950 research outputs found
On the Construction of Radio Environment Maps for Cognitive Radio Networks
The Radio Environment Map (REM) provides an effective approach to Dynamic
Spectrum Access (DSA) in Cognitive Radio Networks (CRNs). Previous results on
REM construction show that there exists a tradeoff between the number of
measurements (sensors) and REM accuracy. In this paper, we analyze this
tradeoff and determine that the REM error is a decreasing and convex function
of the number of measurements (sensors). The concept of geographic entropy is
introduced to quantify this relationship. And the influence of sensor
deployment on REM accuracy is examined using information theory techniques. The
results obtained in this paper are applicable not only for the REM, but also
for wireless sensor network deployment.Comment: 6 pages, 7 figures, IEEE WCNC conferenc
SLAM for Multiple Extended Targets using 5G Signal
5th Generation (5G) mobile communication systems operating at around 28 GHz
have the potential to be applied to simultaneous localization and mapping
(SLAM). Most existing 5G SLAM studies estimate environment as many point
targets, instead of extended targets. In this paper, we focus on the
performance analysis of 5G SLAM for multiple extended targets. To evaluate the
mapping performance of multiple extended targets, a new mapping error metric,
named extended targets generalized optimal sub-pattern assignment (ET-GOPSA),
is proposed in this paper. Compared with the existing metrics, ET-GOPSA not
only considers the accuracy error of target estimation, the cost of missing
detection, the cost of false detection, but also the cost of matching the
estimated point with the extended target. To evaluate the performance of 5G
signal in SLAM, we analyze and simulate the mapping error of 5G signal sensing
by ET-GOPSA. Simulation results show that, under the condition of SNR = 10 dB,
5G signal sensing can barely meet to meet the requirements of SLAM for multiple
extended targets with the carrier frequency of 28 GHz, the bandwidth of 1.23
GHz, and the antenna size of 32
The Performance Analysis of Spectrum Sharing between UAV enabled Wireless Mesh Networks and Ground Networks
Unmanned aerial vehicle (UAV) has the advantages of large coverage and
flexibility, which could be applied in disaster management to provide wireless
services to the rescuers and victims. When UAVs forms an aerial mesh network,
line-of-sight (LoS) air-to-air (A2A) communications have long transmission
distance, which extends the coverage of multiple UAVs. However, the capacity of
UAV is constrained due to the multiple hop transmissions in aerial mesh
networks. In this paper, spectrum sharing between UAV enabled wireless mesh
networks and ground networks is studied to improve the capacity of UAV
networks. Considering two-dimensional (2D) and three-dimensional (3D)
homogeneous Poisson point process (PPP) modeling for the distribution of UAVs
within a vertical range {\Delta}h, stochastic geometry is applied to analyze
the impact of the height of UAVs, the transmit power of UAVs, the density of
UAVs and the vertical range, etc., on the coverage probability of ground
network user and UAV network user. Besides, performance improvement of spectrum
sharing with directional antenna is verified. With the object function of
maximizing the transmission capacity, the optimal altitude of UAVs is obtained.
This paper provides a theoretical guideline for the spectrum sharing of UAV
enabled wireless mesh networks, which may contribute significant value to the
study of spectrum sharing mechanisms for UAV enabled wireless mesh networks.Comment: 12 pages, 13 figures, IEEE Sensors Journa
Research on the Improved Combinatorial Prediction Model of Steel Price Based on Time Series
Accurately predicting the price change of steel (main building materials) is an effective means to control and manage the cost of construction projects. It is one of the ways for construction enterprises to reasonably allocate building materials, save resources, reduce carbon emissions and reduce environmental pollution. Based on the monthly historical price data of 100 steel rebar (16 mm) from November 2010 to February 2019, the separation and retrieval process of the four components in the time series are improved. The improved multiplicative and additive models were used to make separate predictions, and the reasonable weight is given to combine the multiplication and addition model by the reciprocal of variance method. Finally, an improved prediction model of steel bar price combination with higher prediction accuracy is obtained. The prediction results show that the improved multiplication model and addition model have higher prediction accuracy, their MAPE are 2.62% and 2.36% respectively. Moreover, the prediction accuracy of the combined model is even higher, its MAPE is 2.29%. The prediction accuracy of the improved composite model is higher than that of the individual models. The improved combined prediction model of reinforcement price based on time series method can provide some reference and help for cost control and management in construction engineering, further reduce resource waste and construction non-point source pollution
Modeling and Design of the Communication Sensing and Control Coupled Closed-Loop Industrial System
With the advent of 5G era, factories are transitioning towards wireless
networks to break free from the limitations of wired networks. In 5G-enabled
factories, unmanned automatic devices such as automated guided vehicles and
robotic arms complete production tasks cooperatively through the periodic
control loops. In such loops, the sensing data is generated by sensors, and
transmitted to the control center through uplink wireless communications. The
corresponding control commands are generated and sent back to the devices
through downlink wireless communications. Since wireless communications,
sensing and control are tightly coupled, there are big challenges on the
modeling and design of such closed-loop systems. In particular, existing
theoretical tools of these functionalities have different modelings and
underlying assumptions, which make it difficult for them to collaborate with
each other. Therefore, in this paper, an analytical closed-loop model is
proposed, where the performances and resources of communication, sensing and
control are deeply related. To achieve the optimal control performance, a
co-design of communication resource allocation and control method is proposed,
inspired by the model predictive control algorithm. Numerical results are
provided to demonstrate the relationships between the resources and control
performances.Comment: 6 pages, 3 figures, received by GlobeCom 202
ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and Communication
Recent advances in wireless communication with the enormous demands of
sensing ability have given rise to the integrated sensing and communication
(ISAC) technology, among which passive sensing plays an important role. The
main challenge of passive sensing is how to achieve high sensing performance in
the condition of communication demodulation errors. In this paper, we propose
an ISAC network (ISAC-NET) that combines passive sensing with communication
signal detection by using model-driven deep learning (DL). Dissimilar to
existing passive sensing algorithms that first demodulate the transmitted
symbols and then obtain passive sensing results from the demodulated symbols,
ISAC-NET obtains passive sensing results and communication demodulated symbols
simultaneously. Different from the data-driven DL method, we adopt the
block-by-block signal processing method that divides the ISAC-NET into the
passive sensing module, signal detection module and channel reconstruction
module. From the simulation results, ISAC-NET obtains better communication
performance than the traditional signal demodulation algorithm, which is close
to OAMP-Net2. Compared to the 2D-DFT algorithm, ISAC-NET demonstrates
significantly enhanced sensing performance. In summary, ISAC-NET is a promising
tool for passive sensing and communication in wireless communications.Comment: 29 pages, 11 figure
Capacity and Delay of Unmanned Aerial Vehicle Networks with Mobility
Unmanned aerial vehicles (UAVs) are widely exploited in environment
monitoring, search-and-rescue, etc. However, the mobility and short flight
duration of UAVs bring challenges for UAV networking. In this paper, we study
the UAV networks with n UAVs acting as aerial sensors. UAVs generally have
short flight duration and need to frequently get energy replenishment from the
control station. Hence the returning UAVs bring the data of the UAVs along the
returning paths to the control station with a store-carry-and-forward (SCF)
mode. A critical range for the distance between the UAV and the control station
is discovered. Within the critical range, the per-node capacity of the SCF mode
is O(n/log n) times higher than that of the multi-hop mode. However, the
per-node capacity of the SCF mode outside the critical range decreases with the
distance between the UAV and the control station. To eliminate the critical
range, a mobility control scheme is proposed such that the capacity scaling
laws of the SCF mode are the same for all UAVs, which improves the capacity
performance of UAV networks. Moreover, the delay of the SCF mode is derived.
The impact of the size of the entire region, the velocity of UAVs, the number
of UAVs and the flight duration of UAVs on the delay of SCF mode is analyzed.
This paper reveals that the mobility and short flight duration of UAVs have
beneficial effects on the performance of UAV networks, which may motivate the
study of SCF schemes for UAV networks.Comment: 14 pages, 10 figures, IEEE Internet of Things Journa
Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency
Supervised learning has been widely used for attack categorization, requiring
high-quality data and labels. However, the data is often imbalanced and it is
difficult to obtain sufficient annotations. Moreover, supervised models are
subject to real-world deployment issues, such as defending against unseen
artificial attacks. To tackle the challenges, we propose a semi-supervised
fine-grained attack categorization framework consisting of an encoder and a
two-branch structure and this framework can be generalized to different
supervised models. The multilayer perceptron with residual connection is used
as the encoder to extract features and reduce the complexity. The Recurrent
Prototype Module (RPM) is proposed to train the encoder effectively in a
semi-supervised manner. To alleviate the data imbalance problem, we introduce
the Weight-Task Consistency (WTC) into the iterative process of RPM by
assigning larger weights to classes with fewer samples in the loss function. In
addition, to cope with new attacks in real-world deployment, we propose an
Active Adaption Resampling (AAR) method, which can better discover the
distribution of unseen sample data and adapt the parameters of encoder.
Experimental results show that our model outperforms the state-of-the-art
semi-supervised attack detection methods with a 3% improvement in
classification accuracy and a 90% reduction in training time.Comment: Tech repor
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