925 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
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
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
Fast Neighbor Discovery for Wireless Ad Hoc Network with Successive Interference Cancellation
Neighbor discovery (ND) is a key step in wireless ad hoc network, which
directly affects the efficiency of wireless networking. Improving the speed of
ND has always been the goal of ND algorithms. The classical ND algorithms lose
packets due to the collision of multiple packets, which greatly affects the
speed of the ND algorithms. Traditional methods detect packet collision and
implement retransmission when encountering packet loss. However, they does not
solve the packet collision problem and the performance improvement of ND
algorithms is limited. In this paper, the successive interference cancellation
(SIC) technology is introduced into the ND algorithms to unpack multiple
collision packets by distinguishing multiple packets in the power domain.
Besides, the multi-packet reception (MPR) is further applied to reduce the
probability of packet collision by distinguishing multiple received packets,
thus further improving the speed of ND algorithms. Six ND algorithms, namely
completely random algorithm (CRA), CRA based on SIC (CRA-SIC), CRA based on SIC
and MPR (CRA-SIC-MPR), scan-based algorithm (SBA), SBA based on SIC (SBA-SIC),
and SBA based on SIC and MPR (SBA-SIC-MPR), are theoretically analyzed and
verified by simulation. The simulation results show that SIC and MPR reduce the
ND time of SBA by 69.02% and CRA by 66.03% averagely.Comment: 16 pages, 16 figure
Coherent Compensation based ISAC Signal Processing for Long-range Sensing
Integrated sensing and communication (ISAC) will greatly enhance the
efficiency of physical resource utilization. The design of ISAC signal based on
the orthogonal frequency division multiplex (OFDM) signal is the mainstream.
However, when detecting the long-range target, the delay of echo signal exceeds
CP duration, which will result in inter-symbol interference (ISI) and
inter-carrier interference (ICI), limiting the sensing range. Facing the above
problem, we propose to increase useful signal power through coherent
compensation and improve the signal to interference plus noise power ratio
(SINR) of each OFDM block. Compared with the traditional 2D-FFT algorithm, the
improvement of SINR of range-doppler map (RDM) is verified by simulation, which
will expand the sensing range
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