246 research outputs found
Downlink Small-cell Base Station Cooperation Strategy in Fractal Small-cell Networks
Coordinated multipoint (CoMP) communications are considered for the
fifth-generation (5G) small-cell networks as a tool to improve the high data
rates and the cell-edge throughput. The average achievable rates of the
small-cell base stations (SBS) cooperation strategies with distance and
received signal power constraints are respectively derived for the fractal
small-cell networks based on the anisotropic path loss model. Simulation
results are presented to show that the average achievable rate with the
received signal power constraint is larger than the rate with a distance
constraint considering the same number of cooperative SBSs. The average
achievable rate with distance constraint decreases with the increase of the
intensity of SBSs when the anisotropic path loss model is considered. What's
more, the network energy efficiency of fractal smallcell networks adopting the
SBS cooperation strategy with the received signal power constraint is analyzed.
The network energy efficiency decreases with the increase of the intensity of
SBSs which indicates a challenge on the deployment design for fractal
small-cell networks.Comment: 5 figures. Accepted by Globecom 201
5G green cellular networks considering power allocation schemes
It is important to assess the effect of transmit power allocation schemes on
the energy consumption on random cellular networks. The energy efficiency of 5G
green cellular networks with average and water-filling power allocation schemes
is studied in this paper. Based on the proposed interference and achievable
rate model, an energy efficiency model is proposed for MIMO random cellular
networks. Furthermore, the energy efficiency with average and water-filling
power allocation schemes are presented, respectively. Numerical results
indicate that the maximum limits of energy efficiency are always there for MIMO
random cellular networks with different intensity ratios of mobile stations
(MSs) to base stations (BSs) and channel conditions. Compared with the average
power allocation scheme, the water-filling scheme is shown to improve the
energy efficiency of MIMO random cellular networks when channel state
information (CSI) is attainable for both transmitters and receivers.Comment: 14 pages, 7 figure
HM-ViT: Hetero-modal Vehicle-to-Vehicle Cooperative perception with vision transformer
Vehicle-to-Vehicle technologies have enabled autonomous vehicles to share
information to see through occlusions, greatly enhancing perception
performance. Nevertheless, existing works all focused on homogeneous traffic
where vehicles are equipped with the same type of sensors, which significantly
hampers the scale of collaboration and benefit of cross-modality interactions.
In this paper, we investigate the multi-agent hetero-modal cooperative
perception problem where agents may have distinct sensor modalities. We present
HM-ViT, the first unified multi-agent hetero-modal cooperative perception
framework that can collaboratively predict 3D objects for highly dynamic
vehicle-to-vehicle (V2V) collaborations with varying numbers and types of
agents. To effectively fuse features from multi-view images and LiDAR point
clouds, we design a novel heterogeneous 3D graph transformer to jointly reason
inter-agent and intra-agent interactions. The extensive experiments on the V2V
perception dataset OPV2V demonstrate that the HM-ViT outperforms SOTA
cooperative perception methods for V2V hetero-modal cooperative perception. We
will release codes to facilitate future research
Model-Agnostic Multi-Agent Perception Framework
Existing multi-agent perception systems assume that every agent utilizes the
same model with identical parameters and architecture. The performance can be
degraded with different perception models due to the mismatch in their
confidence scores. In this work, we propose a model-agnostic multi-agent
perception framework to reduce the negative effect caused by the model
discrepancies without sharing the model information. Specifically, we propose a
confidence calibrator that can eliminate the prediction confidence score bias.
Each agent performs such calibration independently on a standard public
database to protect intellectual property. We also propose a corresponding
bounding box aggregation algorithm that considers the confidence scores and the
spatial agreement of neighboring boxes. Our experiments shed light on the
necessity of model calibration across different agents, and the results show
that the proposed framework improves the baseline 3D object detection
performance of heterogeneous agents
V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception
Recent advancements in Vehicle-to-Everything communication technology have
enabled autonomous vehicles to share sensory information to obtain better
perception performance. With the rapid growth of autonomous vehicles and
intelligent infrastructure, the V2X perception systems will soon be deployed at
scale, which raises a safety-critical question: \textit{how can we evaluate and
improve its performance under challenging traffic scenarios before the
real-world deployment?} Collecting diverse large-scale real-world test scenes
seems to be the most straightforward solution, but it is expensive and
time-consuming, and the collections can only cover limited scenarios. To this
end, we propose the first open adversarial scene generator V2XP-ASG that can
produce realistic, challenging scenes for modern LiDAR-based multi-agent
perception systems. V2XP-ASG learns to construct an adversarial collaboration
graph and simultaneously perturb multiple agents' poses in an adversarial and
plausible manner. The experiments demonstrate that V2XP-ASG can effectively
identify challenging scenes for a large range of V2X perception systems.
Meanwhile, by training on the limited number of generated challenging scenes,
the accuracy of V2X perception systems can be further improved by 12.3\% on
challenging and 4\% on normal scenes. Our code will be released at
https://github.com/XHwind/V2XP-ASG.Comment: ICRA 2023, see https://github.com/XHwind/V2XP-AS
Incorporating basic calibrations in existing machine-learned turbulence modeling
This work aims to incorporate basic calibrations of Reynolds-averaged
Navier-Stokes (RANS) models as part of machine learning (ML) frameworks. The ML
frameworks considered are tensor-basis neural network (TBNN), physics-informed
machine learning (PIML), and field inversion & machine learning (FIML) in J.
Fluid Mech., 2016, 807, 155-166, Phys. Rev. Fluids, 2017, 2(3), 034603 and J.
Comp. Phys., 2016, 305, 758-774, and the baseline RANS models are the
one-equation Spalart-Allmaras model, the two-equation - model, and
the seven-equation Reynolds stress transport models. ML frameworks are trained
against plane channel flow and shear-layer flow data. We compare the ML
frameworks and study whether the machine-learned augmentations are detrimental
outside the training set. The findings are summarized as follows. The
augmentations due to TBNN are detrimental. PIML leads to augmentations that are
beneficial inside the training dataset but detrimental outside it. These
results are not affected by the baseline RANS model. FIML's augmentations to
the two eddy viscosity models, where an inner-layer treatment already exists,
are largely neutral. Its augmentation to the seven-equation model, where an
inner-layer treatment does not exist, improves the mean flow prediction in a
channel. Furthermore, these FIML augmentations are mostly non-detrimental
outside the training dataset. In addition to reporting these results, the paper
offers physical explanations of the results. Last, we note that the conclusions
drawn here are confined to the ML frameworks and the flows considered in this
study. More detailed comparative studies and validation & verification studies
are needed to account for developments in recent years
The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Research
Advances in Single-vehicle intelligence of automated driving have encountered
significant challenges because of limited capabilities in perception and
interaction with complex traffic environments. Cooperative Driving
Automation~(CDA) has been considered a pivotal solution to next-generation
automated driving and intelligent transportation. Though CDA has attracted much
attention from both academia and industry, exploration of its potential is
still in its infancy. In industry, companies tend to build their in-house data
collection pipeline and research tools to tailor their needs and protect
intellectual properties. Reinventing the wheels, however, wastes resources and
limits the generalizability of the developed approaches since no standardized
benchmarks exist. On the other hand, in academia, due to the absence of
real-world traffic data and computation resources, researchers often
investigate CDA topics in simplified and mostly simulated environments,
restricting the possibility of scaling the research outputs to real-world
scenarios. Therefore, there is an urgent need to establish an open-source
ecosystem~(OSE) to address the demands of different communities for CDA
research, particularly in the early exploratory research stages, and provide
the bridge to ensure an integrated development and testing pipeline that
diverse communities can share. In this paper, we introduce the OpenCDA research
ecosystem, a unified OSE integrated with a model zoo, a suite of driving
simulators at various resolutions, large-scale real-world and simulated
datasets, complete development toolkits for benchmark training/testing, and a
scenario database/generator. We also demonstrate the effectiveness of OpenCDA
OSE through example use cases, including cooperative 3D LiDAR detection,
cooperative merge, cooperative camera-based map prediction, and adversarial
scenario generation
SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
Federated learning (FL) utilizes edge computing devices to collaboratively
train a shared model while each device can fully control its local data access.
Generally, FL techniques focus on learning model on independent and identically
distributed (iid) dataset and cannot achieve satisfiable performance on non-iid
datasets (e.g. learning a multi-class classifier but each client only has a
single class dataset). Some personalized approaches have been proposed to
mitigate non-iid issues. However, such approaches cannot handle underlying data
distribution shift, namely data distribution skew, which is quite common in
real scenarios (e.g. recommendation systems learn user behaviors which change
over time). In this work, we provide a solution to the challenge by leveraging
smart-contract with federated learning to build optimized, personalized deep
learning models. Specifically, our approach utilizes smart contract to reach
consensus among distributed trainers on the optimal weights of personalized
models. We conduct experiments across multiple models (CNN and MLP) and
multiple datasets (MNIST and CIFAR-10). The experimental results demonstrate
that our personalized learning models can achieve better accuracy and faster
convergence compared to classic federated and personalized learning. Compared
with the model given by baseline FedAvg algorithm, the average accuracy of our
personalized learning models is improved by 2% to 20%, and the convergence rate
is about 2 faster. Moreover, we also illustrate that our approach is
secure against recent attack on distributed learning.Comment: 12 pages, 9 figure
- …