Learning the site-specific distribution of the wireless channel within a particular environment of interest is essential to exploit the full potential of machine learning (ML) for wireless communications and radar applications. Generative modeling offers a promising framework to address this problem. However, existing approaches pose unresolved challenges, including the need for high-quality training data, limited generalizability, and a lack of physical interpretability. To address these issues, we combine the physics-related compressibility of wireless channels with generative modeling, in particular, sparse Bayesian generative modeling (SBGM), to learn the distribution of the underlying physical channel parameters. By leveraging the sparsity-inducing characteristics of SBGM, our methods can learn from compressed observations received by an access point (AP) during default online operation. Moreover, they are physically interpretable and generalize over system configurations without requiring retraining.

Uplink receiver cooperation of base stations is investigated and compared to hybrid ARQ from a system level's point of view. The cooperation of receivers is performed by a common detection entity, receiving signals from different base stations. We review an iterative joint detector, delivering the applied link level results. These results are used to compare receiver cooperation with conventional hybrid ARQ. Simulation results show remarkable gains for receiver cooperation compared to non-cooperative receivers with hybrid ARQ, in terms of throughput and number of users that can be supported. It even turns out that, due to its better performance, receiver cooperation has a lower resulting additional delay than hybrid ARQ in some SNR-regions. We further derive the necessary capacity on the cooperation links, to make receiver cooperation possible.

Recently, distributed decoding was introduced, where multi-user detection is performed across the cell-boundary. Thereby, base-stations cooperate and exchange information in order to cancel intercell interference. Alternatively, a joint detector can be applied. This work extends the idea of distributed decoding to a joint distributed decoding combined with turbo equalization: The Distributed Turbo Equalization (DTE). Thereby, both intercell and intersymbol interference are canceled simultaneously. We present several strategies how base-stations can cooperate from a network's point of view in order to allow distributed turbo equalization. For different cooperation schemes, diverse equalization strategies are presented. Simulation results show remarkable performance gains compared to a non- cooperating system.

Interference cancellation is an important issue since the coverage and capacity in the uplink are mainly interference limited. The interference from the own cell can be mitigated by multiuser detection. However, there does not exist a powerful interference cancellation technique for the interference from neighboring cells, the intercell interference. This work proposes a technique for combating the intercell interference. Since base-stations in wireless systems are connected through a backbone network, we propose the exchange of information through the network between the base-stations, in order to help each other in the detection of the signals. We present several strategies how this could be performed. Especially, we propose an iterative way for reducing the intercell interference: The Turbo Base-Station Cooperation. Simulation results show remarkable gains, especially in the presence of a strong interferer.