This work is on the beamforming designs for the interference cancellation and mitigation in a system where multiple ground base stations equipped with uniform linear antenna arrays communicate with their associated unmanned aerial vehicle (UAV) users with the same time-frequency resource. Under the line-of-sight (LOS) channel condition, the interference between non-associated pairs of ground base stations and UAVs is a prominent issue that degrades the communication performance. For both the uplink and downlink communications, through identifying the beamforming vectors with polynomials, we derive beamforming solutions in closed-forms that can fully cancel the interference and have the highest SINR under the interference-free condition. The SINR expressions of the proposed interference-free designs are also obtained in closed-form, revealing the effect of the systems parameters and the UAV locations. For the uplink transmissions, the SINR-maximum receive beamforming design is also investigated, where the beamforming solution and the SINR result are obtained in closed-forms. Simulation results are provided for the sum-rate performance of the beamforming designs and for the validation of the theoretical analysis.

In today’s interconnected world, as the scale of data sharing across diverse systems and organizations continues to grow, the secure and verifiable access to these data is of escalating importance. Nonetheless, existing verification methods for prevalent access control models, such as Role-Based Access Control (RBAC), usually face several prevalent challenges, including inadequate support for cross-domain operations, potential systemic risks stemming from potential flaws in policy design and improper implementation in large-scale complex systems, and concerns about modeling credibility. To address the issues, we propose AcVerifier, a novel solution that employs blockchain technology coupled with deterministic finite automaton (DFA) to enable verifiable enforcement of access control policies across disparate domains. AcVerifier uploads data indices and policies to a secure authorization server, which records operation logs in blockchain-connected data containers, thereby ensuring data credibility. AcVerifier employs a hybrid verification method combining static and dynamic analysis techniques, leveraging their respective strengths for preemptive auditing and real-time monitoring. Thus, AcVerifier can verify the correctness and consistency between the permissions granted by the extended access control policies and their actual execution in cross-domain data sharing and ubiquitous circulation scenarios. Here, the permissions encompass desensitization, access, modification and forwarding of personal information. Comprehensive evaluation demonstrates the correctness and efficiency of AcVerifier in addressing the challenges of cross-domain access control verification.

In this paper we derive the Fisher information matrix (FIM) and Cramér-Rao lower bound (CRLB) for positioning a terminal with a planar large intelligent surface (LIS), under Rician channel. For a disk-shaped continuous LIS and a terminal located on the central perpendicular line (CPL) of the LIS, we obtain expressions for the CRLBs in the form of a single integration. For the situation that the terminal is far from the LIS compared to the LIS radius and the situation with an asymptotically large LIS, closed-form approximations of the CRLBs are derived, based on which scalings and properties of the positioning precision with respect to different system parameters are obtained. For positioning a terminal with arbitrary location, we derive closed-form expressions of the CRLBs when the terminal is far from the LIS and the wavelength is small. When the surface area is small and the path-loss exponent is not larger than 5, the CRLBs of a CPL-terminal are smaller than those of a non-CPL terminal for all three dimensions, while the reverse may occur when the surface area grows large enough. We also carry out a comparative study of the continuous and discrete models of the LIS. Numerical results are presented to validate the precision of our theoretical analysis and approximated performance.

This paper presents split attenuators combined with adjustable gain amplifiers as a two-stage time-gain compensation (TGC), such that it can extend the gain range of the Analog Front-end (AFE) for ultrasound image systems. To avoid artifacts caused by discrete gain control, continuous dB-linear gain varying with time is achieved by the split two-stage resistive voltage-divider attenuator whose attenuation value is decided by the MOSFET resistance which is inversely proportional to area. Rigorous theoretical analysis proves that placing the attenuators in the front and back stages of a programmable-gain amplifier (PGA) in the AFE as the proposed architecture demonstrated solves the contradiction between extending the gain range and saving area. The total attenuation range is broken down into two stages so that the system noise performance is optimized and unlike the prior single-stage-attenuator work, the dB-linearity is free from the negative impact of parasitic resistance introduced by area expansion. The proposed AFE has been fabricated by a 28-nm CMOS process, occupying 0.145 mm2 active area and consuming 75mW from a 2.5 V supply. It achieves a 74.7 dB adjustable gain range while providing a 15 MHz/30 MHz switchable bandwidth for all gain modes. The lowest input-referred noise (IRN) of the system was measured as 2.38 nV/$\sqrt {\mathrm {Hz}}$at 5MHz with the TGC reaching the highest gain.

We propose an interleaved training design for multi-user massive MIMO downlink and study the performance thereof with the maximum-ratio transmission (MRT) precoding. In our proposed design, the channels are trained one BS antenna at a time and each training step is interleaved with the channel state information (CSI) feedback. The decision to continue training depends on whether or not the signal-to-interference-plus-noise (SINR) requirements of all users are satisfied with currently available instantaneous CSI. For the MRT precoding, we analyze the system performance in terms of the training length and the transmission success rate. Our simulation results show that the proposed training scheme has a significant performance advantage over existing full training scheme and fixed-length partial training scheme.

An interleaved training scheme is proposed for multi-user massive multi-input-multi-output (MIMO) downlink with maximum-ratio-transmission (MRT). The base station (BS) sends pilots to train the channels antenna-by-antenna and the training steps are interleaved with the feedback of the channel state information (CSI) from the users. For each training step of the interleaved scheme, the BS decides whether to continue or to stop the training process based on the quality-of-service (QoS) provided by the available CSI. The training time and the transmission success rate of the proposed scheme are analyzed with closed-form approximations derived. Simulations show that the proposed scheme can largely save the average training time without sacrificing the QoS of users. The analytical results are also verified via simulation.

This paper proposes a new scheme for distributed spectrum sensing in the blind scenario, wherein the channel gains, the signal power, and the noise power are unknown. By utilizing energy detection and homogeneity test concepts, the cognitive radios (CRs) make binary local decisions based on whether a CR has the maximum sample energy within a sampling window among all CRs. With independent channels, the use of this maximum energy indicator generates a desirable discrepancy among the CR decisions when the frequency band is in use. Closed-form results on the distribution of the CR decisions are obtained for Gaussian noises and signal. Asymptotic analytical expressions are derived on the detection performance of the proposed scheme. Simulation results show the advantage of the proposed scheme and validate the analysis.

This paper proposes a new scheme for blind distributed spectrum sensing (DSS), where multiple distributed cognitive radios (CRs) and a fusion center (FC) collaboratively detect the availability of a frequency band of interest under communications constraints without knowledge on the channels, signal power, or noise power. By following the energy detection and homogeneity test concepts, in the proposed scheme, the CRs make local binary decisions based on relative comparison of the total energy in a sampling window, particularly, whether a CR has the maximum total energy. With independent channels, the use of the maximum function generates desirable discrepancy among the CR decisions when the frequency band is in use. The introduction of the window size enables the balancing between communications costs and the performance. Closed-form results on the distribution of the CR decisions are obtained for Gaussian noises and signals. Further, asymptotic analytical expressions are derived on the detection performance of the proposed scheme for both the generalized likelihood ratio test (GLRT) and$\chi ^{2}$-test at the FC. Simulation results are shown which validate the theoretical results and show the advantage of the proposed scheme to others.

Liwei Song, Xinwei Yu, Hsuan-Tung Peng, Karthik Narasimhan. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Channel estimation schemes based on SVD (singular value decomposition) are proposed for reduced-rank multi-input-multi-output (MIMO) systems, where instead of estimating each entry of the channel matrix, the singular spaces and singular values are estimated. When the channel rank is fixed and known, the maximum-likelihood (ML) estimator is derived. When the channel rank is random and unknown, a threshold-based rank detection algorithm using the singular values is adopted. In finding the threshold, a lower bound on the correct detection probability is derived and the threshold is chosen to maximize the lower bound. Simulations show that the SVD-based estimation achieves lower MSE and higher capacity than the entry-based estimation for both cases.

This letter considers the binary threshold system (TS) based detector for a general binary testing problem. First, the optimal binary TS that maximizes the area under the ROC curve (AUC), where ROC stands for the receiver operating characteristic, is derived. Then the noise-enhanced effect is investigated. The optimal noise that can achieve the maximum AUC is derived and shown to be deterministic. An example is shown to help justify the derived results.

For a general multi-input-multi-output (MIMO) relay network, an estimation method for the receiver to obtain the end-to-end channels is proposed. Instead of straightforwardly estimating entries of the end-to-end channel matrix, the proposed scheme takes into consideration the special structure of the end-to-end channel matrix. By parameterizing the channel matrix with its singular values and singular vectors using singular value decomposition (SVD), the proposed scheme estimates the singular values and left and right singular vectors, which are then combined to form an estimation of the overall channel matrix. The proposed estimation follows the maximum-likelihood (ML) estimation method. Simulations on the mean square error (MSE) of the channel estimation are presented, which show the advantage of the proposed scheme over straightforward estimation of the channel entries for networks whose transmitter and receiver are equipped with multiple antennas.

This paper investigates the channel estimations in a relay network with multiple transmit and receive antennas, including the estimation of the end-to-end channel matrix and the individual estimation of the transmitter-relay channels and the relay-receiver channels. For the end-to-end channel estimation, instead of directly estimating entries of the channel matrix, we use singular value decomposition (SVD) and estimate its largest singular value and singular vectors, which are then combined to form an estimation of the channel matrix. An approximate maximum-likelihood (ML) estimation is proposed, which is shown to become the exact ML estimation when the time duration of each training step equals the number of antennas at the transmitter. Simulation on the mean square error (MSE) shows that the SVD-based approximate ML estimation performs about the same as the exact ML estimation and is superior to entry-based estimations. For the individual channel estimation, we decompose each channel vector into the product of its length and direction, and find the ML estimation of each. By using an approximation on the probability density function (PDF) of the observations during training, an analytical ML estimation is derived. The ML estimation with the exact PDF is also investigated and a solution is obtained numerically. Simulation on the MSE shows that the two have similar performance. Compared with cascade channel estimations, its performance is superior for the relay-receiver channel estimation and comparable for the transmitter-relay channel estimation. Extension to the general multiple-antenna multiple-relay network is also provided.