Efficient Wideband DoA Estimation with a Robust Iterative Method for Uniform Circular Arrays

Direction-of-arrival (DoA) is a critical parameter in wireless channel estimation. With the ever-increasing requirement of high data rate and ubiquitous devices in wireless communication systems, effective wideband DoA estimation is desirable. In this paper, an iterative coherent signal-subspace method including three main steps in each iteration is proposed for wideband two-dimensional (2D) DoA estimation with a uniform circular array. The first step selects partial frequency points for the subsequent focusing process. The second step performs the focusing process, where the angle intervals are designed to generate focusing matrices with robustness, and the signal-subspaces at the selected frequency points are focused into a reference frequency. The third step estimates DoAs with the multiple signal classification (MUSIC) algorithm, where the range of the MUSIC spatial spectrum is constrained by the aforementioned angle intervals. The key parameters of the proposed method in the current iteration are adjusted based on the estimation results in the previous iterations. Besides, the Cram\'er-Rao bound of the investigated scenario of DoA estimation is derived as a performance benchmark, based on which the guidelines for practical application are provided. The simulation results indicate the proposed method enjoys better estimation performance and preferable efficiency when compared with the benchmark methods

Joint Radio Frequency Fingerprints Identification via Multi-antenna Receiver

In Internet of Things (IoT), radio frequency fingerprints (RFF) technology has been widely used for passive security authentication to identify the special emitter. However, few works took advantage of independent oscillator distortions at the receiver side, and no work has yet considered filtering receiver distortions. In this paper, we investigate the RFF identification (RFFI) involving unknown receiver distortions, where the phase noise caused by each antenna oscillator is independent. Three RFF schemes are proposed according to the number of receiving antennas. When the number is small, the Mutual Information Weighting Scheme (MIWS) is developed by calculating the weighted voting of RFFI result at each antenna; when the number is moderate, the Distortions Filtering Scheme (DFS) is developed by filtering out the channel noise and receiver distortions; when the number is large enough, the Group-Distortions Filtering and Weighting Scheme (GDFWS) is developed, which integrates the advantages of MIWS and DFS. Furthermore, the ability of DFS to filter out the channel noise and receiver distortions is theoretically analyzed at a specific confidence level. Experiments are provided when both channel noise and receiver distortions exist, which verify the effectiveness and robustness of the proposed schemes

Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO

Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency. However, hundreds of antennas require large volumes of pilot overhead to guarantee reliable channel estimation in FDD massive MIMO system. Compressive sensing (CS) has been applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel but suffer from high complexity. To overcome this challenge, this paper develops a hybrid channel estimation scheme by integrating the model-driven CS and data-driven deep unrolling technique. The proposed scheme consists of a coarse estimation part and a fine correction part to respectively exploit the inter- and intraframe sparsities of channels to greatly reduce the pilot overhead. Theoretical result is provided to indicate the convergence of the fine correction and coarse estimation net. Simulation results are provided to verify that our scheme can estimate MIMO channels with low pilot overhead while guaranteeing estimation accuracy with relatively low complexity.Comment: arXiv admin note: substantial text overlap with arXiv:2210.1721

Hierarchical structured graphene/metal oxide/porous carbon composites as anode materials for lithium-ion batteries

This work was financially supported by the Fundamental Research Funds for the Central Universities, and National Natural Science Foundation of China (21101014 and 21273022).As a novel anode material for lithium-ion batteries, CeO2 displays imperceptible volumetric and morphological changes during the lithium insertion and extraction processes, and thereby exhibits good cycling stability. However, the low theoretical capacity and poor electronic conductivity of CeO2 hinder its practical application. In contrast, Co3O4 possesses high theoretical capacity, but undergoes huge volume change during cycling. To overcome these issues, CeO2 and Co3O4 nanoparticles are formed inside the pores of CMK-3 and display various electrochemical behaviors due to the different morphological structures of CeO2 and Co3O4 within CMK-3. Moreover, the graphene/metal oxide/CMK-3 composites with a hierarchical structure are then prepared and exhibit better electrochemical performances than metal oxides with or without CMK-3. This novel synthesis strategy is hopefully employed in the electrode materials design for Li-ion batteries or other energy conversion and storage devices.PostprintPeer reviewe

Compressive Spectrum Sensing Using Sampling-Controlled Block Orthogonal Matching Pursuit

This paper proposes two novel schemes of wideband compressive spectrum sensing (CSS) via block orthogonal matching pursuit (BOMP) algorithm, for achieving high sensing accuracy in real time. These schemes aim to reliably recover the spectrum by adaptively adjusting the number of required measurements without inducing unnecessary sampling redundancy. To this end, the minimum number of required measurements for successful recovery is first derived in terms of its probabilistic lower bound. Then, a CSS scheme is proposed by tightening the derived lower bound, where the key is the design of a nonlinear exponential indicator through a general-purpose sampling-controlled algorithm (SCA). In particular, a sampling-controlled BOMP (SC-BOMP) is developed through a holistic integration of the existing BOMP and the proposed SCA. For fast implementation, a modified version of SC-BOMP is further developed by exploring the block orthogonality in the form of sub-coherence of measurement matrices, which allows more compressive sampling in terms of smaller lower bound of the number of measurements. Such a fast SC-BOMP scheme achieves a desired tradeoff between the complexity and the performance. Simulations demonstrate that the two SC-BOMP schemes outperform the other benchmark algorithms.Comment: 15 figures, accepted by IEEE Transactions on Communication

OTS: A One-shot Learning Approach for Text Spotting in Historical Manuscripts

Historical manuscript processing poses challenges like limited annotated training data and novel class emergence. To address this, we propose a novel One-shot learning-based Text Spotting (OTS) approach that accurately and reliably spots novel characters with just one annotated support sample. Drawing inspiration from cognitive research, we introduce a spatial alignment module that finds, focuses on, and learns the most discriminative spatial regions in the query image based on one support image. Especially, since the low-resource spotting task often faces the problem of example imbalance, we propose a novel loss function called torus loss which can make the embedding space of distance metric more discriminative. Our approach is highly efficient and requires only a few training samples while exhibiting the remarkable ability to handle novel characters, and symbols. To enhance dataset diversity, a new manuscript dataset that contains the ancient Dongba hieroglyphics (DBH) is created. We conduct experiments on publicly available VML-HD, TKH, NC datasets, and the new proposed DBH dataset. The experimental results demonstrate that OTS outperforms the state-of-the-art methods in one-shot text spotting. Overall, our proposed method offers promising applications in the field of text spotting in historical manuscripts

Cognitive Multihop Wireless Sensor Networks over Nakagami-m Fading Channels

This work is supported by the National Science Foundation of China (NSFC) under Grant 61372114, by the National 973 Program of China under Grant 2012CB316005, by the Joint Funds of NSFC-Guangdong under Grant U1035001, and by Beijing Higher Education Young Elite Teacher Project (no. YETP0434)

Direct growth of SnO2 nanocrystallites on electrochemically exfoliated graphene for lithium storage

This work was financially supported by National Natural Science Foundation of China (21573023). WZ thanks EPSRC’s support to the electron microscopy Laboratory for a Capital Equipment Grant EP/L017008/1.As a new generation of high quality graphene, electrochemically exfoliated graphene is an ideal platform for constructing integrated high-performance nanocomposites as advanced electrode materials for energy storage and conversion devices. To take on a challenge of direct growth of nanoparticles on electrochemically exfoliated graphene with limited oxygen-containing functional groups and its hydrophobic nature, a systematic study is carried out on growth of SnO2 nanocrystallites on the surface of electrochemically exfoliated graphene. The results indicate that these nanocrystals can efficiently grow on the functional group-free surface of electrochemically exfoliated graphene, if the precursor molecules can polymerize into larger molecules and aggregate on electrochemically exfoliated graphene followed by decomposition and phase transformation into the final metal oxide nanocrystallites. Some key factors affecting this non-classical crystal growth are investigated. Addition of a small amount of water in a polar aprotic solvent to stimulate polymerization of the precursor molecules and a solvothermal treatment to facilitate decomposition of the disordered aggregates of the polymerized precursors are crucial to the growth of nanocrystals on electrochemically exfoliated graphene. The improved electrical conductivity and structural stability of the hybrids may promote the performance of the materials in various applications, such as exceptional lithium storage capability.PostprintPeer reviewe

Crepe cake structured layered double hydroxide/sulfur/graphene as a positive electrode material for Li-S batteries

This work was financially supported by National Natural Science Foundation of China (Nos. 21975030 and 11674005), and the Ministry of Science and Technology of China (No. 2016YFB0700600 (National Materials Genome Project)).Solving the polysulfide shuttle problem is one of the core challenges for the industrialization of lithium–sulfur batteries. In this work, a triphasic composite of LDH/sulfur/rGO (LDH: layered double hydroxide, rGO: reduced graphene oxide) with a crepe cake like structure is designed and fabricated as a positive electrode material for lithium–sulfur batteries. Sulfur nanoparticles are embedded in the interlayer space of the composite and thus are well protected physically via three-dimensional wrapping and chemically via strong interaction of LDH nanoflakes with lithium polysulfides, such as ionic bonds and S···H hydrogen bonds. In addition, the flexible lamellar structure of the composite with soft graphene layers can tolerate the volume expansion of sulfur during lithiation as well as facilitate ionic permeability and electron transport, which is favorable for the redox reactions of polysulfide. The present work sheds light on the future development and industrialization of lithium–sulfur batteries.PostprintPeer reviewe