Search

In this paper, beam training and beam tracking are investigated for extremely large-scale multiple-input-multiple-output communication systems with partially-connected hybrid combining structures. Firstly, we propose a two-stage hybrid-field beam training scheme for both the near field and the far field. In the first stage, each subarray independently uses multiple far-field channel steering vectors to approximate near-field ones for analog combining. To find the codeword best fitting for the channel, digital combiners in the second stage are designed to combine the outputs of the analog combiners from the first stage. Then, based on the principle of stationary phase and the time-frequency duality, the expressions of subarray signals after analog combining are analytically derived and a beam refinement based on phase shifts of subarrays (BRPSS) scheme with closed-form solutions is proposed for high-resolution channel parameter estimation. Moreover, a low-complexity near-field beam tracking scheme is developed, where the kinematic model is adopted to characterize the channel variations and the extended Kalman filter is exploited for beam tracking. Simulation results verify the effectiveness of the proposed schemes.

Multiuser beamforming is considered for partially-connected millimeter wave massive MIMO systems. Based on perfect channel state information (CSI), a low-complexity hybrid beamforming scheme that decouples the analog beamformer and the digital beamformer is proposed to maximize the sum-rate. The analog beamformer design is modeled as a phase alignment problem to harvest the array gain. Given the analog beamformer, the digital beamformer is designed by solving a weighted minimum mean squared error problem. Then based on imperfect CSI, an analog-only beamformer design scheme is proposed, where the design problem aims at maximizing the desired signal power on the current user and minimizing the power on the other users to mitigate the multiuser interference. The original problem is then transformed into a series of independent beam nulling subproblems, where an efficient iterative algorithm using the majorization–minimization framework is proposed to solve the subproblems. Simulation results show that, under perfect CSI, the proposed scheme achieves almost the same sum-rate performance as the existing schemes but with lower computational complexity; and under imperfect CSI, the proposed analog-only beamforming design scheme can effectively mitigate the multiuser interference.

For reconfigurable intelligent surface (RIS)-assisted mmWave massive MIMO, we propose a fast multibeam training (FMT) scheme with two stages. In the first stage, we find a multibeam together with the RIS reflection coefficients best matching the LoS links or the cascaded LoS links, where all the RISs use the same reflection coefficients at the same time to reduce the overhead of beam training. In the second stage, we find a DFT codeword best matching the LoS links or the cascaded LoS links for the BS instead of the multibeam. Different from the FMT scheme that uses a single-layer DFT codebook, in the proposed fast hierarchical multibeam training (FHMT) scheme we use a multi-layer hierarchical codebook, where in each layer only two wide beams are used to cover the angle space of interest, and the wide beams are narrowed down from top layer to bottom layer. Simulation results show that significantly smaller training overhead can be achieved by the FMT and FHMT schemes than the baseline scheme with slightly worse performance.

Non-terrestrial networks (NTNs) have become appealing resolutions for seamless coverage in the next-generation wireless transmission, where a large number of Internet of Things (IoT) devices diversely distributed can be efficiently served. The explosively growing number of IoT devices brings a new challenge for massive connection. The long-distance wireless signal propagation in NTNs leads to severe path loss and large latency, where the accurate acquisition of channel state information (CSI) is another challenge, especially for fast-moving non-terrestrial base stations (NTBSs). Moreover, the scarcity of on-board resources of NTBSs is also a challenge for resource allocation. To this end, we investigate three key issues, where the existing schemes and emerging resolutions for these three key issues have been comprehensively presented. The first issue is to enable the massive connection by designing random access to establish the wireless link and multiple access to transmit data streams. The second issue is to accurately acquire CSI in various channel conditions by channel estimation and beam training, where orthogonal time frequency space modulation and dynamic codebooks are on focus. The third issue is to efficiently allocate the wireless resources, including power allocation, spectrum sharing, beam hopping, and beamforming. At the end of this article, some future research topics are identified.

In this letter, a beam tracking scheme based on variable step beam (VSB) is proposed for millimeter wave (mmWave) massive MIMO. Firstly, a design method for VSB is proposed, where a small portion of the beam energy is taken to track the movement of user equipment (UE) and the large portion of the beam energy is used to keep the quality of current mmWave communication link. Then based on the beam sweeping using VSBs, the angle-of-departure (AoD) of the UE is estimated by iteratively updating the posterior probability together with the range prediction of the fading coefficient of the mmWave channel, where the mainlobe of the VSBs is iteratively updated by Gaussian process regression to cover the movement of the UE. Simulation results verify the effectiveness of the proposed scheme and show that it has larger achievable rate than the existing schemes.

We propose a blockage prediction and fast base station (BS) handover (BP-FBSH) scheme based on the reference signal received power (RSRP) of the mobile terminal (MT) and the indices of the BS transmit beams for millimeter wave communications. Using a specific beam tracking method called neighborhood beam search, the BS transmits multiple neighborhood beams to the MT and collects the RSRPs of these beams from the MT. Then, the BP-FBSH scheme uses the beam-associated information sequences composed of the RSRPs and the indices of the BS transmit beams to train a long short-term memory (LSTM)-based blockage prediction neural network (BPNN). If the BPNN predicts the MT is to be blocked, the scheme triggers a handover of this MT to an adjacent BS as well as determining an initial access (IA) beam for it by an LSTM-based BS handover neural network. Simulation results based on Wireless Insite software show that the scheme can achieve high success rate for both the blockage prediction and the IA beam prediction.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is considered as a key technology for future wireless communications. For near-field beam training in XL-MIMO, the distance information is another important factor aside of the angular information. The most recent work trains the deep neural network (DNN) by performing beam training based on far-field codebook that only contains angular information, where the optimal angle and distance are independently predicted. In this letter, the DNN is trained by performing beam training based on near-field codebook that contains both angular and distance information, where the optimal angle and distance are jointly predicted with improved performance. We first propose a deep learning-based near-field beam training (DNBT) scheme. To further improve the beam training performance, a DNBT with supplementary codewords (DNBT-SC) scheme is proposed, where the supplementary codewords are selected to perform beam training based on the probability vector acquired in the DNBT scheme. Simulation results show that under the same training overhead, the proposed schemes can achieve better performance than the existing schemes.

At present, the distribution network fault self-healing method based on deep learning in smart grid work often has problems such as low accuracy and insufficient feature extraction ability. To overcome this, the authors propose a method of fault self-healing in a distribution network based on robot patrol and deep learning in a cloud edge architecture. Firstly, the data collected by the robot fault collection system is preprocessed by using one-hot coding and normalization methods to prevent data flooding. Secondly, they propose an improved bi-directional short-term memory (BiLSTM) fault location method which combines the advantages of both BiLSTM and attention mechanism, adjusts attention weight, filters, or weakens redundant information. Finally, the I-BiLSTM network and the U-BiLSTM network are trained, respectively, and the fault section can be accurately located based on the data of each node of the robot fault collection system topology. Experimental results show that this method has achieved accuracy scores of 0.928, 0.933, 0.948, and 0.942, respectively, in four fault types, namely single-phase grounding, two-phase grounding, phase-to-phase short circuit, and three-phase short circuit, which outperform those in previous literature. The proposed method is well suited for applications in smart grid work because of its desirable fault self-healing ability.

The combination of millimeter wave (mmWave) and covert communications can ensure high security for some specific circumstances. Instead of considering the covertness only in the data transmission (DT) phase for mmWave massive MIMO systems, the covertness in both the beam alignment (BA) phase and the DT phase is investigated. As a common beam training method for mmWave massive multiple-input multiple-output (MIMO), beam sweeping is adopted to obtain the optimal beam pair in the BA phase. Aiming at maximizing the average effective achievable covert rate in the DT phase, a two-stage power optimization scheme is proposed subject to the covertness and power constraints in both the BA and the DT phases. Specifically, by introducing a covertness allocation factor, closed-form expressions of the transmit power in both phases are derived according to the likelihood function of the received signal. Simulation results demonstrate that the average covert rate of MIMO is larger than that of multiple-input single-output (MISO) although the BA success rate of the former is lower than that of the latter.

The numbers of beam positions (BPs) and time slots for beam hopping (BH) dominate the latency of LEO satellite communications. Aiming at minimizing the number of BPs subject to a predefined requirement on the radius of BP, a low-complexity user density-based BP design scheme is proposed, where the original problem is decomposed into two subproblems, with the first one to find the sparsest user and the second one to determine the corresponding best BP. In particular, for the second subproblem, a user selection and smallest BP radius algorithm is proposed, where the nearby users are sequentially selected until the constraint of the given BP radius is no longer satisfied. These two subproblems are iteratively solved until all the users are selected. To further reduce the BP radius, a duplicated user removal algorithm is proposed to decrease the number of the users covered by two or more BPs. Aiming at minimizing the number of time slots subject to the no co-channel interference (CCI) constraint and the traffic demand constraint, a low-complexity CCI-free BH design scheme is proposed, where the BPs having difficulty in satisfying the constraints are considered to be illuminated in priory. Simulation results verify the effectiveness of the proposed schemes.

This letter investigates beam alignment for multi-user millimeter wave (mmWave) massive multi-input multi-output system. Unlike the existing works using machine learning (ML), an alignment method with partial beams using ML (AMPBML) is proposed without any prior knowledge such as user location information. The neural network (NN) for the AMPBML is trained offline using simulated environments according to the mmWave channel model and is then deployed online to predict the beam distribution vector using partial beams. Afterwards, the beams for all users are all aligned simultaneously based on the indices of the dominant entries of the obtained beam distribution vector. Simulation results demonstrate that the AMPBML outperforms the existing methods, including the adaptive compressed sensing, hierarchical search, and multi-path decomposition and recovery, in terms of the total training time slots and the spectral efficiency.

Aiming at maximizing the energy efficiency (EE) of multicast multibeam satellite communications, we consider the precoding design under the total power and quality of service (QoS) constraints. Since the original EE maximization problem is nonconvex, it is sequentially converted into a concave-convex fractional programming problem by introducing some variables and using the first-order Taylor low bound approximation. Based on the Charnes-Cooper transformation, it is further converted into a convex problem. Then an iterative algorithm is presented to design the energy efficient precoding. To find feasible initialization points for the algorithm and ensure its convergence, another convex optimization problem with some nonnegative slack variables and a positive penalty parameter is iteratively solved. In particular, the algorithm is verified by the measured channel data of multibeam satellite communications.

Sparse channel estimation is investigated for millimeter wave massive MIMO systems, where a base station equipped with a uniform planar array serves several single-antenna users. At first, the 2-D multiuser channel estimation is formulated as several sparse recovery problems. Then a regularized multipath matching pursuit (RMMP) algorithm is proposed for sparse channel estimation. Compared to the existing multipath matching pursuit (MMP) algorithm, a regularization step is introduced in RMMP to screen the candidate paths, which can reduce the computational complexity as well as the storage overhead. Simulation results show that the proposed RMMP algorithm outperforms the existing orthogonal matching pursuit and orthogonal least squares algorithms. In particular, RMMP has the same sparse channel estimation performance as MMP while the computational complexity of the former is much lower than the latter.

Given the efficient reaction of P-containing species with radionuclide U(VI), phosphate-modified carbon nitrides (GPCNs) were prepared with enhanced surface areas (16.9–102.0 m2/g) and abundant phosphate groups (15.8–9.1 at% of P) and applied to capture aquatic U(VI). GPCNs presented a fast equilibrium of 8 h, efficient capture capability (maximum 333.5 mg/g at pH 5.0 and T 278 K), excellent selectivity and favorable reusability. The enrichment of U(VI) from the artificial nuclide wastewater (101.5 mg/g) and seawater (89.3 mg/g) were evaluated. Spectroscopic studies proved that the formation of hydrogen uranite was dominant for the high-efficiency selective sorption of U(VI). Under the illumination of visible-light, GPCNs showed a ∼4.9-fold faster kinetics for the photocatalytic reduction of U(VI) than CNs. Moreover, the origin of the photocatalytic activity of GPCNs including the role of phosphate modification was also discussed in detail. This study provided a valuable reference for the facile preparation of porous phosphate-modified carbon nitrides as well as a meaningful option for the extraction of radionuclides from aqueous solutions.

The spectrum resource in the air becomes more occupied, so it is necessary to increase spectrum efficiency with our purpose to relax more resource. Traditionally, we increase the data rate in the unit frequency band through higher order modulation, orthogonal frequency division multiplexing (OFDM) and pulse shaping. However, another way for improving spectrum efficiency is to reduce the transmitting bandwidth required for unit data rate. From this point of view, the ultra narrow band (UNB) modulation was proposed. UNB modulation is a sort of transmission technique with efficient bandwidth and high data rate. This patent review will highlight recent patents on UNB modulations, and briefly explain their theoretic foundation and key technology.

The accuracy of channel state information (CSI) acquisition directly affects the performance of millimeter wave (mmWave) communications. In this article, we provide an overview on CSI acquisition, including beam training and channel estimation for mmWave massive multiple-input multiple-output systems. The beam training can avoid the estimation of a high-dimension channel matrix, while the channel estimation can flexibly exploit advanced signal processing techniques. In addition to introducing the traditional and machine learning-based approaches in this article, we also compare different approaches in terms of spectral efficiency, computational complexity, and overhead.