70 research outputs found
Green Beamforming Design for Integrated Sensing and Communication Systems: A Practical Approach Using Beam-Matching Error Metrics
In this paper, we propose a green beamforming design for the integrated
sensing and communication (ISAC) system, using beam-matching error to assess
radar performance. The beam-matching error metric, which considers the mean
square error between the desired and designed beam patterns, provides a more
practical evaluation approach. To tackle the non-convex challenge inherent in
beamforming design, we apply semidefinite relaxation (SDR) to address the
rank-one relaxation issue, followed by the iterative rank minimization
algorithm (IRM) for rank-one recovery. The simulation results showcase the
effectiveness of our proposed optimal beamforming design, emphasizing the
exceptional performance of the radar component in sensing tasks
Deep Joint Source-Channel Coding for DNA Image Storage: A Novel Approach with Enhanced Error Resilience and Biological Constraint Optimization
In the current era, DeoxyriboNucleic Acid (DNA) based data storage emerges as
an intriguing approach, garnering substantial academic interest and
investigation. This paper introduces a novel deep joint source-channel coding
(DJSCC) scheme for DNA image storage, designated as DJSCC-DNA. This paradigm
distinguishes itself from conventional DNA storage techniques through three key
modifications: 1) it employs advanced deep learning methodologies, employing
convolutional neural networks for DNA encoding and decoding processes; 2) it
seamlessly integrates DNA polymerase chain reaction (PCR) amplification into
the network architecture, thereby augmenting data recovery precision; and 3) it
restructures the loss function by targeting biological constraints for
optimization. The performance of the proposed model is demonstrated via
numerical results from specific channel testing, suggesting that it surpasses
conventional deep learning methodologies in terms of peak signal-to-noise ratio
(PSNR) and structural similarity index (SSIM). Additionally, the model
effectively ensures positive constraints on both homopolymer run-length and GC
content
Polar Coded Integrated Data and Energy Networking: A Deep Neural Network Assisted End-to-End Design
Wireless sensors are everywhere. To address their energy supply, we proposed
an end-to-end design for polar-coded integrated data and energy networking
(IDEN), where the conventional signal processing modules, such as
modulation/demodulation and channel decoding, are replaced by deep neural
networks (DNNs). Moreover, the input-output relationship of an energy harvester
(EH) is also modelled by a DNN. By jointly optimizing both the transmitter and
the receiver as an autoencoder (AE), we minimize the bit-error-rate (BER) and
maximize the harvested energy of the IDEN system, while satisfying the transmit
power budget constraint determined by the normalization layer in the
transmitter. Our simulation results demonstrate that the DNN aided end-to-end
design conceived outperforms its conventional model-based counterpart both in
terms of the harvested energy and the BER
Robust NOMA-assisted OTFS-ISAC Network Design with 3D Motion Prediction Topology
This paper proposes a novel non-orthogonal multiple access (NOMA)-assisted
orthogonal time-frequency space (OTFS)-integrated sensing and communication
(ISAC) network, which uses unmanned aerial vehicles (UAVs) as air base stations
to support multiple users. By employing ISAC, the UAV extracts position and
velocity information from the user's echo signals, and non-orthogonal power
allocation is conducted to achieve a superior achievable rate. A 3D motion
prediction topology is used to guide the NOMA transmission for multiple users,
and a robust power allocation solution is proposed under perfect and imperfect
channel estimation for Maxi-min Fairness (MMF) and Maximum sum-Rate (SR)
problems. Simulation results demonstrate the superiority of the proposed
NOMA-assisted OTFS-ISAC system over other systems in terms of achievable rate
under both perfect and imperfect channel conditions with the aid of 3D motion
prediction topology
Multi-Domain Polarization for Enhancing the Physical Layer Security of MIMO Systems
A novel Physical Layer Security (PLS) framework is conceived for enhancing
the security of the wireless communication systems by exploiting multi-domain
polarization in Multiple-Input Multiple-Output (MIMO) systems. We design a
sophisticated key generation scheme based on multi-domain polarization, and the
corresponding receivers. An in-depth analysis of the system's secrecy rate is
provided, demonstrating the confidentiality of our approach in the presence of
eavesdroppers having strong computational capabilities. More explicitly, our
simulation results and theoretical analysis corroborate the advantages of the
proposed scheme in terms of its bit error rate (BER), block error rate (BLER),
and maximum achievable secrecy rate. Our findings indicate that the innovative
PLS framework effectively enhances the security and reliability of wireless
communication systems. For instance, in a MIMO setup, the proposed
PLS strategy exhibits an improvement of dB compared to conventional MIMO,
systems at a BLER of while the eavesdropper's BLER reaches
Massive Wireless Energy Transfer without Channel State Information via Imperfect Intelligent Reflecting Surfaces
Intelligent Reflecting Surface (IRS) utilizes low-cost, passive reflecting
elements to enhance the passive beam gain, improve Wireless Energy Transfer
(WET) efficiency, and enable its deployment for numerous Internet of Things
(IoT) devices. However, the increasing number of IRS elements presents
considerable channel estimation challenges. This is due to the lack of active
Radio Frequency (RF) chains in an IRS, while pilot overhead becomes
intolerable. To address this issue, we propose a Channel State Information
(CSI)-free scheme that maximizes received energy in a specific direction and
covers the entire space through phased beam rotation. Furthermore, we take into
account the impact of an imperfect IRS and meticulously design the active
precoder and IRS reflecting phase shift to mitigate its effects. Our proposed
technique does not alter the existing IRS hardware architecture, allowing for
easy implementation in the current system, and enabling access or removal of
any Energy Receivers (ERs) without additional cost. Numerical results
illustrate the efficacy of our CSI-free scheme in facilitating large-scale IRS
without compromising performance due to excessive pilot overhead. Furthermore,
our scheme outperforms the CSI-based counterpart in scenarios involving
large-scale ERs, making it a promising solution in the era of IoT
Detector Guidance for Multi-Object Text-to-Image Generation
Diffusion models have demonstrated impressive performance in text-to-image
generation. They utilize a text encoder and cross-attention blocks to infuse
textual information into images at a pixel level. However, their capability to
generate images with text containing multiple objects is still restricted.
Previous works identify the problem of information mixing in the CLIP text
encoder and introduce the T5 text encoder or incorporate strong prior knowledge
to assist with the alignment. We find that mixing problems also occur on the
image side and in the cross-attention blocks. The noisy images can cause
different objects to appear similar, and the cross-attention blocks inject
information at a pixel level, leading to leakage of global object understanding
and resulting in object mixing. In this paper, we introduce Detector Guidance
(DG), which integrates a latent object detection model to separate different
objects during the generation process. DG first performs latent object
detection on cross-attention maps (CAMs) to obtain object information. Based on
this information, DG then masks conflicting prompts and enhances related
prompts by manipulating the following CAMs. We evaluate the effectiveness of DG
using Stable Diffusion on COCO, CC, and a novel multi-related object benchmark,
MRO. Human evaluations demonstrate that DG provides an 8-22\% advantage in
preventing the amalgamation of conflicting concepts and ensuring that each
object possesses its unique region without any human involvement and additional
iterations. Our implementation is available at
\url{https://github.com/luping-liu/Detector-Guidance}
A Tutorial on Coding Methods for DNA-based Molecular Communications and Storage
Exponential increase of data has motivated advances of data storage
technologies. As a promising storage media, DeoxyriboNucleic Acid (DNA) storage
provides a much higher data density and superior durability, compared with
state-of-the-art media. In this paper, we provide a tutorial on DNA storage and
its role in molecular communications. Firstly, we introduce fundamentals of
DNA-based molecular communications and storage (MCS), discussing the basic
process of performing DNA storage in MCS. Furthermore, we provide tutorials on
how conventional coding schemes that are used in wireless communications can be
applied to DNA-based MCS, along with numerical results. Finally, promising
research directions on DNA-based data storage in molecular communications are
introduced and discussed in this paper
Orthogonal-Time-Frequency-Space Signal Design for Integrated Data and Energy Transfer: Benefits from Doppler Offsets
Integrated data and energy transfer (IDET) is an advanced technology for enabling energy sustainability for massively deployed low-power electronic consumption components. However, the existing work of IDET using the orthogonal-frequency-division-multiplexing (OFDM) waveforms is designed for static scenarios, which would be severely affected by the destructive Doppler offset in high-mobility scenarios. Therefore, we proposed an IDET system based on orthogonal-time-frequency-space (OTFS) waveforms with the imperfect channel assumption, which is capable of counteracting the Doppler offset in high-mobility scenarios. At the transmitter, the OTFS-IDET system superimposes the random data signals and deterministic energy signals in the delay-Doppler (DD) domain with optimally designed amplitudes. The receiver optimally splits the received signal in the power domain for achieving the best IDET performance. After formulating a non-convex optimisation problem, it is transformed into a geometric programming (GP) problem through inequality relaxations to obtain the optimal solution. The simulation demonstrates that a higher amount of energy can be harvested when employing our proposed OTFS-IDET waveforms than the conventional OFDM-IDET ones in high mobility scenarios
Green Beamforming Design for Integrated Sensing and Communication Systems: A Practical Approach Using Beam-Matching Error Metrics
In this paper, we propose a green beamforming design for the integrated sensing and communication (ISAC) system, using beam-matching error to assess radar performance. The beam-matching error metric, which considers the mean square error between the desired and designed beam patterns, provides a more practical evaluation approach. To tackle the non-convex challenge inherent in beamforming design, we apply semidefinite relaxation (SDR) to address the rank-one relaxation issue, followed by the iterative rank minimisation algorithm (IRM) for rank-one recovery. The simulation results showcase the effectiveness of our proposed optimal beamforming design, emphasizing the exceptional performance of the radar component in sensing tasks
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