ترغب بنشر مسار تعليمي؟ اضغط هنا

Composite Time-Frequency Analysis and Siamese Neural Network based Compound Interference Identification for Hopping Frequency System

142   0   0.0 ( 0 )
 نشر من قبل Weiheng Jiang
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

In a hostile environment, interference identification plays an important role in protecting the authorized communication system and avoiding its performance degradation. In this paper, the interference identification problem for the frequency hopping communication system is discussed. Considering presence of multiple and compound interference in the frequency hopping system, in order to fully extracted effective features of the interferences from the received signals, a composite time-frequency analysis method based on both the linear and bilinear transform is proposed. The time-frequency spectrograms obtained from the time-frequency analysis are constructed as matching pairs and input into the deep neural network for identification. In particular, the Siamese neural network is adopted as the classifier to perform the interference identification. That is, the paired spectrograms are input into the two sub-networks of the Siamese neural network to extract the features of the paired spectrograms. The Siamese neural network is trained and tested by calculating the gap between the generated features, and the interference type identification is realized by the trained Siamese neural network. The simulation results confirm that the proposed algorithm can obtain higher identification accuracy than both traditional single time-frequency representation based approach and the AlexNet transfer learning or convolutional neural network based methods.



قيم البحث

اقرأ أيضاً

We present experimental observations of interference between an atomic spin coherence and an optical field in a {Lambda}-type gradient echo memory. The interference is mediated by a strong classical field that couples a weak probe field to the atomic coherence through a resonant Raman transition. Interference can be observed between a prepared spin coherence and another propagating optical field, or between multiple {Lambda} transitions driving a single spin coherence. In principle, the interference in each scheme can yield a near unity visibility.
Convolutional neural network (CNN) is one of the most widely-used successful architectures in the era of deep learning. However, the high-computational cost of CNN still hampers more universal uses to light devices. Fortunately, the Fourier transform on convolution gives an elegant and promising solution to dramatically reduce the computation cost. Recently, some studies devote to such a challenging problem and pursue the complete frequency computation without any switching between spatial domain and frequent domain. In this work, we revisit the Fourier transform theory to derive feed-forward and back-propagation frequency operations of typical network modules such as convolution, activation and pooling. Due to the calculation limitation of complex numbers on most computation tools, we especially extend the Fourier transform to the Laplace transform for CNN, which can run in the real domain with more relaxed constraints. This work more focus on a theoretical extension and discussion about frequency CNN, and lay some theoretical ground for real application.
Enabled by the advancement in radio frequency technologies, the convergence of radar and communication systems becomes increasingly promising and is envisioned as a key feature of future 6G networks. Recently, the frequency-hopping (FH) MIMO radar is introduced to underlay dual-function radar-communication (DFRC) systems. Superior to many previous radar-centric DFRC designs, the symbol rate of FH-MIMO radar-based DFRC (FH-MIMO DFRC) can exceed the radar pulse repetition frequency. However, many practical issues, particularly those regarding effective data communications, are unexplored/unsolved. To promote the awareness and general understanding of the novel DFRC, this article is devoted to providing a timely introduction of FH-MIMO DFRC. We comprehensively review many essential aspects of the novel DFRC: channel/signal models, signaling strategies, modulation/demodulation processing and channel estimation methods, to name a few. We also highlight major remaining issues in FH-MIMO DFRC and suggest potential solutions to shed light on future research works.
In this paper, we present a systematic approach that transforms the program execution trace into the frequency domain and precisely identifies program phases. The analyzed results can be embedded into program code to mark the starting point and execu tion characteristics, such as CPI (Cycles per Instruction), of each phase. The so generated information can be applied to runtime program phase prediction. With the precise program phase information, more intelligent software and system optimization techniques can be further explored and developed.
Integrating time-frequency resource conversion (TFRC), a new network resource allocation strategy, with call admission control can not only increase the cell capacity but also reduce network congestion effectively. However, the optimal setting of TFR C-oriented call admission control suffers from the curse of dimensionality, due to Markov chain-based optimization in a high-dimensional space. To address the scalability issue of TFRC, in [1] we extend the study of TFRC into the area of scheduling. Specifically, we study downlink scheduling based on TFRC for an LTE-type cellular network, to maximize service delivery. The service scheduling of interest is formulated as a joint request, channel and slot allocation problem which is NP-hard. An offline deflation and sequential fixing based algorithm (named DSFRB) with only polynomial-time complexity is proposed to solve the problem. For practical online implementation, two TFRC-enabled low-complexity algorithms, modified Smith ratio algorithm (named MSR) and modified exponential capacity algorithm (named MEC), are proposed as well. In this report, we present detailed numerical results of the proposed offline and online algorithms, which not only show the effectiveness of the proposed algorithms but also corroborate the advantages of the proposed TFRC-based schedule techniques in terms of quality-of-service (QoS) provisioning for each user and revenue improvement for a service operator.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا