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Beamspace millimeter-wave (mmWave) and terahertz (THz) massive MIMO constitute attractive schemes for next-generation communications, given their abundant bandwidth and high throughput. However, their user and beam selection problem has to be efficiently addressed. Inspired by this challenge, we develop low-complexity solutions explicitly. We introduce the dirty paper coding (DPC) into the joint user and beam selection problem. We unveil the compelling properties of the DPC sum rate in beamspace massive MIMO, showing its monotonic evolution against the number of users and beams selected. We then exploit its beneficial properties for substantially simplifying the joint user and beam selection problem. Furthermore, we develop a set of algorithms striking unique trade-offs for solving the simplified problem, facilitating simultaneous user and beam selection based on partial beamspace channels for the first time. Additionally, we derive the sum rate bound of the algorithms and analyze their complexity. Our simulation results validate the effectiveness of the proposed design and analysis, confirming their superiority over prior solutions., Comment: 12 pages, 8 figures; to appear in IEEE Transactions on Communications

The joint communication and sensing (JCAS) technique has drawn great attention due to its high spectrum efficiency by using the same transmit signal for both communication and sensing. Exploiting the correlation between the uplink (UL) channel and the sensing results, we propose a sensing-aided Kalman filter (SAKF)-based channel state information (CSI) estimation method for UL JCAS, which exploits the angle-of-arrival (AoA) estimation to improve the CSI estimation accuracy. A Kalman filter (KF)-based CSI enhancement method is proposed to refine the least-square CSI estimation by exploiting the estimated AoA as the prior information. Simulation results show that the bit error rates (BER) of UL communication using the proposed SAKF-based CSI estimation method approach those using the minimum mean square error (MMSE) method, while at significantly reduced complexity., 4 pages, 5 figures, IEEE letter

Deep learning has demonstrated its great potential in channel state information (CSI)-based human activity recognition (HAR), and hence has attracted increasing attention in both the industry and academic communities. While promising, most existing high-accuracy methodologies require to retrain their models when applying the previous-trained ones to a new/unseen environment. This issue has limited their practical usabilities. In order to overcome this challenge, this article proposes an innovative scheme, which combines an activity-related feature extraction and enhancement (AFEE) method and matching network (AFEE-MatNet). The proposed scheme is 'one-fits-all,' meaning that the trained model can be directly applied in new/unseen environments without any retraining. We introduce the AFEE method to enhance CSI quality by eliminating noise. Specifically, the approach mitigates environmental noises unrelated to activity while better compressing and preserving the behavior-related information. Moreover, the size of feature signals generated by AFEE are reduced, which in turn significantly shortens the training time. For effective feature extraction, we propose to use the MatNet architecture to learn transferable features shared among source environments. To further improve the recognition performance, we introduce a prediction checking and correction scheme to rectify some classification errors that do not abide by the state transition of human behaviors. Extensive experimental results demonstrate that our proposed AFEE-MatNet significantly outperforms existing state-of-the-art HAR methods, in terms of both recognition accuracy and training time.

Estimating the Doppler frequency is an important part of sensing moving targets in integrated sensing and communications (ISAC) systems, such as human tracking and activity recognition. However, it can be highly challenging when there is clock asynchronism between the transmitter (Tx) and the receiver (Rx), in bistatic setups that are common nowadays. In this article, we propose three algorithms for Doppler frequency estimation based on the ratio of channel state information (CSI). These algorithms explore different properties of the CSI ratio, including the circle-preserving property of the Mobius transform, the periodicity of the CSI ratio, and the difference (or correlation) between segments of CSI-ratio signals. Experimental results demonstrate that the proposed algorithms can estimate Doppler frequency accurately, outperforming the commonly used approach based on cross-antenna cross correlation (CACC).

Current methods for quantitative assessment of cerebral small vessel disease (CSVD) ignore critical aspects of the disease, namely lesion type and regionality. We developed and tested a new scoring system for CSVD, "regional Cerebral Small Vessel Disease" (rCSVD) based on regional assessment of magnetic resonance imaging (MRI) features.141 patients were retrospectively included with a derivation cohort of 46 consecutive brain MRI exams and a validation cohort of 95 patients with known cerebrovascular disease. We compared the predictive value of rCSVD against existing scoring methods. We determined the predictive value of rCSVD score for all-cause mortality and recurrent strokes.46 (44 male) veteran patients (age: 66-93 years), were included for derivation of the rCSVD score. A non-overlapping validation cohort consisted of 95 patients (89 male; age: 34-91 years) with known cerebrovascular disease were enrolled. Based on ROC analysis with comparison of AUC (Area Under the Curve), "rCSVD" score performed better compared to "total SVD score" and Fazekas score for predicting all-cause mortality (0.75 vs 0.68 vs 0.69; p = 0.046). "rCSVD" and total SVD scores were predictive of recurrent strokes in our validation cohort (p-values 0.004 and 0.001). At a median of 5.1 years (range 2-17 years) follow-up, Kaplan-Meier survival analysis demonstrated an rCSVD score of 2 to be a significant predictor of all-cause-mortality."rCSVD" score can be derived from routine brain MRI, has value in risk stratification of patients at risk of CSVD, and has potential in clinical trials once fully validated in a larger patient cohort.

Beamforming design is an important technique for enhancing the performance of integrated sensing and communication (ISAC) systems. However, related research based on the hybrid analog-digital (HAD) architecture is still limited. In this paper, we investigate the partially-connected hybrid beamforming design for multi-user ISAC systems. Instead of the commonly used beampattern related metric, the Cramér-Rao bound (CRB) is employed as the sensing performance metric for direction of arrival (DOA) estimation. We aim to minimize the CRB while satisfying the signal-to-interference-plus-noise ratio (SINR) constraints for individual communication users by jointly optimizing the digital and analog beamformers. Subsequently, we propose an alternating optimization based framework, which is significantly different from the conventional methods based on the approximation of the optimal fully-digital beamformer with a hybrid one. We also consider an alternative formulation of optimizing the SINR of radar echo signals. Based on optimal receive beamformer design, we transform the SINR based joint transmitter and receiver optimization problem to a series of problems sharing a similar form with the CRB based transmitter optimization problem, which can be efficiently solved via the proposed algorithm. Simulation results show that the proposed designs provide significant performance gains in DOA estimation over the existing beampattern approximation based design.

Angle-of-Arrival (AoA) estimation is an important problem in passive radio-frequency identification (RFID) systems. Affixing an RFID tag array to an object enables to acquire its orientation information. However, the electromagnetic interaction between the tags can induce mutual coupling interference, distorting the RFID fingerprint measurements used for AoA estimation. Moreover, RFID reader modes with radio-frequency (RF) noise-tolerant Miller encoding can induce π-radians phase jump. In this article, we propose a scheme called RF-Mirror that can resolve the mutual coupling and phase jump problems and achieve accurate AoA estimation for an array with two or more tags. First, we characterize the impact of mutual coupling on a tag's signal fingerprint and develop novel RSSI/phase-distance models. We then develop new experimental methods and signal processing techniques to verify the effectiveness of the proposed models. Based on the validated models, we develop new AoA estimation algorithms for tag arrays that deal with the mutual coupling effect explicitly. We provide extensive experimental results, which demonstrate that RF-Mirror can achieve significantly improved performance compared to baseline schemes, with median AoA estimation errors of 11.65° and 6.29° for two- and four-tag arrays, respectively.

As a special case of the Volterra system, the second-order Volterra (SOV) filter is very efficient for nonlinear system identification. The improved correntorpy based on the generalized Gaussian density function has been proven robust against impulsive noise. In this brief, we propose several SOV filters based on a recursive maximum correntropy (RMC) algorithm for nonlinear system identification. We first introduce a basic RMC algorithm, which faces a trade-off between filtering accuracy and tracking capability due to the use of a fixed forgetting factor (FFF). Two RMCs with variable FF (VFF) are further proposed to enhance the tracking ability. Simulation results demonstrate that our proposed algorithms outperform existing ones in impulsive noise environments and/or in time-varying systems.