# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 1 Introduction
### Our Contribution  
Similarly to [32; 36], in this paper, we aim at analyzing the impact of mutual interference on future service classes. Differently from [32; 36], whose focus is on semantic communication and ergodic (semantic) rate, and where a single base station serves users with bit and semantic streams, we rather focus on GO communication and resource allocation in connect-compute networks, in which a GO and a DO communication system coexist. Therefore, the main contributions of our work are the following:  
* We consider the communication effectiveness in accomplishing a predefined goal/task as our system constraint. Therefore, the focus of our work is on GO communications (i.e., effectiveness of communication toward achieving a goal--and in particular through resource allocation) rather than semantic communications (i.e., understanding the meaning of data).
* We introduce the edge inference service as the main use case under investigation, defining it as a GO communication service and problem.
* We consider the communication reliability (i.e., Packet Error Rate--PER) as a variable to be controlled to achieve target goal effectiveness at the GO system while maximizing performance of the DO system in terms of data rate.
* We propose a _computation resource-aware_ method for guaranteeing goal effectiveness, taking into account the computing resource availability for edge inference.  
In other words, our work is about goal-aware system coexistence, while [32; 36] are about a multiple access scheme where different traffic types (semantic, bit-oriented) are multiplexed by a single AP that can process both sorts of data.  
Then, as a first step of this work, we formally define a GO resource optimization problem in the context of wireless communication systems. This will help us to introduce, in the sequel, the fundamental problem of coexistence of GO and DO systems in 6G, with full spectrum sharing. As a specific use case, we consider an edge inference service for the GO system, in which an end device uploads data to a MEH, which runs a classification task through a pre-trained and pre-uploaded ML model (here, a Convolutional Neural Network--CNN). In such a system, we will define goal effectiveness and goal cost, with the former relating to the probability of receiving confident classification results within a predefined deadline and the latter relating to the data rate loss of the DO user, compared to the case in which the GO user is absent. This bonds interference, communication reliability, computing resources, goal effectiveness and goal cost into a unified framework. To the best of our knowledge, this has never been studied before in the literature for GO communication and edge inference scenarios jointly factoring in wireless and compute resources.  
After a performance evaluation of the proposed scenario, aiming to gain insights about the system behavior as a function of different free system parameters, we will propose a simple yet relevant optimization problem, along with an adaptive algorithm able to optimize radio resources to minimize the goal cost, under goal-effectiveness constraints, with a connect-compute resources-aware approach. In summary, the main novelty of the paper consists of conducting a GO performance analysis of an edge inference system sharing resources with a DO system, along with an adaptive algorithm to attain desired performance, being aware of application constraints, its online performance, as well as connect-compute resource availability. After proposing an algorithm with theoretical guarantees, we test it on scenarios with different parameters and requirements, against baseline strategies (e.g., orthogonal bandwidth splitting), but also on non-stationary environments in which GO requirements (e.g., goal effectiveness) or computation resource availability statistics can unexpectedly change over time.