# Semantic Communication on Digital Wireless Communication Systems
## 2 Bit-Conversion-Based JSCC Transmission Framework and Simulator
### Simulation Platform for E2E Semantic Communication  
There is a common issue in the semantic communication simulation tools, which is that traditional simulation tools implement complete physical layer processing but do not transmit the real payload data; random bits are used as the payload for transmission. In contrast, numerical simulations mentioned in most semantic communication papers emphasize the encoding and decoding algorithms, while the physical layer processing is relatively simplified and cannot reflect the impact of complete physical layer algorithms on semantic transmission performance.  
To solve this problem, based on the transmission frameworks we designed, we develop a semantic communication simulation platform. The software architecture of the simulation platform is illustrated in Figure 5. By configuration, this simulation platform can support the semantic SSCC and JSCC transmission schemes, as well as the traditional bit transmission. And this is different from most of the traditional physical layer simulation systems or semantic communication simulation systems at present; with the adaption module, we can simulate the transmission of the real payload on the physical layer, not only of random bits.  
Figure 4: E2E procedure for bit-conversion-based SSCC transmission framework.  
Figure 5: Software architecture of the simulation platform.  
The simulation procedure is illustrated in Figure 6. To be specific, the input original data are firstly processed by the semantic or source encoder. After that, the encoded data proceeds to execute the remaining physical layer functions. Then, differences in the data files can be received with customized configurations (e.g., JSCC, SSCC, modulation,......) under changing channel conditions. Finally, the semantic decoder outputs the final result and compares this with the source files to calculate the semantic key performance indicators (KPIs). In addition, the physical KPIs can also be obtained during the simulation procedure.  
Taking image transmission as an example, we simulate the semantic transmission with QPSK/16 QAM/64 QAM using both the JSCC and SSCC transmission schemes in this simulation platform. For the SSCC transmission scheme, a 0.5 code rate LDPC is adopted. And for JSCC, there is no channel coding. A layer-based semantic communication system for images (LSCI) is chosen for the semantic coding algorithm [25].  
It is observed from simulation results that, as shown in Figure 7a,c,d, for SSCC, when the SNR is in the range where LDPC can work, there is no bit error and the PSNR and SSIM maintain perfect performance as well. Below a certain SNR threshold, LDPC could not correct the bit error and suffers from the \"Cliff Effect\". The BER, PSNR, and SSIM decrease sharply. For JSCC, since it does not include channel coding, few error bits exist, even in a good SNR range, but PSNR and SSIM perform well. The BER, PSNR, and SSIM decrease smoothly when the SNR decreases; thus, the PSNR of JSCC can remain acceptable in an SNR range lower than the \"Cliff Effect\" point of SSCC. At the same time, JSCC can achieve two times the spectrum efficiency of SSCC with different modulation orders, as shown in Figure 7b, since there is no channel coding overhead.  
Together with other papers [14; 20], the results obtained from our semantic simulation platform show the same trend of the semantic transmission of JSCC and SSCC. This demonstrates the accuracy of this simulation platform. Furthermore, we can leverage this simulation platform to study the impact of physical layer algorithms and semantic algorithms on the end-to-end semantic communication system.  
Figure 6: Semantic signal processing flow of simulation platform.