# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 6 Conclusions and Future Directions  
We have analyzed and optimized a wireless network scenario in which a GO and a legacy DO communication system coexist, fully sharing the same spectrum resources. While this has been proposed previously for the coexistence of DO and semantic communication systems, we focused on GO communication, also analyzing the effect of communication errors and availability of computing resources toward accomplishing the goal of communication. We first explained the concept of GO communications, and we provided a general problem formulation approach to then tailor it to the proposed system scenario, showing how a GO resource allocation strategy can bring high gains in terms of overall system performance. The latter has been defined through two measures, goal effectiveness and goal cost, with the former translating into probability of confident inference on time, and the latter referring to the performance loss of the legacy system, in terms of data rate. The proposed GO approach is to adapt communication KPIs (i.e., PER) to the actual outcome of communication, measured at the application level. In addition to a first numerical performance evaluation for a non-optimized setting aimed at showing the potential tradeoffs, our proposed optimization leverages application performance measures to update  
Figure 11: Adaptation capabilities of proposed method to compute resource availability (i.e., computing delay). Figure 10: Adaptation capabilities of proposed method to online GO system requirement changes (i.e., \(\Theta_{\text{th}}\) and \(\mathcal{E}_{\delta}\)). suitably defined state variables, whose long-term stability has been exploited to achieve the goal with the lowest cost. After proposing an algorithm with theoretical guarantees, we tested it on the proposed scenario, in which the two systems coexist and interfere with each other. It has been shown, through numerical simulations, the gain, in terms of DO system data rate for a given goal effectiveness of the GO system, of using an adaptive method for both communication reliability (i.e., PER) and legacy system user transmit power (i.e., affecting interference received by goal-oriented system and legacy system user data rate). Moreover, the proposed approach has been tested in non-stationary environments, showing good adaptation capabilities to new requirements and computing resource availability, showing the strong link between communication and computing in future networks. Overall, the paper shows the superiority of a GO selection of communication KPIs, based on communication and computation resource availability, as well as interference. Future steps involve scenarios with multiple GO and legacy system users, the optimization of GO user transmit power and other communication related parameters, cooperative inference, but also other applications beyond edge inference. Finally, an interesting research direction is that of semantic and GO feature extraction into a unified framework in which only the most important features are transmitted and/or protected from wireless errors by the GO users. From a methodological point of view, the exploration of data-driven techniques such as DRL could help in solving the issue of not having a model relating communication reliability (e.g., PER) to the goal value. In this case, this relation would be learnt from experience, by the interaction of the agent(s) with the environment. Further, practical demonstrations of the proposed GO communication paradigm are worth being investigated, with clear challenges related to the validity of the models (including energy consumption, delay and inference confidence), the needed message exchange between agents to enable the proposed scheme and the interaction/interface between network and MEC operators, also from an economic perspective.