7,462 research outputs found
AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing
The next generation of Internet services, such as Metaverse, rely on mixed
reality (MR) technology to provide immersive user experiences. However, the
limited computation power of MR headset-mounted devices (HMDs) hinders the
deployment of such services. Therefore, we propose an efficient information
sharing scheme based on full-duplex device-to-device (D2D) semantic
communications to address this issue. Our approach enables users to avoid heavy
and repetitive computational tasks, such as artificial intelligence-generated
content (AIGC) in the view images of all MR users. Specifically, a user can
transmit the generated content and semantic information extracted from their
view image to nearby users, who can then use this information to obtain the
spatial matching of computation results under their view images. We analyze the
performance of full-duplex D2D communications, including the achievable rate
and bit error probability, by using generalized small-scale fading models. To
facilitate semantic information sharing among users, we design a contract
theoretic AI-generated incentive mechanism. The proposed diffusion model
generates the optimal contract design, outperforming two deep reinforcement
learning algorithms, i.e., proximal policy optimization and soft actor-critic
algorithms. Our numerical analysis experiment proves the effectiveness of our
proposed methods. The code for this paper is available at
https://github.com/HongyangDu/SemSharingComment: Accepted by IEEE JSA
An investigation of entorhinal spatial representations in self-localisation behaviours
Spatial-modulated cells of the medial entorhinal cortex (MEC) and neighbouring cortices are thought to provide the neural substrate for self-localisation behaviours. These cells include grid cells of the MEC which are thought to compute path integration operations to update self-location estimates. In order to read this grid code, downstream cells are thought to reconstruct a positional estimate as a simple rate-coded representation of space.
Here, I show the coding scheme of grid cell and putative readout cells recorded from mice performing a virtual reality (VR) linear location task which engaged mice in both beaconing and path integration behaviours. I found grid cells can encode two unique coding schemes on the linear track, namely a position code which reflects periodic grid fields anchored to salient features of the track and a distance code which reflects periodic grid fields without this anchoring. Grid cells were found to switch between these coding schemes within sessions. When grid cells were encoding position, mice performed better at trials that required path integration but not on trials that required beaconing. This result provides the first mechanistic evidence linking grid cell activity to path integration-dependent behaviour.
Putative readout cells were found in the form of ramp cells which fire proportionally as a function of location in defined regions of the linear track. This ramping activity was found to be primarily explained by track position rather than other kinematic variables like speed and acceleration. These representations were found to be maintained across both trial types and outcomes indicating they likely result from recall of the track structure.
Together, these results support the functional importance of grid and ramp cells for self-localisation behaviours. Future investigations will look into the coherence between these two neural populations, which may together form a complete neural system for coding and decoding self-location in the brain
Secure Short-Packet Communications via UAV-Enabled Mobile Relaying: Joint Resource Optimization and 3D Trajectory Design
Short-packet communication (SPC) and unmanned aerial vehicles (UAVs) are
anticipated to play crucial roles in the development of 5G-and-beyond wireless
networks and the Internet of Things (IoT). In this paper, we propose a secure
SPC system, where a UAV serves as a mobile decode-and-forward (DF) relay,
periodically receiving and relaying small data packets from a remote IoT device
to its receiver in two hops with strict latency requirements, in the presence
of an eavesdropper. This system requires careful optimization of important
design parameters, such as the coding blocklengths of both hops, transmit
powers, and UAV's trajectory. While the overall optimization problem is
nonconvex, we tackle it by applying a block successive convex approximation
(BSCA) approach to divide the original problem into three subproblems and solve
them separately. Then, an overall iterative algorithm is proposed to obtain the
final design with guaranteed convergence. Our proposed low-complexity algorithm
incorporates 3D trajectory design and resource management to optimize the
effective average secrecy throughput of the communication system over the
course of UAV-relay's mission. Simulation results demonstrate significant
performance improvements compared to various benchmark schemes and provide
useful design insights on the coding blocklengths and transmit powers along the
trajectory of the UAV
Improving diagnostic procedures for epilepsy through automated recording and analysis of patients’ history
Transient loss of consciousness (TLOC) is a time-limited state of profound cognitive impairment characterised by amnesia, abnormal motor control, loss of responsiveness, a short duration and complete recovery. Most instances of TLOC are caused by one of three health conditions: epilepsy, functional (dissociative) seizures (FDS), or syncope. There is often a delay before the correct diagnosis is made and 10-20% of individuals initially receive an incorrect diagnosis. Clinical decision tools based on the endorsement of TLOC symptom lists have been limited to distinguishing between two causes of TLOC. The Initial Paroxysmal Event Profile (iPEP) has shown promise but was demonstrated to have greater accuracy in distinguishing between syncope and epilepsy or FDS than between epilepsy and FDS. The objective of this thesis was to investigate whether interactional, linguistic, and communicative differences in how people with epilepsy and people with FDS describe their experiences of TLOC can improve the predictive performance of the iPEP. An online web application was designed that collected information about TLOC symptoms and medical history from patients and witnesses using a binary questionnaire and verbal interaction with a virtual agent. We explored potential methods of automatically detecting these communicative differences, whether the differences were present during an interaction with a VA, to what extent these automatically detectable communicative differences improve the performance of the iPEP, and the acceptability of the application from the perspective of patients and witnesses. The two feature sets that were applied to previous doctor-patient interactions, features designed to measure formulation effort or detect semantic differences between the two groups, were able to predict the diagnosis with an accuracy of 71% and 81%, respectively. Individuals with epilepsy or FDS provided descriptions of TLOC to the VA that were qualitatively like those observed in previous research. Both feature sets were effective predictors of the diagnosis when applied to the web application recordings (85.7% and 85.7%). Overall, the accuracy of machine learning models trained for the threeway classification between epilepsy, FDS, and syncope using the iPEP responses from patients that were collected through the web application was worse than the performance observed in previous research (65.8% vs 78.3%), but the performance was increased by the inclusion of features extracted from the spoken descriptions on TLOC (85.5%). Finally, most participants who provided feedback reported that the online application was acceptable. These findings suggest that it is feasible to differentiate between people with epilepsy and people with FDS using an automated analysis of spoken seizure descriptions. Furthermore, incorporating these features into a clinical decision tool for TLOC can improve the predictive performance by improving the differential diagnosis between these two health conditions. Future research should use the feedback to improve the design of the application and increase perceived acceptability of the approach
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
SWIPT aided Cooperative Communications with Energy Harvesting based Selective-Decode-and-Forward Protocol: Benefiting from Channel Aging Effect
Simultaneous wireless information and power transfer (SWIPT) in radio-frequency (RF) bands enables flexible deployment of battery-powered relays for extending communication coverage. Relays receive downlink RF signals emitted by a source for information decoding and energy harvesting, while the harvested energy is consumed for both information decoding and information forwarding to a destination. An energy harvesting based selective-decode-and-forward (EH-SDF) protocol is proposed, where only the relays having information correctly decoded are activated for information forwarding, while others harvest and store energy for the future use. By considering the channel aging effect, we propose a joint relay selection, power allocation, transmit beamforming and signal splitting design in order to maximise the end-to-end (e2e) throughput of this EH-SDF aided cooperative communication system. Two scenarios with/without direct link between the source and the destination are studied, respectively. The original formulated non-convex optimisation problems with coupled variables are decoupled into three subproblems which are solved by an iterative optimisation algorithm. Numerical results demonstrate that our design with the EH-SDF protocol achieves a higher e2e throughput than the traditional decode-and-forward (DF) counterpart. Moreover, the impact of the channel aging effect on the e2e throughput is also evaluated
Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks
We develop a novel graph-based trainable framework to maximize the weighted
sum energy efficiency (WSEE) for power allocation in wireless communication
networks. To address the non-convex nature of the problem, the proposed method
consists of modular structures inspired by a classical iterative suboptimal
approach and enhanced with learnable components. More precisely, we propose a
deep unfolding of the successive concave approximation (SCA) method. In our
unfolded SCA (USCA) framework, the originally preset parameters are now
learnable via graph convolutional neural networks (GCNs) that directly exploit
multi-user channel state information as the underlying graph adjacency matrix.
We show the permutation equivariance of the proposed architecture, which is a
desirable property for models applied to wireless network data. The USCA
framework is trained through a stochastic gradient descent approach using a
progressive training strategy. The unsupervised loss is carefully devised to
feature the monotonic property of the objective under maximum power
constraints. Comprehensive numerical results demonstrate its generalizability
across different network topologies of varying size, density, and channel
distribution. Thorough comparisons illustrate the improved performance and
robustness of USCA over state-of-the-art benchmarks.Comment: Published in IEEE Transactions on Wireless Communication
Mean Field Game-based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems
Data collection and processing timely is crucial for mobile crowd integrated
sensing, communication, and computation~(ISCC) systems with various
applications such as smart home and connected cars, which requires numerous
integrated sensing and communication~(ISAC) devices to sense the targets and
offload the data to the base station~(BS) for further processing. However, as
the number of ISAC devices growing, there exists intensive interactions among
ISAC devices in the processes of data collection and processing since they
share the common network resources. In this paper, we consider the environment
sensing problem in the large-scale mobile crowd ISCC systems and propose an
efficient waveform precoding design algorithm based on the mean field
game~(MFG). Specifically, to handle the complex interactions among large-scale
ISAC devices, we first utilize the MFG method to transform the influence from
other ISAC devices into the mean field term and derive the
Fokker-Planck-Kolmogorov equation, which model the evolution of the system
state. Then, we derive the cost function based on the mean field term and
reformulate the waveform precoding design problem. Next, we utilize the G-prox
primal-dual hybrid gradient algorithm to solve the reformulated problem and
analyze the computational complexity of the proposed algorithm. Finally,
simulation results demonstrate that the proposed algorithm can solve the
interactions among large-scale ISAC devices effectively in the ISCC process. In
addition, compared with other baselines, the proposed waveform precoding design
algorithm has advantages in improving communication performance and reducing
cost function.Comment: 13 pages,9 figure
Modelling, Monitoring, Control and Optimization for Complex Industrial Processes
This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors
MIMO無線伝送に適したスケーラブルビデオコーディングに関する研究
Because of the COVID-19 pandemic, a new normal has taken over. It affects the higher demand for using video traffic. H.264/SVC is the video compression standard with several advantages compared with the previous standard, such as a smaller storage space and scalability of video quality depending on network quality. The H.264/SVC bitstream includes one base layer (BL), the most important layer, and one or more enhancement layers (EL) which can be leveraged to optimize the video scalability depending on the network condition and user preferences. The method of transmission is powerful as the video coding method. The transmission of the good video quality will not be effective without a suitable transmission method. In this thesis, we study and research the H.264 scalable video coding transmission with IEEE 802.11ac standard MIMO wireless transmission. We focus on the suitable transmission method for H.264/SVC in a different environment. We divide the research focusing on two issues: 1. With the difference channel environment: The suitable H.264/SVC transmission technique in IEEE 802.11ac with the specific quantization parameter of video encoding was proposed. This aim is to compare three techniques in IEEE 802.11ac: STBC, SISO, and MIMO. In this focus, only the accuracy of the video was considered to measure the efficiency of the transmission technique. This part proposed to utilize STBC to improve the quality of H.264/SVC video transmission. We have shown the performance of H.264/SVC video transmission with three multiple antenna techniques. The results show that STBC is the best technique for H.264/SVC transmission under a low-quality channel environment. The best result shows that STBC in channel model D can improve the PSNR by 67 percent and 76 percent compared with SISO and MIMO, respectively, at low SNR of 20 dB. Due to STBC transmitting multiple copies of data, it can increase data reliability. We proved that STBC is the most suitable multiple antenna technique to improve the quality and realizability of video transmission in both PSNR and bit error rate (BER). 2. With the different transmission distance: H.264/SVC video transmission on MIMO with RSSI feedback was proposed. This aim to proposes the allocation of packetization in the transmission packet and the compromising of quantization parameter encoding both vary on the channel efficiency. This part proposed a MIMO transmission system for H.264 scalable video coding that does not require full CSI feedback. Instead of the CSI feedback, we have used the RSSI and table of encoding rules obtained via link simulation in MATLAB. The encoding rule takes the form of the encoding ratio between the base and enhancement layer, which was done by adjusting the quantization parameter. This proposed system has been shown to improve the PSNR by at least 16 dB and increase the effective distance of 6 meters above compared with the conventional method.九州工業大学博士学位論文 学位記番号:情工博甲第372号 学位授与年月日:令和4年12月27日1 Introduction|2 Video Transmission System Overview|3 H.264/SVC Video Transmission by IEEE 802.11ac Techniques|4 H.264/SVC Video Transmission on MIMO with RSSI Feedback|5 Conclusion and Future Work九州工業大学令和4年
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