Search

Interleaved training has been studied for single-user and multi-user massive MIMO downlink with either fully-digital or hybrid beamforming. However, the impact of channel correlation on its average training overhead is rarely addressed. In this paper, we explore the channel correlation to improve the interleaved training for single-user massive MIMO downlink. For the beam-domain interleaved training, we propose a modified scheme by optimizing the beam training codebook. The basic antenna-domain interleaved training is also improved by dynamically adjusting the training order of the base station (BS) antennas during the training process based on the values of the already trained channels. Exact and simplified approximate expressions of the average training length are derived in closed-form for the basic and modified beam-domain schemes and the basic antenna-domain scheme in correlated channels. For the modified antenna-domain scheme, a deep neural network (DNN)-based approximation is provided for fast performance evaluation. Analytical results and simulations verify the accuracy of our derived training length expressions and explicitly reveal the impact of system parameters on the average training length. In addition, the modified beam/antenna-domain schemes are shown to have a shorter average training length compared to the basic schemes.

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.

In frequency division duplexing (FDD) cell-free massive MIMO, the acquisition of the channel state information (CSI) is very challenging because of the large overhead required for the training and feedback of the downlink channels of multiple cooperating base stations (BSs). In this paper, for systems with partial uplink-downlink channel reciprocity, and a general spatial domain channel model with variations in the average port power and correlation among port coefficients, we propose a joint-port-selection-based CSI acquisition and feedback scheme for the downlink transmission with zero-forcing precoding. The scheme uses an eigenvalue-decomposition-based transformation to reduce the feedback overhead by exploring the port correlation. We derive the sum-rate of the system for any port selection. Based on the sum-rate result, we propose a low-complexity greedy-search-based joint port selection (GS-JPS) algorithm. Moreover, to adapt to fast time-varying scenarios, a supervised deep learning-enhanced joint port selection (DL-JPS) algorithm is proposed. Simulations verify the effectiveness of our proposed schemes and their advantage over existing port-selection channel acquisition schemes.

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.

Pipelines are among the principal means of transporting hydrocarbon fluids and gases, so leakage detection is critical to avoid the loss of these energy resources. In this article, based on studies in field data, we model pipeline leakage as an increase in the mean value of the flow rate difference between the inlet and the outlet sensors, where the increased value is unknown and subject to change. Then, an adaptive filter is proposed based on the naive Bayes classification and the estimated cumulative distribution function (CDF) of the data in steady-state conditions using kernel density estimation. The proposed filter has a better performance in small leaks in comparison with different benchmarks.

This paper is on the beamforming design and performance analysis of the power-domain non-orthogonal multiple access (NOMA) systems. First, a closed-form linear beamforming design is proposed for single-cluster NOMA systems with two users to minimize the required transmit power with users' signal-to-interference-plus-noise-ratios (SINRs) guaranteed. The average power consumption and the power scaling law are derived for the proposed beamforming design. For multi-cluster NOMA, we exploit a two-stage beamforming design where the first stage eliminates the inter-cluster interference via zero-forcing (ZF) and the second stage aims at saving the required transmit power within each cluster by using the proposed single-cluster beamforming design based on the effective channel vectors. Numerical results are provided to show the superiority of NOMA transmissions with our proposed beamforming comparing to other transmission schemes in terms of the required transmit power and the outage performance.

Modern power systems are vulnerable to false data injection attacks (FDIAs) due to the widespread applications of two-way communication networks for system operation and control. Diagnosing such malicious attacks are of great significance for the resilient operations of power systems. Yet, in literature, the majority of studies in FDIA detection focus on power transmission systems. In this paper, a novel FDIA detection scheme is proposed for three-phase distribution systems based on zero-sequence voltage (ZSV). From the voltage and power measurements, the bus voltages are estimated, and then the estimated ZSV is calculated as the sum of the estimated bus voltages on the three phases to represent the degree of unbalance of the distribution system. Via mathematical analysis of the linear distribution system state estimation (DSSE) model, the distribution of the estimated ZSV under the normal condition is derived, based on which a whitening process is adopted on the estimated ZSV to weaken the effect of measurement noises. The $\mathcal {L}_{2}$ -norm of the whitened ZSV vector is then compared with a predefined threshold for FDIA detection. Moreover, the probability of false alarm of the proposed scheme is derived, which can be utilized to determine the detection threshold for a desired tolerance of false alarm rate. The proposed scheme is validated on several IEEE Test Feeders and simulation results show the effectiveness of the proposed scheme in detecting FDIAs in three-phase distribution systems.

The channel state information (CSI) is crucial in communication systems assisted by intelligent reflecting surfaces (IRSs). This paper is on the phase estimation of individual channels in IRS-assisted communication systems with single-antenna transceivers under the correlated Rayleigh fading. We consider both the fully-active-IRS where all IRS elements are active and the hybrid-IRS where partially IRS elements are active, where an active IRS element is equipped with a sensing device for pilot signal reception. We derive the maximum likelihood (ML) estimator for channel phases of all IRS elements based on the observations on active IRS elements. This estimator is also proved to be the minimum mean square error (MMSE), maximum a posterior (MAP) and minimum mean absolute error (MMAE) estimators. Then we conduct performance analysis in terms of the gain and the capacity of the cascaded transmitter-IRS-receiver channel with perfectly known and estimated individual channel phases. Numerical results are provided to show that the performance of IRS-assisted communication systems with our proposed phase estimator is close to that with perfect CSI and validate our theoretical analysis.

This letter proposes a modified non-orthogonal multiple-access (NOMA) scheme for systems with a multi-antenna base station (BS) and two single-antenna users, where NOMA transmissions are conducted only when the absolute correlation coefficient (ACC) between the user channels exceeds a threshold and the BS uses matched-filter (MF) precoding along the user with the stronger average channel gain. We derive the average required transmit power to guarantee the signal-to-interference-plus-noise-ratio (SINR) levels of both users. Our results show that the average required transmit power grows logarithmically in the reciprocal of the ACC threshold and a non-zero threshold is necessary for finite average transmit power. Further, for the massive MIMO scenario, we derive scaling laws of the average transmit power and outage probability with respect to the antenna numbers, as well as their tradeoff law. Simulation results are shown to validate our theoretical results.

This letter is on the multi-user massive multi-input multi-output (MIMO) uplink with the mixed analog-to-digital converter (ADC) architecture and the zero-forcing (ZF) receiver at the base station (BS). Under imperfect channel state information (CSI), a closed-form approximation of the spectral efficiency (SE) is derived for a general resolution profile. Further, simplified SE results are provided for two common asymptotic massive MIMO scenarios. In the SE analysis, to overcome the difficulty induced by the mixed-ADC structure, new techniques are developed in calculating the average noise powers caused by the CSI error and the quantization.

An uplink multi-user massive Multi-input multi-output (MIMO) system is considered with a mixed Analog-to-digital converter (ADC) architecture under imperfect Channel state information (CSI). A closed-form approximation for the Spectral efficiency (SE) is derived for a general mixed ADC structure with any resolution profile. To achieve a balance between the SE and receive Energy efficiency (EE) of the system, the ADC resolution profile optimization problem that maximizes a linear combination of the SE and the Base station (BS) receive power consumption is formulated. An algorithm based on gradient search is proposed whose complexity is linear in the number of BS antennas. Numerical results verify that the proposed ADC resolution design largely outperforms the two-level structure especially in the lower SE region and provides more choices than the uniform-ADC architecture for resolving the SE-EE tradeoff.

NOMA, as the newest member of the multiple access family, is envisioned to be an essential component of 5G mobile networks. The combination of NOMA and multi-antenna MIMO technologies exhibits significant potential in improving spectral efficiency and providing better wireless services to more users. In this article, we introduce the basic concepts of MIMO-NOMA and summarize the key technical problems in MIMO-NOMA systems. Then we explore the problem formulation, beamforming, user clustering, and power allocation of single/multi-cluster MIMO-NOMA in the literature along with their limitations. Furthermore, we point out an important issue of the stability of successive interference cancellation that arises using achievable rates as performance metrics in practical NOMA/MIMO-NOMA systems. Finally, we discuss incorporating NOMA with massive/millimeter- wave MIMO, and identify the main challenges and possible future research directions in this area.

In this work, we study joint user-relay selection and association in multi-user multi-relay cooperative wireless relay uplinks with multi-antenna nodes. For non-regenerative and altruistic relays we propose a low-complexity joint scheme, which simultaneously selects multiple relays and users for cooperation as well as assigns the selected users to different selected relays for service. The proposed scheme is sub-optimal and utilizes only the channel gains between the nodes, which leads to reduced feedback and overhead in comparison to schemes that require full channel knowledge. Furthermore, the complexity of the scheme scales linearly as the product of the total number of relays, the total number of users and the number of selected users. Simulation results demonstrate the superiority of the proposed joint scheme compared to a scheme with neither user-relay selection nor user-relay association and another scheme with user-relay association, but no user-relay selection. The favorable performance and low-complexity of the proposed scheme make it very attractive for possible implementation in emerging broadband wireless relay networks (e.g., LTE-Advanced).