step positioning approach toward more accurate positioning performance. Furthermore, the Cramér-Rao lower bound of the developed positioning scheme is quantified through theoretical analysis. Both theoretical analysis and simulation results demonstrate that RIS has the potential to realize accurate positioning even with a single access point, due to its ability to mark the channel and replace traditional active positioning anchors. Meanwhile, we also show that the developed two-step positioning scheme can achieve considerable performance gain in accurate positioning.

Reconfigurable intelligent surface (RIS)-assisted wireless communications have been widely investigated toward enhanced signal quality, energy efficiency as well as throughput. The issue of multipath separation and identification via multiple RISs, which, however, remains to be further investigated, is of paramount importance. For alleviating this problem, we conceive a novel RIS-assisted spreading and code division multiple access (CDMA) structure, where each RIS holds a time-varying reflection coefficient sequence for spreading, in order to tag the channel and distinguish each other with the idea of CDMA. Moreover, practical channel estimation and multipath identification are considered, and the ergodic channel capacity is also derived when the receiver employs maximum ratio combining. Simulation results corroborate the feasibility of the proposed RIS-CDMA structure and the effectiveness of the developed channel estimation and multipath combination approaches.

Novel communication technology based on large intelligent surface (LIS) [1] has arisen recently, with the aim to enhance the signal quality at the receiver. In this paper, a practical structure of LIS-based spatial modulation (LIS-SM) is proposed, in order to utilize both transmit and receive antenna indices. Meanwhile, the theoretical average bit error rate (ABER) performance bound of the developed LIS-SM scheme is investigated. For the sake of achieving further spatial diversity gain, we extend its employment to the antenna selection (AS) scenario, and a low-complexity selection algorithm is designed on the basis of minimum squared Euclidian distance and signal-to-leakage-and-noise ratio as well as the idea of greedy elimination algorithm. Performance analysis shows that AS-aided LIS-SM is more robust in terms of ABER compared with conventional LIS-SM. Moreover, complexity analysis also depicts that the proposed fast selection algorithm achieves much lower complexity yet a comparable ABER performance, compared to the traditional exhaustive search.