409 research outputs found
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
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