Open Radio Access Networks (RANs) leverage disaggregated and programmable RAN functions and open interfaces to enable closed-loop, data-driven radio resource management. This is performed through custom intelligent applications on the RAN Intelligent Controllers (RICs), optimizing RAN policy scheduling, network slicing, user session management, and medium access control, among others. In this context, we have proposed dApps as a key extension of the O-RAN architecture into the real-time and user-plane domains. Deployed directly on RAN nodes, dApps access data otherwise unavailable to RICs due to privacy or timing constraints, enabling the execution of control actions within shorter time intervals. In this paper, we propose for the first time a reference architecture for dApps, defining their life cycle from deployment by the Service Management and Orchestration (SMO) to real-time control loop interactions with the RAN nodes where they are hosted. We introduce a new dApp interface, E3, along with an Application Protocol (AP) that supports structured message exchanges and extensible communication for various service models. By bridging E3 with the existing O-RAN E2 interface, we enable dApps, xApps, and rApps to coexist and coordinate. These applications can then collaborate on complex use cases and employ hierarchical control to resolve shared resource conflicts. Finally, we present and open-source a dApp framework based on OpenAirInterface (OAI). We benchmark its performance in two real-time control use cases, i.e., spectrum sharing and positioning in a 5th generation (5G) Next Generation Node Base (gNB) scenario. Our experimental results show that standardized real-time control loops via dApps are feasible, achieving average control latency below 450 microseconds and allowing optimal use of shared spectral resources.

We consider robust multi-group multicast beamforming design in massive multiple-input multiple-output (MIMO) large-scale systems. The goal is to minimize the transmit power subject to the minimum signal-to-interference-plus-noise-ratio (SINR) targets under channel uncertainty. Using the exact worst-case SINR constraints, we transform the problem into a non-convex optimization problem. We develop an alternating direction method of multipliers (ADMM)based fast algorithm to solve this problem directly with convergence guarantee. Our two-layer ADMM-based algorithm decomposes the non-convex problem into a sequence of convex subproblems, for which we obtain the semi-closed-form or closed-form solutions. Simulation studies show that our algorithm provides a considerable computational advantage over the conventional interior-point method non-convex solver with nearly identical performance.