We've also updated our Privacy Notice. Click here to see what's new. We carried out a fast processing investigation based on a graphics processing unit GPU for a distributed acoustic sensor using a linear frequency modulation pulse. The moving window cross-correlation calculations are realized on the GPU, which makes use of parallel computing. We analyzed the effect of the thread number in a block on the GPU streaming multiprocessor utilization efficiency and then compared the acceleration under different calculation scales.
By maximizing the streaming multiprocessor utilization efficiency and large calculation scale, a maximum acceleration ratio of Express 28 17 You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only.
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Cited by links are available to subscribers only. Figure files are available to subscribers only. Equations are available to subscribers only. Article level metrics are available to subscribers only. Allow All Cookies. This section simulates the measurement and data demodulation of the algorithm. After the binary data generation, QAM modulation and OFDM-LFM modulation, the authors use the signal processing method based on the modulation symbol domain to verify the external measurement and data demodulation capability of the unified system.
The target relative distance is , the relative velocity is , and the other main simulation environments are shown as follows see Table 1. Similarly, from Figure 4 , the peak value corresponds to the target relative velocity. Wherein the modulation symbol processing method can effectively suppress the side lobes generated by the communication modulation symbols.
Thus, the processing method can effectively avoid the influence of the communication information on the distance and the Doppler information, and then it reduces the influence between the external instrumentation part and the communication part.
Next, the authors simulated focusing on data transmission using a linear demodulation method. As far as authors know, LFM is mostly used in radar waveform design in which LFM does not carry data information, and only use amplitude and frequency changes of LFM to obtain velocity and distance information.
One of the innovations of this paper lies in the usage of OFDM-LFM to carry data information such as communication and remote control, meanwhile to obtain the external measurement information by signal features of the received signal. In addition, the current low-pass filtering method is not applicable. The low-pass filtering method is a noncoherent demodulation method, often used in signal blind detection, i.
The low-pass filter cannot remove the frequency offset, which would thus lead to the rotation of the constellation and then lost of the lock. Therefore, this paper adopts a linear demodulation method.
Although the center frequency is linearly transformed in each sub-band, in cooperative communication, the receiver can simulate this linear transformation locally and obtain effective data through coherent demodulation. The constellation simulation result is shown below. As shown in Figure 5 , the simulated constellation distribution is similar to the expected: data points are distributed around constellation points. But there are still a few data points at 0, 0 , which means that there still exists bit error due to misjudgment.
This is also the focus of the work of further research. This paper proposed a unified measurement, control, and communication system framework that will integrate multiple signal functions into a framework. Traditional LFM does not carry data information, only using amplitude and frequency changes of LFM to obtain velocity and distance information. In the proposed OFDM-LFM framework, the received signal also carries additional channel information besides the data flow due to its linear carrier frequency.
The additional signal features are related to time delay and frequency offset, which makes it possible to estimate the velocity and distance of the target. That is to say, in the proposed unified system, a unified backend algorithm can be used to demodulate telemetry, remote control, and communication data at the same time.
Instead of using multiple separate units to perform multiple functions, simulation results show that the unified system can effectively satisfy the need for velocity and distance estimation. The linear demodulation method overcomes the linear and frequency offset drawbacks caused by the current demodulation methods.
In further research, the authors will pay more attention to the exploration of the bit error rate. The data used to support the findings of this study are available from the corresponding author upon request. The authors declare that there are no conflicts of interest regarding the publication of this paper.
This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors.
Read the winning articles. Journal overview. Special Issues. Academic Editor: Zhiyong Xu. Received 21 May Accepted 02 Aug Published 24 Sep Abstract This paper proposes a unified system framework based on linear frequency modulation LFM and orthogonal frequency-division multiplexing OFDM as a solution for resource sharing, especially sharing space and hardware.
Introduction The increasing demand for different electronic applications results in multiusage of both hardware and space resources.
Figure 1. Figure 2. Parameter Value Symbol cycle s 0. Table 1. Main simulation parameters of unified measurement and control communication system. Figure 3. Figure 4. Figure 5. Coordinate reception signal constellation using linear demodulation method. References Y.
Cao, C. Long, T. Jiang, and S. Ivashko, G. Leus, and A. Liu, L. Zhao, D. Ding, Y. Li, and Y. Li, Z. Sun, X. Beginning in Junos OS Release To configure You can configure threshold values for fault events that trigger the sending of link event TLVs when the values exceed the threshold. To set threshold values for fault events on an interface, include the event-thresholds statement at the [edit protocols oam ethernet link-fault-management interface] hierarchy level.
You can also configure OAM threshold values within an action profile and apply the action profile to multiple interfaces. To create an action profile, include the action-profile statement at the [edit protocols oam ethernet link-fault-management] hierarchy level. You can configure Ethernet OAM either on an aggregate interface or on each of its member links.
To view OAM statistics, use the show oam ethernet link-fault-management operational mode command. To clear OAM statistics, use the clear oam ethernet link-fault-management statistics operational mode command.
To clear link-fault management state information and restart the link discovery process on Ethernet interfaces, use the clear oam ethernet link-fault-management state operational mode command. For more information about these commands, see the CLI Explorer. To enable IEEE When you enable IEEE Help us improve your experience. Let us know what you think. Do you have time for a two-minute survey?
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