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.

The fifth generation new radio (5G NR) technology is expected to fulfill reliable and accurate positioning requirements of industry use cases, such as autonomous robots, connected vehicles, and future factories. Starting from Third Generation Partnership Project (3GPP) Release-16, several enhanced positioning solutions are featured in the 5G standards, including the multi-cell round trip time (multi-RTT) method. This work presents a novel framework to estimate the round-trip time (RTT) between a user equipment (UE) and a base station (gNB) in 5G NR. Unlike the existing scheme in the standards, RTT can be estimated without the need to send timing measurements from both the gNB and UE to a central node. The proposed method relies on obtaining multiple coherent uplink wide-band channel measurements at the gNB by circumventing the timing advance control loops and the clock drift. The performance is evaluated through experiments leveraging a real world 5G testbed based on OpenAirInterface (OAI). Under a moderate system bandwidth of 40MHz, the experimental results show meter level range accuracy even in low signal-to-noise ratio (SNR) conditions.

Network slicing allows Telecom Operators (TOs) to support service provisioning with diverse Service Level Agreements (SLAs). The combination of network slicing and Open Radio Access Network (RAN) enables TOs to provide more customized network services and higher commercial benefits. However, in the current Open RAN community, an open-source end-to-end slicing solution for 5G is still missing. To bridge this gap, we developed ORANSlice, an open-source network slicing-enabled Open RAN system integrated with popular open-source RAN frameworks. ORANSlice features programmable, 3GPP-compliant RAN slicing and scheduling functionalities. It supports RAN slicing control and optimization via xApps on the near-real-time RAN Intelligent Controller (RIC) thanks to an extension of the E2 interface between RIC and RAN, and service models for slicing. We deploy and test ORANSlice on different O-RAN testbeds and demonstrate its capabilities on different use cases, including slice prioritization and minimum radio resource guarantee.

In this work, we develop an algorithm to construct radio maps that can predict the received signal strength between a UAV-mounted base station and arbitrary ground users. The novelty of the work lies in the fact that these maps are constructed by fusing UAV-user radio signal strength measurements, and depth information of the surrounding environment which is obtained by an on-board laser range finder sensor. The proposed approach exploits both line-of-sight (LoS) and non-line-of-sight (NLoS) nature of UAV-user channels and depth information to first obtain the 3D map of the city and then later use it to estimate the radio map. Numerical results demonstrate the significant gain brought by the fusion of radio and depth measurements as opposed to a system which only relies on radio measurements.

This article considers the problem of localizing outdoor ground radio users with the help of an unmanned aerial vehicle (UAV) on the basis of received signal strength (RSS) measurements in an urban environment. We assume that the propagation model parameters are not known a priori, and depending on the UAV location, the UAV-user link can experience either Line-of-Sight (LoS) or Non-Line-of-Sight (NLoS) propagation condition. We assume that a 3-D map of the environment is available which the UAV can exploit in the localization process. Based on the proposed map-aided estimator, we devise an optimal UAV trajectory to accelerate the learning process under a limited mission time. To do so, we borrow tools, such as Fisher information from the theory of optimal experiment design. Our map-aided estimator achieves superior localization accuracy compared to the map-unaware methods, and our simulations show that optimized UAV trajectory achieves superior learning performance compared to random trajectories.

Cellular connected unmanned aerial vehicles (UAVs) that can operate safely in beyond visual line of sight conditions are expected to open important future opportunities in the areas of transportation, goods delivery, and system monitoring. A key challenge in this area lies in the design of trajectories which, while allowing the completion of the UAV mission, can guarantee reliable cellular connectivity all along the path. Previous approaches in this domain have considered either simplistic propagation model assumptions (e.g. Line of Sight based) or more advanced models but with computationally demanding optimization solutions. In this paper, we propose a novel approach for trajectory design using a coverage map that can be obtained with a combination of a 3D map of the environment and radio propagation models. Leveraging on the convexity of subregions within the coverage map, we propose a low-complexity graph based algorithm which is shown to achieve quasi-optimal performance at a fraction of the computational cost of known optimal methods.

We consider a scenario where an unmanned aerial vehicle (UAV)-mounted flying base station is providing data communication services to a number of radio nodes spread over the ground. We focus on the problem of resource-constrained UAV trajectory design with: 1) optimal channel parameters learning and 2) optimal data throughput as key objectives, respectively. While the problem of throughput optimized trajectories has been addressed in prior works, the formulation of an optimized trajectory to efficiently discover the propagation parameters has not yet been addressed. When it comes to the communication phase, the advantage of this paper comes from the exploitation of a 3-D city map. Unfortunately, the communication trajectory design based on the raw map data leads to an intractable optimization problem. To solve this issue, we introduce a map compression method that allows us to tackle the problem with standard optimization tools. The trajectory optimization is then combined with a node scheduling algorithm. The advantages of the learning-optimized trajectory and of the map compression method are illustrated in the context of intelligent Internet of Things data harvesting.

We determine the achievable distortion region when the correlated source samples are transmitted by two energy harvesting (EH) sensor nodes to the destination over orthogonal fading channels. A time slotted system is considered in which the energy and the source samples arrive at the beginning of each time slot (TS), and both the correlation between source samples at the two nodes and fading coefficients change over time but remain constant in each TS. Assuming non-causal knowledge of these time-varying source statistics, energy arrivals and the channel gains, i.e., under the offline optimization framework, we obtain the optimal transmission and coding schemes that achieve the points on the Pareto boundary of the total distortion region. An iterative directional 2D waterfilling algorithm is proposed to obtain two specific points on this boundary.

Optimization of a point-to-point (p2p) multiple-input single-output (MISO) communication system is considered when both the transmitter (TX) and the receiver (RX) have energy harvesting (EH) capabilities. The RX is interested in feeding back the channel state information (CSI) to the TX to help improve the transmission rate. The objective is to maximize the throughput by a deadline, subject to the EH constraints at the TX and the RX. The throughput metric considered is an upper bound on the ergodic rate of the MISO channel with beamforming and limited feedback. Feedback bit allocation and transmission policies that maximize the upper bound on the ergodic rate are obtained. Tools from majorization theory are used to simplify the formulated optimization problems. Optimal policies obtained for the modified problem outperform the naive scheme in which no intelligent management of energy is performed.

This work considers the benefits of allowing spectrum sharing among co-located wireless service providers operating in the same multicell network. Although spectrum sharing was shown to be valuable in some scenarios where the created interference can be eliminated, the benefits have not clearly shown for multicell networks with aggressive reuse. We explore this question and show that spectrum sharing is preferred for just a certain subset of the users defined by their distance from the serving bases, while beyond this distance, an orthogonal division of resources between operators gives better results. The claims are backed with theoretical analysis matching our simulations.