Afsaneh Gharouni

• Machine Learning (ML) is expected to become an integrated part of future mobile networks due to its capacity for solving complex problems. During inference, ML algorithms extract the hidden knowledge of their input data which is delivered to them through wireless links in many scenarios. Transmission of a massive amount of such input data can impose a huge burden on the mobile network. On the other hand, it is known that ML algorithms can tolerate different levels of distortion on their input components, while the quality of their predictions remains unaffected. Therefore, utilization of the conventional approaches implies a waste of radio resources, since they target an exact reconstruction of transmitted data, i.e., the input of ML algorithms. In this thesis, we propose a novel relevance based framework that focuses on the quality of final ML outputs instead of such syntax based reconstruction of transmitted inputs. To this end, we quantify the semantics or relevancy of input components in terms of the bit allocation aspect of data compression, where a higher tolerance for distortion implies less relevancy. A lower relevance level is translated into the allocation of less radio resources, e.g., bandwidth. The introduced formulation provides the foundations for the efficient support of ML models with their required data in the inference phase, while wireless resources are employed efficiently. In this dissertation, a generic relevance based framework utilizing the Kullback-Leibler Divergence (KLD) is developed that is applicable to many realistic scenarios. The system model under study contains multiple sources transmitting correlated multivariate input components of a ML algorithm. The ML model is seen as a black box, which is trained and has fixed parameters while operating in the inference phase. Our proposed bit allocation accounts for the rate-distortion tradeoff. Hence, it is simply adjustable for application to other problems. Here, an extended version of the proposed bit allocation strategy is introduced for signaling overhead reduction, in which the relevancy level of each input attribute changes instantaneously. In another expansion, to take the effect of dynamic channel states into account, a resource allocation approach for ML based centralized control systems is proposed. The novel quality of service metric takes outputs of ML algorithms into consideration, and in combination with the designed greedy algorithm, provides significantly improved end-to-end performance for a network of cart inverted pendulums. The introduced relevance based framework is comprehensively investigated by considering various case studies, real and synthetic data, regression and classification, different estimators for the KLD, various ML models and codebook designs. Furthermore, the reliability of this proposed solution is explored in presence of packet drops, indicating robustness of the relevance based compression. In all of the simulations, the relevance based solutions deliver the best outcome in terms of the carefully chosen key performance indicators. In most of them, significantly high gains are also achieved compared to the conventional techniques, motivating further research on the subject.