## 1 Introduction  
With the evolution of post-5G and future 6G mobile communication systems, networks are expected to provide native sensing capabilities, presenting their possibilities to realize high-precision sensing by utilizing ubiquitous communication signals. Consequently, the convergence of wireless communication and perception technologies has received a lot of attention. In the past, communication and sensing systems were developed independently based on their respective operating frequency bands and functional purposes, demonstrating an independent development state [1]. But as technology advanced, sensing and communication systems could potentially be designed together. In this regard, Integrated Sensing and Communications (ISAC) technology was created [2]. Integrated Sensing and Communications (ISAC) senses the orientation angle, distance, speed, and other information of objects in the environment while transferring information. Reference [3] utilized the signal transmitted from a single base station for the vehicle position estimation study and, combined with the sparsity of the millimeter-wave channel model, proposed a vehicle position estimation algorithm based on distributed compressed sensing, but it did not take into account the Doppler effect of the moving target. Reference [4] proposed a signal processing algorithm based on modulated symbol processing at the receiver end of OFDM pass-sensing integration, which realizes the sensing function of estimating the target distance and speed and adopts the continuous wave transmission method of transceiver division to realize the estimation of the target distance and speed through the matrix point division, but the speed resolution of this method is very low. In order to estimate the target's distance and speed, Reference [5] employed the subspace projection method. This method's performance is superior to that of the conventional Fourier transform method, but noise still significantly affects it, and it is challenging to guarantee detection performance at low signal-to-noise ratios. An OFDM-based radar communication integrated system with superimposed frequency guides was examined in Reference [6]. The Expectation Maximization (EM) algorithm was used to jointly estimate the unknown symbols and channels, improving spectrum utilization. However, in order to guarantee that the frequency guides and the data at the receiving end are strictly orthogonal, the information rate had to be sacrificed. The Moving Target Indication-Moving Target Detection (MTI-MTD) technique is derived from the moving target detection technique centered on the Doppler effect, which incorporates the frequency difference between a moving target and clutter as a tool for suppressing ground clutter [7]. By emitting echoes from the ground and stationary targets, MTI compares the received radar signals over a number of scanning cycles in order to identify and follow moving targets. The MTI system compares the phase variations of several pulses to determine whether a target is moving. The radar echo's phase varies with the target's motion, whereas the stationary target's phase stays constant [8]. The MTI system suppresses background clutter and strengthens the moving target signal by accumulating the coherent information of several pulses. In areas with high levels of clutter, this makes it possible for the radar to detect moving targets more effectively. In summary, despite the continuous development of comms-integrated technology, it is still used to estimate the velocity of a moving target by transmitting a signal and receiving an echo. In this regard, this paper proposes an inter-symbol processing method for target velocity estimation only by receiving the radiated source signals of motion communication, realizing wireless communication without adding a sensing module for sensing, and simplifying the steps for the secondary design of waveforms.