基于OTFS通信系统的可分离CNN的信号检测
Ying Wang1, Zixu Zhang2, Hang Li1
1The School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
本研究介绍了SeCNN-OTFS,这是一种用于正交时频空间 (OTFS) 系统的低复杂度信号检测方法. 它在较少的参数下实现了高性能,非常适合资源有限的通信系统.
科学领域:
- 无线通信
- 信号处理
- 机器学习
背景情况:
- 在高多普勒环境中,正交时频空间 (OTFS) 调制具有优势.
- 在OTFS系统中,传统的信号检测方法面临着计算复杂性和性能方面的挑战.
- 深度学习方法,特别是卷积神经网络 (CNN) 是有前途的,但可能会耗费大量资源.
研究的目的:
- 为OTFS系统开发一种低复杂性和高效的信号检测方法.
- 在高多普勒条件下增强特征区分和训练稳定性.
- 为实际部署减少OTFS信号检测的计算开销.
主要方法:
- 提出了一个新的可分离卷积神经网络 (SeCNN) 架构,称为SeCNN-OTFS.
- 在SeparableBlock中集成的剩余连接和通道注意力机制.
- 将标准卷曲分解为深度和点向操作以减少复杂性.
主要成果:
- 与最小平方误差 (LS) 和最小平均平方误差 (MMSE) 估计器相比,SeCNN-OTFS表现优越.
- 在信号噪声比率 (SNR) 超过12.5dB时,实现与2D-CNN几乎相同的比特误差率 (BER).
- 与标准的2D-CNN相比,只需要19%的参数,这表明复杂性显著降低.
结论:
- SeCNN-OTFS为OTFS系统中的信号检测提供了有效和计算效率高的解决方案.
- 该方法非常适用于资源有限的应用,如卫星和物联网 (IoT) 通信.
- 对于需要更高准确度且有足够资源的场景,可使用常规卷积层的变体.
相关概念视频
Classification of Signals
878
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
878
Signal and System
1.1K
A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
1.1K
Difference from Background: Limit of Detection
7.1K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
7.1K
Signal Sequences and Sorting Receptors
5.6K
Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
5.6K
Even and Odd Signals
1.3K
An even signal, whether in continuous-time or discrete-time, is defined by its symmetry with its time-reversed version. Mathematically, this is represented as
1.3K
Discrete-Time Fourier Series
355
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
For a discrete-time periodic signal x[n]...
355


