ISAC Meets SWIPT: Multi-functional Wireless Systems Integrating Sensing, Communication, and Powering

This paper unifies integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT), by investigating a new multi-functional multiple-input multiple-output (MIMO) system integrating wireless sensing, communication, and powering. In this system, one multi-antenna hybrid access point (H-AP) transmits wireless signals to communicate with one multi-antenna information decoding (ID) receiver, wirelessly charge one multi-antenna energy harvesting (EH) receiver, and perform radar target sensing based on the echo signal at the same time. Under this setup, we aim to reveal the fundamental performance tradeoff limits among sensing, communication, and powering, in terms of the estimation Cramer-Rao bound (CRB), achievable communication rate, and harvested energy level, respectively. In particular, we consider two different target models for radar sensing, namely the point and extended targets, for which we are interested in estimating the target angle and the complete target response matrix, respectively. For both models, we define the achievable CRB-rate-energy (C-R-E) region and characterize its Pareto boundary by maximizing the achievable rate at the ID receiver, subject to the estimation CRB requirement for target sensing, the harvested energy requirement at the EH receiver, and the maximum transmit power constraint at the H-AP. We obtain the well-structured optimal transmit covariance solutions to the two formulated problems by applying advanced convex optimization techniques. Numerical results show the optimal C-R-E region boundary achieved by our proposed design, as compared to the benchmark schemes based on time switching and eigenmode transmission (EMT).Comment: 30 pages, 9 figures, submitted to IEEE TCOM. arXiv admin note: substantial text overlap with arXiv:2210.1671

Optimal Transmit Beamforming for Integrated Sensing and Communication

This paper studies the transmit beamforming in a downlink integrated sensing and communication (ISAC) system, where a base station (BS) equipped with a uniform linear array (ULA) sends combined information-bearing and dedicated radar signals to simultaneously perform downlink multiuser communication and radar target sensing. Under this setup, we maximize the radar sensing performance (in terms of minimizing the beampattern matching errors or maximizing the minimum weighted beampattern gains), subject to the communication users' minimum signal-to-interference-plus-noise ratio (SINR) requirements and the BS's transmit power constraints. In particular, we consider two types of communication receivers, namely Type-I and Type-II receivers, which do not have and do have the capability of cancelling the interference from the {\emph{a-priori}} known dedicated radar signals, respectively. Under both Type-I and Type-II receivers, the beampattern matching and minimum weighted beampattern gain maximization problems are globally optimally solved via applying the semidefinite relaxation (SDR) technique together with the rigorous proof of the tightness of SDR for both Type-I and Type-II receivers under the two design criteria. It is shown that at the optimality, radar signals are not required with Type-I receivers under some specific conditions, while radar signals are always needed to enhance the performance with Type-II receivers. Numerical results show that the minimum weighted beampattern gain maximization leads to significantly higher beampattern gains at the worst-case sensing angles with a much lower computational complexity than the beampattern matching design. We show that by exploiting the capability of canceling the interference caused by the radar signals, the case with Type-II receivers results in better sensing performance than that with Type-I receivers and other conventional designs.Comment: submitted for possible journal publicatio

An Overview on IEEE 802.11bf: WLAN Sensing

With recent advancements, the wireless local area network (WLAN) or wireless fidelity (Wi-Fi) technology has been successfully utilized to realize sensing functionalities such as detection, localization, and recognition. However, the WLANs standards are developed mainly for the purpose of communication, and thus may not be able to meet the stringent requirements for emerging sensing applications. To resolve this issue, a new Task Group (TG), namely IEEE 802.11bf, has been established by the IEEE 802.11 working group, with the objective of creating a new amendment to the WLAN standard to meet advanced sensing requirements while minimizing the effect on communications. This paper provides a comprehensive overview on the up-to-date efforts in the IEEE 802.11bf TG. First, we introduce the definition of the 802.11bf amendment and its formation and standardization timeline. Next, we discuss the WLAN sensing use cases with the corresponding key performance indicator (KPI) requirements. After reviewing previous WLAN sensing research based on communication-oriented WLAN standards, we identify their limitations and underscore the practical need for the new sensing-oriented amendment in 802.11bf. Furthermore, we discuss the WLAN sensing framework and procedure used for measurement acquisition, by considering both sensing at sub-7GHz and directional multi-gigabit (DMG) sensing at 60 GHz, respectively, and address their shared features, similarities, and differences. In addition, we present various candidate technical features for IEEE 802.11bf, including waveform/sequence design, feedback types, as well as quantization and compression techniques. We also describe the methodologies and the channel modeling used by the IEEE 802.11bf TG for evaluation. Finally, we discuss the challenges and future research directions to motivate more research endeavors towards this field in details.Comment: 31 pages, 25 figures, this is a significant updated version of arXiv:2207.0485

Near-Field 3D Localization via MIMO Radar: Cram\'er-Rao Bound and Estimator Design

Future sixth-generation (6G) networks are envisioned to provide both sensing and communications functionalities by using densely deployed base stations (BSs) with massive antennas operating in millimeter wave (mmWave) and terahertz (THz). Due to the large number of antennas and the high frequency band, the sensing and communications will operate within the near-field region, thus making the conventional designs based on the far-field channel models inapplicable. This paper studies a near-field multiple-input-multiple-output (MIMO) radar sensing system, in which the transceivers with massive antennas aim to localize multiple near-field targets in the three-dimensional (3D) space. In particular, we adopt a general wavefront propagation model by considering the exact spherical wavefront with both channel phase and amplitude variations over different antennas. Besides, we consider the general transmit signal waveforms and also consider the unknown cluttered environments. Under this setup, the unknown parameters to estimate include the 3D coordinates and the complex reflection coefficients of the multiple targets, as well as the noise and interference covariance matrix. Accordingly, we derive the Cram\'er-Rao bound (CRB) for estimating the target coordinates and reflection coefficients. Next, to facilitate practical localization, we propose an efficient estimator based on the 3D approximate cyclic optimization (3D-ACO), which is obtained following the maximum likelihood (ML) criterion. Finally, numerical results show that considering the exact antenna-varying channel amplitudes achieves more accurate CRB as compared to prior works based on constant channel amplitudes across antennas, especially when the targets are close to the transceivers. It is also shown that the proposed estimator achieves localization performance close to the derived CRB, thus validating its superior performance.Comment: 8 pages, 4 figures as an extended version. Its 6 pages version is submitted for conference publicatio

MIMO Integrated Sensing and Communication with Extended Target: CRB-Rate Tradeoff

This paper studies a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) sends unified wireless signals to estimate an extended target and communicate with a multi-antenna communication user (CU) at the same time. We investigate the fundamental tradeoff between the estimation Cram\'er-Rao bound (CRB) for sensing and the data rate for communication, by characterizing the Pareto boundary of the achievable CRB-rate (C-R) region. Towards this end, we formulate a new MIMO rate maximization problem by optimizing the transmit covariance matrix at the BS, subject to a new form of maximum CRB constraint together with a maximum transmit power constraint. We derive the optimal transmit covariance solution in a semi-closed form, by first implementing the singular-value decomposition (SVD) to diagonalize the communication channel and then properly allocating the transmit power over these subchannels for communication and other orthogonal subchannels (if any) for dedicated sensing. It is shown that the optimal transmit covariance is of full rank, which unifies the conventional rate maximization design with water-filling power allocation and the CRB minimization design with isotropic transmission. Numerical results are provided to validate the performance achieved by our proposed optimal design, in comparison with other benchmark schemes

The use of automated Ki67 analysis to predict Oncotype DX risk-of-recurrence categories in early-stage breast cancer

<div><p>Ki67 is a commonly used marker of cancer cell proliferation, and has significant prognostic value in breast cancer. In spite of its clinical importance, assessment of Ki67 remains a challenge, as current manual scoring methods have high inter- and intra-user variability. A major reason for this variability is selection bias, in that different observers will score different regions of the same tumor. Here, we developed an automated Ki67 scoring method that eliminates selection bias, by using whole-slide analysis to identify and score the tumor regions with the highest proliferative rates. The Ki67 indices calculated using this method were highly concordant with manual scoring by a pathologist (Pearson’s r = 0.909) and between users (Pearson’s r = 0.984). We assessed the clinical validity of this method by scoring Ki67 from 328 whole-slide sections of resected early-stage, hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer. All patients had Oncotype DX testing performed (Genomic Health) and available Recurrence Scores. High Ki67 indices correlated significantly with several clinico-pathological correlates, including higher tumor grade (1 versus 3, P<0.001), higher mitotic score (1 versus 3, P<0.001), and lower Allred scores for estrogen and progesterone receptors (P = 0.002, 0.008). High Ki67 indices were also significantly correlated with higher Oncotype DX risk-of-recurrence group (low versus high, P<0.001). Ki67 index was the major contributor to a machine learning model which, when trained solely on clinico-pathological data and Ki67 scores, identified Oncotype DX high- and low-risk patients with 97% accuracy, 98% sensitivity and 80% specificity. Automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy, in a manner that integrates readily into the workflow of a pathology laboratory. Furthermore, automated Ki67 scores contribute significantly to models that predict risk of recurrence in breast cancer.</p></div

Psychological Resilience as a Protective Factor for Depression and Anxiety Among the Public During the Outbreak of COVID-19

Background Psychological resilience may reduce the impact of psychological distress to some extent. We aimed to investigate the mental health status of the public during the outbreak of coronavirus disease 2019 (COVID-19) and explore the level and related factors of anxiety and depression. Methods From February 8 to March 9, 2020, 3,180 public completed the Zung's Self-Rating Anxiety Scale (SAS) for anxiety, Zung's Self-Rating Depression Scale (SDS) for depression, the Connor-Davidson resilience scale (CD-RISC) for psychological resilience, and the Simplified Coping Style Questionnaire (SCSQ) for the attitudes and coping styles. Results The number of people with depressive symptoms (SDS > 53) was 1,303 (the rate was 41.0%). The number of people with anxiety symptoms (SAS > 50) was 1,184 (the rate was 37.2%). The depressed group and anxiety group had less education, more unmarried and younger age, as well as had significant different in SDS total score (P < 0.001), SAS total score (P < 0.001), CD-RISC total score (P < 0.001), and SCSQ score (P < 0.001). The binary logistic regression showed that female (B = -0.261, P = 0.026), strength (B = -0.079, P = 0.000), and the subscales of active coping style in SCSQ (B = -0.983, P = 0.000) remained protective factors and passive coping style (B = 0.293, P = 0.003) and higher SAS score (B = 0.175, P = 0.000) were risk factors for depression. Optimism (B = -0.041, P = 0.015) in CD-RISC was a protective factor, and passive coping styles (B = 0.483, P = 0.000) and higher SDS score (B = 0.134, P = 0.000) were risk factors for anxiety. Limitations This study adopted a cross-sectional design and used self-report questionnaires. Conclusion The mental health of the public, especially females, the younger and less educational populations, and unmarried individuals, should be given more attention. Individuals with high level of mental resilience and active coping styles would have lower levels of anxiety and depression during the outbreak of COVID-19

Outline of Random Forest training and evaluation workflow.

<p>Outline of Random Forest training and evaluation workflow.</p

Summary performance of the Random Forest models predicting Oncotype DX risk groups.

<p>Recurrence Scores were predicted using the clinico-pathological variables listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188983#pone.0188983.s004" target="_blank">S2 Table</a> alone (pRS), or using the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188983#pone.0188983.s004" target="_blank">S2 Table</a> variables in addition to gene expression scores for ER, PgR and HER2 that were included in the official Oncotype DX reports (pRS_odx). Evaluation of performance of the Random Forest models was based on the extent to which the models correctly predicted, or failed to predict, each patient’s actual low- or high-risk Oncotype DX category. Values represent the mean outcomes ± standard deviations over 1,000 testing iterations.</p

Assessment of correlation between hot-spot Ki67 index and Oncotype DX scores.

<p>(A) Plot showing association between Ki67 index and Oncotype DX low-risk (blue), intermediate-risk (green) and high-risk (red) groupings. Pearson’s r = 0.5533; P<0.001. (B) Plot showing association between Ki67 and Oncotype DX low- and high-risk groupings. Pearson’s r = 0.684; P<0.001.</p