# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 2 The Development Status and Key Technology Of ISAC
### Key Technology
#### 2.4.2 ISAC Beamforming  
Beamforming has many advantages for communication and target sensing. On the one hand, it can improve the effectiveness of power utilization, so that the sensor can better capture the position, speed, shape, and other information of the target, which improves the reliability of environmental sensing. On the other hand, beamforming can be used to suppress interference and enhance the anti-interference capacity, which is very important for some applications in special environments. Over the past few years, there has been a notable surge in research efforts aimed at optimizing ISAC beamforming, resulting in improved quality of ISAC functions. In [51], the authors designed an algorithm to reduce the complexity of beamforming where the two subsystems of communication and radar are co-located on the same BS. The authors of [52] introduced a new symbol-level precoding (SLP) technique into ISAC and studied the related waveform design technology. The principle of SLP based on quadrature phase shift keying is shown in Figure 1. It mainly uses interference signals to make the received signal more conducive to decision-making. The radar beam diagram without modulus length constraints is shown in Figure 2. Simulation results show that both block-level precoding and SLP can achieve better beam gain. In [53], the authors enhanced the beam shape through the optimization of the waveform based on OFDM. The authors of [25] studied DFRC beamforming for MIMO radar sensing and multi-user MIMO (MU-MIMO) communication to propose a generalized beamforming method. In [54], the authors combined space-time adaptive processing (STAP) and SLP to propose a STAP-SLP-based ISAC system. An interrupt-based DFRC beamforming design is proposed [55], which can sense imperfect channel state information to achieve high communication rates and detect passive targets. In [56], a design for block-level beamforming in ISAC systems is proposed, which introduces a finite alphabet input. This approach is more realistic compared to the original infinite-length Gaussian signal. In [57], the authors introduced a new packet SLP strategy and optimal algorithms to address the max-min fair problem and power constraint problem. The authors of [58] studied the problem of maximizing the EE of ISAC systems under the premise that user communication and radar sensing are simultaneously ensured at the BS. A multivariate alternating minimization framework [59] is proposed to address the non-convex issue of optimizing the performance of communication and radar. In [60], an alternating minimization framework is developed to address the problem of minimizing the MSE of the designed beamforming and the target beampattern under the constraints of communication quality of service (QoS). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator** & **OFDM** & **OTFS** \\ \hline BER & It maintains a low BER in conditions of a low SNR scenario. & It maintains a low BER in conditions of a low SNR scenario. \\ \hline Peak-to-average power ratio (PAPR) & High & Lower than OFDM \\ \hline Symbol size & Larger & Smaller \\ \hline Stability & Susceptible to delay and Doppler effects & Utilizing modulation information in the delay-doppler domain exhibits stability \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A brief comparison between OFDM and OTFS signals in ISAC system. In summary, this section investigates the communication and sensing metrics, optimization objectives, and the proposed methods in related studies, which illustrates the research progress of ISAC beamforming at the current stage. The related works are summarized in Table 3. In the optimization for the ISAC waveform, the proposed algorithms simplify the computational complexity of beamforming and optimize the energy distribution to different degrees compared with the previous algorithms. At the same time, the use of new waveforms, such as the introduction of SLP technology, can further reduce the limitations of constraints and lead to more degrees of freedom.