# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 1 Introduction  
Today, as the fifth mobile generation (5G) is at its deployment stage ([https://www.3 gpp.org/technologies/5g-system-overview](https://www.3 gpp.org/technologies/5g-system-overview) accessed on 20 July 2023), the race to 6G has started all around the world [1, 2]. The latter entails theoretical research of new technologies' fundamental limits, the definition of new cutting edge use cases [3, 4], their associated Key Performance Indicators (KPIs) and Key Value Indicators (KVIs) and, obviously, the new technological levers to enable the ecosystem. New technological enablers are required at all layers of the protocol stack, from application and network to the physicallayer [5, 6], and also down to the wireless propagation environment with reconfigurable intelligent surfaces [7, 8, 9]. This cross-layer perspective will give birth to a fully reconfigurable system that can adapt to extremely diverse requirements, with a joint orchestration of heterogeneous resources (wireless, computing, storage). Following the revolution provided by Multi-access Edge Computing (MEC) [10], a paradigm shift in 6G will be the integration of communication, computing and storage, natively enabling _heterogeneous intelligence_ (e.g., humans, robots or machines) to accomplish complex cooperative tasks [11, 12, 13, 14, 15]. Connecting intelligence requires informative cooperative interactions. New trends in 6G explore both the _semantic problem_ of understanding the meaning of the source data and the _effectiveness problem_, whose aim is accomplishing target goals through possibly corrupted/compressed/encoded/misunderstood received information [16]. Achieving goals with target effectiveness requirements defines a new family of KPIs in future 6G systems. We refer to such family of KPIs as _goal effectiveness_. This paper explores as main KPIs the _goal effectiveness_ together with its associated _goal cost_, which refers to the price to pay to achieve a goal. Goal effectiveness is one of the main KPIs to assess the performance of _goal-oriented (GO) communication_[16, 17], a true paradigm shift from _data-oriented_ (DO) communication [11], envisioned to proliferate in 6G. While the aim of DO communications is for an intended data destination to reliably receive the original data generated by a data source, the aim of GO communications is rather for the data destination (playing the role of an acting agent) to receive adequate data of sufficient quality in the context of correctly performing a (possibly cooperative) task. Indeed, in our view, GO communications can refer to:  
* _goal-oriented data compression_[11, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28], aiming at extracting and adapting the relevant information needed to make the receiver accomplish a goal with desired effectiveness. This can be based on semantic information extraction [22, 28, 29], but it is not restricted to the meaning of data;
* _goal-oriented transmission_[30, 31, 32], aiming at adapting communication reliability (e.g., the Packet Error Rate--PER), to achieve target goal effectiveness, i.e., the probability of achieving the goal. This can also involve semantic-aware packet protection, under the assumption that some packets bring more relevant information than others, from the perspective of the goal. Both concepts above also entail the definition of costs, which can include, among the others, energy consumption, delay, resource utilization and communication overhead. Obviously, a combination of both approaches is possible and would represent a unified view towards a fully GO system design. As a second aspect tackled in this paper, a fundamental challenge of future 6G systems is to accommodate, on the same network infrastructure, services that are heterogeneous from several perspectives: requirements, resource consumption, time-variability, etc.