# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 2 Definition of a Goal  
The GO communication paradigm constitutes a communication approach for which performance is not measured by classical metrics, such as data rate or wireless link reliability, but rather by the success level with which a network entity is able to perform a sequence of application-related tasks, as a result of exchanging data with one or more other network entities. In the sequel, we will also refer to these entities as _agents_. In this context, an agent is any entity endowed with communication capabilities, which could also have computing capabilities and be possibly embarked with Artificial Intelligence (AI) and/or ML models.  
\begin{table}
\begin{tabular}{l l l l} \hline AI & Artificial Intelligence & GPU & Graphical Processing Unit \\ \hline AP & Access Point & KPI & Key Performance Indicator \\ \hline CLD & Conditional Lyapunov Drift & KVI & Key Value Indicator \\ \hline CNN & Convolutional Neural Network & MEC & Mutli-access Edge Computing \\ \hline CPU & Central Processing Unit & MEH & Mobile Edge Host \\ \hline DNN & Deep Neural Network & MCS & Modulation and Coding Scheme \\ \hline DO & Data-Oriented & ML & Machine Learning \\ \hline DPP & Drift Plus Penalty & NREI & Negative Relative average Entropy Increase \\ \hline DRL & Deep Reinforcement Learning & NOMA & Non-Orthogonal Multiple Access \\ \hline E2E & End-to-End & PER & Packet Error Rate \\ \hline eMBB & enhanced Mobile Broad Band & SINR & Signal-to-Interference-plus-Noise Ratio \\ \hline GO & Goal-Oriented & UE & User Equipment \\ \hline \end{tabular}
\end{table}
Table 1: List of acronyms.  
In the latter case, we can refer to it as an _AI agent_. However, this distinction is not essential in this paper.  
A fundamental step is to formally define a goal to be able to properly formulate GO communication problems. Indeed, a wrong, inexact, unclear or ambiguous definition of a goal may lead to dramatically wrong or biased decisions and communication/computation policies.  
In [37], [PERSON] defines a goal out of the context of wireless communications. From its definitions, and with the aim of embedding the GO perspective into wireless communication systems, in the most general case, a goal is characterized by a set of requirements that, if guaranteed, determine its accomplishment. When embedded in wireless scenarios with inter-agent communications, the accomplishment of these requirements is directly linked to communication (and computation)-related performance and strategies. As an example, suppose that the goal of the communication is to exchange sampled data for anomaly detection. Then, data corrupted by a noisy (and possible interference-prone) channel could lead to wrong decisions, depending on the wireless link reliability (e.g., the PER). At the same time, data encoding schemes (e.g., compression) affect the goal achievement due to distortion. Both data encoding and wireless link reliability play a key role in GO communications. A priori, it is not generally easy to predict the exact correlation between goal accomplishment and wireless communication performance, data encoding, etc. This depends on several aspects, including the goal definition, the connect-compute network conditions and the a priori knowledge of communicating agents. Learning and timely adapting communication parameters towards goal accomplishment is the main target of the GO communication paradigm. As also remarked in [37], a goal is achieved through a series of tasks, with a task being a collection of actions. Generally, an action entails a decision on a set of parameters, which could involve communication, computation and possibly control. Let us formally define the three measures that characterize a goal and are necessary to formulate GO communication problems.