# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 1 Introduction
### Related Work  
Although the semantic and GO communication paradigm is in its infancy within the communication community, several works already started investigating the fundamental limits and the algorithmic foundations. In [35], the authors propose an end-to-end semantic communication model, entailing sampling, filtering, preprocessing, reception and control. In [18], a GO data quantization scheme is proposed to reduce the communication overhead of a decision-making service. Similarly, a GO data quantization and data clustering approach is proposed in [20]. In [19], the authors propose an online algorithm able to adapt to channel conditions through the selection of a GO compression scheme in an edge inference scenario. Similarly, reference [21] proposes a GO data compression method based on the information bottleneck principle. The authors of [22] show the gain of a semantic-aware transmission on the reconstruction error of a real-time tracking system, while [23] focuses on the cooperation between agents with a common goal through a task-oriented mutual information exchange. In [24], it is shown how cooperation of different Machine Learning (ML) models can help improve the energy efficiency of an edge inference system with quantized data transmission, and a similar GO resource allocation based on an adaptive selection of JPEG compression is proposed in [25]. In [27], a goal-aware DNN splitting and feature extraction is proposed in an edge inference scenario, while in [28], a unified GO semantic communication framework is proposed in the context of a visual question answering use case. From a GO transmission perspective, the authors of [30] propose a joint transmission and recognition scheme, showing the effect of communication reliability on the accuracy, however not considering delay and energy consumption, as well as cooperative inference. Moreover, in our precursor conference paper [31], we investigate the performance of communication KPIs (i.e., the bit error rate) on a real-time edge inference task, taking into account classification accuracy, energy and delay. Furthermore, reference [34] introduces, for the first time, the concept of coexistence between semantic and DO services, with a performance evaluation aimed to achieve sum-rate maximization of DO users, with a minimum required SNR constraint for semantic users, with mutual interference. However, the authors of [34] do not consider goal-effectiveness constraints in the presence of interfering DO users and do not propose a GO optimization, which is the purpose of this paper for an edge inference use case.  
Finally, references [32, 36], which are the most closely related works, exploit semantic communication for Non-Orthogonal Multiple Access (NOMA) by considering a system with a primary and a secondary user served by the same Access Point (AP) through NOMA. The authors show the gain of defining a semantic ergodic rate in the overall system performance, with the aim of maximizing the primary (i.e., bit-level communication) user data rate, under a semantic ergodic rate constraint of the secondary user, controllingtransmit power and transmission time scheduling. Starting from the research question of [32; 36], we investigate a novel scenario, in which a GO and a DO communication system coexist, thus focusing on GO, rather than semantic, communication.