108 research outputs found
Preparation and Characterization of Self-Assembled Nanoparticles of Hyaluronic Acid-Deoxycholic Acid Conjugates
Novel amphiphilic biopolymers were synthesized using hyaluronic acid (HA) as a hydrophilic segment and deoxycholic acid (DOCA) as a hydrophobic segment by a 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide mediated coupling reaction. The structural characteristics of the HA-DOCA conjugates were investigated using H1 NMR. Self-assembled nanoparticles were prepared based on HA-DOCA conjugates, and its characteristics were investigated using dynamic laser light scattering, transmission electron microscopy (TEM), and fluorescence spectroscopy. The mean diameter was about 293.5 nm with unimodal size distribution in distilled water. The TEM images revealed that the shape of HA-DOCA self-aggregates was spherical. The critical aggregation concentration (CAC) was in the range of 0.025–0.056 mg/mL. The partition equilibrium constant (Kv) of pyrene in self-aggregates solution was from 1.45×104 to 3.64×104. The aggregation number of DOCA groups per hydrophobic microdomain, estimated by the fluorescence quenching method using cetylpyridinium chloride, increased with increasing degree of substitution
Deep Learning-empowered Predictive Precoder Design for OTFS Transmission in URLLC
To guarantee excellent reliability performance in ultra-reliable low-latency
communications (URLLC), pragmatic precoder design is an effective approach.
However, an efficient precoder design highly depends on the accurate
instantaneous channel state information at the transmitter (ICSIT), which
however, is not always available in practice. To overcome this problem, in this
paper, we focus on the orthogonal time frequency space (OTFS)-based URLLC
system and adopt a deep learning (DL) approach to directly predict the precoder
for the next time frame to minimize the frame error rate (FER) via implicitly
exploiting the features from estimated historical channels in the delay-Doppler
domain. By doing this, we can guarantee the system reliability even without the
knowledge of ICSIT. To this end, a general precoder design problem is
formulated where a closed-form theoretical FER expression is specifically
derived to characterize the system reliability. Then, a delay-Doppler domain
channels-aware convolutional long short-term memory (CLSTM) network (DDCL-Net)
is proposed for predictive precoder design. In particular, both the
convolutional neural network and LSTM modules are adopted in the proposed
neural network to exploit the spatial-temporal features of wireless channels
for improving the learning performance. Finally, simulation results
demonstrated that the FER performance of the proposed method approaches that of
the perfect ICSI-aided scheme.Comment: 8 pages, 6 figure
Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach
This paper investigates the orthogonal time frequency space (OTFS)
transmission for enabling ultra-reliable low-latency communications (URLLC). To
guarantee excellent reliability performance, pragmatic precoder design is an
effective and indispensable solution. However, the design requires accurate
instantaneous channel state information at the transmitter (ICSIT) which is not
always available in practice. Motivated by this, we adopt a deep learning (DL)
approach to exploit implicit features from estimated historical delay-Doppler
domain channels (DDCs) to directly predict the precoder to be adopted in the
next time frame for minimizing the frame error rate (FER), that can further
improve the system reliability without the acquisition of ICSIT. To this end,
we first establish a predictive transmission protocol and formulate a general
problem for the precoder design where a closed-form theoretical FER expression
is derived serving as the objective function to characterize the system
reliability. Then, we propose a DL-based predictive precoder design framework
which exploits an unsupervised learning mechanism to improve the practicability
of the proposed scheme. As a realization of the proposed framework, we design a
DDCs-aware convolutional long short-term memory (CLSTM) network for the
precoder design, where both the convolutional neural network and LSTM modules
are adopted to facilitate the spatial-temporal feature extraction from the
estimated historical DDCs to further enhance the precoder performance.
Simulation results demonstrate that the proposed scheme facilitates a flexible
reliability-latency tradeoff and achieves an excellent FER performance that
approaches the lower bound obtained by a genie-aided benchmark requiring
perfect ICSI at both the transmitter and receiver.Comment: 31 pages, 12 figure
Scalable Predictive Beamforming for IRS-Assisted Multi-User Communications: A Deep Learning Approach
Beamforming design for intelligent reflecting surface (IRS)-assisted
multi-user communication (IRS-MUC) systems critically depends on the
acquisition of accurate channel state information (CSI). However, channel
estimation (CE) in IRS-MUC systems causes a large signaling overhead for
training due to the large number of IRS elements. In this paper, taking into
account user mobility, we adopt a deep learning (DL) approach to implicitly
learn the historical line-of-sight (LoS) channel features and predict the IRS
phase shifts to be adopted for the next time slot for maximization of the
weighted sum-rate (WSR) of the IRS-MUC system. With the proposed predictive
approach, we can avoid full-scale CSI estimation and facilitate low-dimensional
CE for transmit beamforming design such that the signaling overhead is reduced
by a scale of , where is the number of IRS elements. To this
end, we first develop a universal DL-based predictive beamforming (DLPB)
framework featuring a two-stage predictive-instantaneous beamforming mechanism.
As a realization of the developed framework, a location-aware convolutional
long short-term memory (CLSTM) graph neural network (GNN) is developed to
facilitate effective predictive beamforming at the IRS, where a CLSTM module is
first adopted to exploit the spatial and temporal features of the considered
channels and a GNN is then applied to empower the designed neural network with
high scalability and generalizability. Furthermore, in the second stage, based
on the predicted IRS phase shifts, an instantaneous CSI-aware fully-connected
neural network is designed to optimize the transmit beamforming at the access
point. Simulation results demonstrate that the proposed framework not only
achieves a better WSR performance and requires a lower CE overhead compared
with state-of-the-art benchmarks, but also is highly scalable in the numbers of
users.Comment: 30 pages, 14 figures, journal pape
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