Orthogonal time frequency space (OTFS) is a promising modulation scheme for
wireless communication in high-mobility scenarios. Recently, a reservoir
computing (RC) based approach has been introduced for online subframe-based
symbol detection in the OTFS system, where only the limited over-the-air (OTA)
pilot symbols are utilized for training. However, the previous RC-based
approach does not design the RC architecture based on the properties of the
OTFS system to fully unlock the potential of RC. This paper introduces a novel
two-dimensional RC (2D-RC) approach for online symbol detection on a subframe
basis in the OTFS system. The 2D-RC is designed to have a two-dimensional (2D)
filtering structure to equalize the 2D circular channel effect in the
delay-Doppler (DD) domain of the OTFS system. With the introduced architecture,
the 2D-RC can operate in the DD domain with only a single neural network,
unlike our previous work which requires multiple RCs to track channel
variations in the time domain. Experimental results demonstrate the advantages
of the 2D-RC approach over the previous RC-based approach and the compared
model-based methods across different modulation orders.

该研究介绍了一种新颖的二维时频空域 (Orthogonal Time Frequency Space, OTFS) 中的在线符号检测方法，该方法基于储备计算 (Reservoir Computing, RC) 技术，具有二维滤波结构以弥补 OTFS 系统中的两个维度循环信道影响，实验证明了该方法在不同调制阶数下相较于以往的 RC 方法和模型基方法的优势。

基于神经网络的 OTFS 符号检测的二维滤波

Neural Network-based Two-Dimensional Filtering for OTFS Symbol Detection

Orthogonal time frequency space (OTFS) is a promising modulation scheme for
wireless communication in high-mobility scenarios. Recently, a reservoir
computing (RC) based approach has been introduced for online subframe-based
symbol detection in the OTFS system, where only a limited number of
over-the-air (OTA) pilot symbols are utilized for training. However, this
approach does not leverage the domain knowledge specific to the OTFS system.
This paper introduces a novel two-dimensional RC (2D-RC) method that
incorporates the structural knowledge of the OTFS system into the design for
online symbol detection on a subframe basis. Specifically, as the channel
response acts as a two-dimensional (2D) operation over the transmitted
information symbols in the delay-Doppler (DD) domain, the 2D-RC is designed to
have a 2D structure to equalize the channel. With the introduced architecture,
the 2D-RC can benefit from the predictable channel representation in the DD
domain. Moreover, unlike the previous work that requires multiple RCs to learn
the channel feature, the 2D-RC only requires a single neural network for
detection. Experimental results demonstrate the effectiveness of the 2D-RC
approach across different OTFS system variants and modulation orders.

一个基于二维嵌入具体知识来设计的二维嵌入系统网络 (2D-RC) 方法，可用于在线子帧基础上的符号检测。通过在时延 - 多普勒（DD）域中具有 2D 结构来平衡信道，并且只需要一个神经网络进行检测。实验结果表明，2D-RC 方法在不同 OTFS 系统变体和调制序列中的效果显著。

2D-RC：基于二维神经网络的 OTFS 符号检测方法

2D-RC: Two-Dimensional Neural Network Approach for OTFS Symbol Detection

Orthogonal time frequency space (OTFS) modulation is a promising candidate
for supporting reliable information transmission in high-mobility vehicular
networks. In this paper, we consider the employment of the integrated (radar)
sensing and communication (ISAC) technique for assisting OTFS transmission in
both uplink and downlink vehicular communication systems. Benefiting from the
OTFS-ISAC signals, the roadside unit (RSU) is capable of simultaneously
transmitting downlink information to the vehicles and estimating the sensing
parameters of vehicles, e.g., locations and speeds, based on the reflected
echoes. Then, relying on the estimated kinematic parameters of vehicles, the
RSU can construct the topology of the vehicular network that enables the
prediction of the vehicle states in the following time instant. Consequently,
the RSU can effectively formulate the transmit downlink beamformers according
to the predicted parameters to counteract the channel adversity such that the
vehicles can directly detect the information without the need of performing
channel estimation. As for the uplink transmission, the RSU can infer the
delays and Dopplers associated with different channel paths based on the
aforementioned dynamic topology of the vehicular network. Thus, inserting guard
space as in conventional methods are not needed for uplink channel estimation
which removes the required training overhead. Finally, an efficient uplink
detector is proposed by taking into account the channel estimation uncertainty.
Through numerical simulations, we demonstrate the benefits of the proposed
ISAC-assisted OTFS transmission scheme.

本文提出了一种利用集成 (radar) 感知和通信技术 (ISAC) 辅助正交时间频率空间 (OTFS) 传输的方法，以便于在汽车通信系统中支持可靠的信息传输，并证明了其能够有效的降低训练开销和传输误差。