# Semantic Communication on Digital Wireless Communication Systems
## 1 Introduction  
In recent years, semantic communication, as one of the potential key technologies for 6G mobile communications, has garnered widespread attention from academia and industry. It is anticipated that semantic communication will become a novel paradigm in the development of end-to-end communication systems for 6G mobile communications [1, 2, 3, 4]. In the research on semantic communication, many end-to-end transmission frameworks have been proposed [5, 6, 7, 8, 9, 10]. The design of these end-to-end transmission frameworks all involve the same issue: the output of semantic encoding based on neural networks (NNs) is a continuous signal, leading to the following question: how can it be transmitted via existing wireless communication systems? To solve this issue, current raised frameworks can be divided into two main approaches. One approach is to change the existing system's modulation method to analog modulation [8, 9, 10, 11, 12, 13]. The advantage of this approach is that it can preserve the information after semantic encoding and reduce quantization error. However, the disadvantage is that it is extremely different from the existing systems, requiring significant changes to both hardware equipment and software protocols. The other approach still utilizes digital modulation, quantizing the output after semantic encoding before transmission [5; 6; 7; 14; 15; 16]. We can call it the quantized approach. The disadvantage of the quantized approach is that, from a conventional perspective, quantization tends to introduce certain performance losses. And the magnitude of the performance loss depends on different quantization methods [17; 18]. Conversely, the advantage of the quantized approach is that its compatibility with existing system protocols is convenient. Moreover, there are currently many studies on quantization methods for semantic communication to improve the performance of quantization [5; 14; 15; 16; 19]. Comprehensively, the quantized approach is a more feasible path for the evolution of semantic communication systems onexisting systems. Furthermore, many transmission schemes have been proposed within the quantized approach, which can be divided into two major categories based on whether traditional channel coding is utilized. One category of the schemes still adopts the separate source-channel coding (SSCC) scheme, called the SSCC transmission scheme [1; 6; 7]. The transmission framework of this scheme is illustrated in Figure 1a. It is only necessary to transmit the content of semantic encoding as the payload data of the existing system, without any modification of the existing wireless communication systems needed. However, the disadvantage of the SSCC transmission scheme is the so-called \"Cliff Effect\". This refers to a sharp decline in distortion or decoding accuracy when the channel quality drops below a certain quality threshold. This issue has been mentioned in many studies on semantic communication, and corresponding references have been provided [14; 20; 21; 22]. The other category of the transmission schemes eliminate traditional channel coding replacing with the JSCC (joint source-channel coding) scheme, called the JSCC transmission scheme. For example, the JSCC image transmission schemes were proposed by [PERSON], [PERSON], and others [14; 20]; The JSCC video transmission schemes were proposed by [PERSON], [PERSON], and others [23; 24]; The speech transmission schemes were proposed by [PERSON] and others [8]. All these schemes have demonstrated that the JSCC transmission scheme for semantic communication can avoid the \"Cliff Effect\" and save transmission bandwidth, compared to the SSCC transmission scheme. However, since the JSCC transmission scheme removes traditional channel coding, the existing wireless communication systems are modified. The extent of the impact depends on the specific function designs.