Integrated Sensing, Communication, and Powering (ISCAP): Towards Multi-functional 6G Wireless Networks

This article presents a novel multi-functional system for a sixth-generation (6G) wireless network with integrated sensing, communication, and powering (ISCAP), which unifies integrated sensing and communication (ISAC) and wireless information and power transfer (WIPT) techniques. The multi-functional ISCAP network promises to enhance resource utilization efficiency, reduce network costs, and improve overall performance through versatile operational modes. Specifically, a multi-functional base station (BS) can enable multi-functional transmission, by exploiting the same radio signals to perform target/environment sensing, wireless communication, and wireless power transfer (WPT), simultaneously. Besides, the three functions can be intelligently coordinated to pursue mutual benefits,i.e., wireless sensing can be leveraged to enable light-training or even training-free WIPT by providing side-channel information, and the BS can utilize WPT to wirelessly charge low-power devices for ensuring sustainable ISAC. Furthermore, multiple multi-functional BSs can cooperate in both transmission and reception phases for efficient interference management, multi-static sensing, and distributed energy beamforming. For these operational modes, we discuss the technical challenges and potential solutions, particularly focusing on the fundamental performance tradeoff limits, transmission protocol design, as well as waveform and beamforming optimization. Finally, interesting research directions are identified.

Secure Cell-Free Integrated Sensing and Communication in the Presence of Information and Sensing Eavesdroppers

This paper studies a secure cell-free integrated sensing and communication (ISAC) system, in which multiple ISAC transmitters collaboratively send confidential information to multiple communication users (CUs) and concurrently conduct target detection. Different from prior works investigating communication security against potential information eavesdropping, we consider the security of both communication and sensing in the presence of both information and sensing eavesdroppers that aim to intercept confidential communication information and extract target information, respectively. Towards this end, we optimize the joint information and sensing transmit beamforming at these ISAC transmitters for secure cell-free ISAC. Our objective is to maximize the detection probability over a designated sensing area while ensuring the minimum signal-to-interference-plus-noise-ratio (SINR) requirements at CUs. Our formulation also takes into account the maximum tolerable signal-to-noise ratio (SNR) at information eavesdroppers for ensuring the confidentiality of information transmission, and the maximum detection probability constraints at sensing eavesdroppers for preserving sensing privacy. The formulated secure joint transmit beamforming problem is highly non-convex due to the intricate interplay between the detection probabilities, beamforming vectors, and SINR constraints. Fortunately, through strategic manipulation and via applying the semidefinite relaxation (SDR) technique, we successfully obtain the globally optimal solution to the design problem by rigorously verifying the tightness of SDR. Furthermore, we present two alternative joint beamforming designs based on the sensing SNR maximization over the specific sensing area and the coordinated beamforming, respectively. Numerical results reveal the benefits of our proposed design over these alternative benchmarks.

Sensing-Assisted Sparse Channel Recovery for Massive Antenna Systems

This correspondence presents a novel sensing-assisted sparse channel recovery approach for massive antenna wireless communication systems. We focus on a fundamental configuration with one massive-antenna base station (BS) and one single-antenna communication user (CU). The wireless channel exhibits sparsity and consists of multiple paths associated with scatterers detectable via radar sensing. Under this setup, the BS first sends downlink pilots to the CU and concurrently receives the echo pilot signals for sensing the surrounding scatterers. Subsequently, the CU sends feedback information on its received pilot signal to the BS. Accordingly, the BS determines the sparse basis based on the sensed scatterers and proceeds to recover the wireless channel, exploiting the feedback information based on advanced compressive sensing (CS) algorithms. Numerical results show that the proposed sensing-assisted approach significantly increases the overall achievable rate than the conventional design relying on a discrete Fourier transform (DFT)-based sparse basis without sensing, thanks to the reduced training overhead and enhanced recovery accuracy with limited feedback.

Training-Free Energy Beamforming Assisted by Wireless Sensing

This paper studies the transmit energy beamforming in a multi-antenna wireless power transfer (WPT) system, in which an access point (AP) equipped with a uniform linear array (ULA) sends radio signals to wirelessly charge multiple single-antenna energy receivers (ERs). Different from conventional energy beamforming designs that require the AP to acquire the channel state information (CSI) via training and feedback, we propose a new training-free energy beamforming approach assisted by wireless radar sensing, which is implemented based on the following two-stage protocol. In the first stage, the AP performs wireless radar sensing to estimate the path gain and angle parameters of the ERs for constructing the corresponding CSI. In the second stage, the AP implements the transmit energy beamforming based on the constructed CSI to efficiently charge these ERs in a fair manner. Under this setup, first, we jointly optimize the sensing beamformers and duration in the first stage to minimize the sensing duration, while ensuring a given accuracy threshold for parameters estimation subject to the maximum transmit power constraint at the AP. Next, we optimize the energy beamformers in the second stage to maximize the minimum harvested energy by all ERs. In this approach, the estimation accuracy threshold for the first stage is properly designed to balance the resource allocation between the two stages for optimizing the ultimate energy harvesting performance. Finally, numerical results show that the proposed training-free energy beamforming design performs close to the performance upper bound with perfect CSI, and outperforms the benchmark schemes without such joint optimization and that with isotropic transmission.

Fundamental CRB-Rate Tradeoff in Multi-Antenna ISAC Systems with Information Multicasting and Multi-Target Sensing

This paper investigates the performance tradeoff for a multi-antenna integrated sensing and communication (ISAC) system with simultaneous information multicasting and multi-target sensing, in which a multi-antenna base station (BS) sends the common information messages to a set of single-antenna communication users (CUs) and estimates the parameters of multiple sensing targets based on the echo signals concurrently. We consider two target sensing scenarios without and with prior target knowledge at the BS, in which the BS is interested in estimating the complete multi-target response matrix and the target reflection coefficients/angles, respectively. First, we consider the capacity-achieving transmission and characterize the fundamental tradeoff between the achievable rate and the multi-target estimation Cram\'er-Rao bound (CRB) accordingly.

Robust Transmit Beamforming for Secure Integrated Sensing and Communication

This paper studies a downlink secure integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) transmits confidential messages to a single-antenna communication user (CU) while performing sensing on targets that may act as suspicious eavesdroppers. To ensure the quality of target sensing while preventing their potential eavesdropping, the BS combines the transmit confidential information signals with additional dedicated sensing signals, which play a dual role of artificial noise (AN) for degrading the qualities of eavesdropping channels. Under this setup, we jointly design the transmit information and sensing beamforming, with the objective of minimizing the weighted sum of beampattern matching errors and cross-correlation patterns for sensing subject to secure communication constraints. The robust design takes into account the channel state information (CSI) imperfectness of the eavesdroppers in two practical CSI error scenarios. First, we consider the scenario with bounded CSI errors of eavesdroppers, in which the worst-case secrecy rate constraint is adopted to ensure secure communication performance. In this scenario, we present the optimal solution to the worst-case secrecy rate constrained sensing beampattern optimization problem, by adopting the techniques of S-procedure, semi-definite relaxation (SDR), and a one-dimensional (1D) search, for which the tightness of the SDR is rigorously proved. Next, we consider the scenario with Gaussian CSI errors of eavesdroppers, in which the secrecy outage probability constraint is adopted. In this scenario, we present an efficient algorithm to solve the more challenging secrecy outage-constrained sensing beampattern optimization problem, by exploiting the convex restriction technique based on the Bernstein-type inequality, together with the SDR and 1D search.

Fundamental CRB-Rate Tradeoff in Multi-antenna Multicast Channel with ISAC

This paper studies the multi-antenna multicast channel with integrated sensing and communication (ISAC), in which a multi-antenna base station (BS) sends common messages to a set of single-antenna communication users (CUs) and simultaneously estimates the parameters of an extended target via radar sensing. We investigate the fundamental performance limits of this ISAC system, in terms of the achievable rate for communication and the estimation Cram\'er-Rao bound (CRB) for sensing. First, we derive the optimal transmit covariance in semi-closed form to balance the CRB-rate (C-R) tradeoff, and accordingly characterize the outer bound of a so-called C-R region. It is shown that the optimal transmit covariance should be of full rank, consisting of both information-carrying and dedicated sensing signals in general. Next, we consider a practical joint information and sensing beamforming design, and propose an efficient approach to optimize the joint beamforming for balancing the C-R tradeoff. Numerical results are presented to show the C-R region achieved by the optimal transmit covariance and the joint beamforming, as compared to other benchmark schemes.

Optimal Transmit Beamforming for Secrecy Integrated Sensing and Communication

This paper studies a secrecy integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) aims to send confidential messages to a single-antenna communication user (CU), and at the same time sense several targets that may be suspicious eavesdroppers. To ensure the sensing quality while preventing the eavesdropping, we consider that the BS sends dedicated sensing signals (in addition to confidential information signals) that play a dual role of artificial noise (AN) for confusing the eavesdropping targets. Under this setup, we jointly optimize the transmit information and sensing beamforming at the BS, to minimize the matching error between the transmit beampattern and a desired beampattern for sensing, subject to the minimum secrecy rate requirement at the CU and the transmit power constraint at the BS. Although the formulated problem is non-convex, we propose an algorithm to obtain the globally optimal solution by using the semidefinite relaxation (SDR) together with a one-dimensional (1D) search. Next, to avoid the high complexity induced by the 1D search, we also present two sub-optimal solutions based on zero-forcing and separate beamforming designs, respectively. Numerical results show that the proposed designs properly adjust the information and sensing beams to balance the tradeoffs among communicating with CU, sensing targets, and confusing eavesdroppers, thus achieving desirable transmit beampattern for sensing while ensuring the CU's secrecy rate.