The optimization of Precoding Matrix Indicators (PMIs) is crucial for enhancing the performance of 5G networks, particularly in dense deployments where inter-cell interference is a significant challenge. Some approaches have leveraged Artificial Intelligence (AI)/Machine Learning (ML) techniques for beamforming and beam selection, however, these methods often overlook the multi-objective nature of PMI selection, which requires balancing Spectral Efficiency (SE) and interference reduction. This paper proposes an interference-aware PMI selection method using an Advantage Actor-Critic (A2C) reinforcement learning model, designed for deployment within an Open Radio Access Network (O-RAN) framework as an xApp. The proposed model prioritizes User Equipment (UE) based on a novel strategy and adjusts PMI values accordingly, with interference management and efficient resource utilization. Experimental results in an O-RAN environment demonstrates the efficacy of this method in improving network performance metrics, including SE and interference mitigation.

Traffic steering is an essential aspect of the radio access network (RAN). The O-RAN Alliance architectural framework provides an environment for intelligent traffic steering using different AI/ML techniques. In this work, we present an xApp in the RAN intelligence controller (RIC) for traffic steering and load balancing to ensure that the user equipment (UE) achieves an acceptable throughput. We present a K-means learning clustering technique on the UEs based on the throughput and quality of service metrics. The clustering technique is used to determine UEs experiencing low throughput for handover. Cell throughput prediction is then performed using long-short-term memory (LSTM) to predict the average cell throughput generated by the individual cells. A steering algorithm is developed to select UEs for handover. A Handover request message is generated and then sent to the E2 nodes. The achieved results demonstrate the effectiveness of our proposed algorithm with a significant gain in throughput and a fair distribution of UEs among cells.

Assisted driving for connected cars is one of the main applications that 5G-and-beyond networks shall support. In this work, we propose an assisted driving system leveraging the synergy between connected vehicles and the edge of the network infrastructure, in order to envision global traffic policies that can effectively drive local decisions. Local decisions concern individual vehicles, e.g., which vehicle should perform a lane-change manoeuvre and when; global decisions, instead, involve whole traffic flows. Such decisions are made at different time scales by different entities, which are integrated within an edge-based architecture and can share information. In particular, we leverage a queuing-based model and formulate an optimization problem to make global decisions on traffic flows. To cope with the problem complexity, we then develop an iterative, linear-time complexity algorithm called Bottleneck Hunting (BH). We show the performance of our solution using a realistic simulation framework, integrating a Python engine with ns-3 and SUMO, and considering two relevant services, namely, lane change assistance and navigation, in a real-world scenario. Results demonstrate that our solution leads to a reduction of the vehicles’ travel times by 66 percent in the case of lane change assistance and by 20 percent for navigation, compared to traditional, local-coordination approaches.

Automotive services for connected vehicles are one of the main fields of application for new-generation mobile networks as well as for the edge computing paradigm. In this paper, we investigate a system architecture that integrates the distributed vehicular network with the network edge, with the aim to optimize the vehicle travel times. We then present a queue-based system model that permits the optimization of the vehicle flows, and we show its applicability to two relevant services, namely, lane change/merge (representative of cooperative assisted driving) and navigation. Furthermore, we introduce an efficient algorithm called Bottleneck Hunting (BH), able to formulate high-quality flow policies in linear time. We assess the performance of the proposed system architecture and of BH through a comprehensive and realistic simulation framework, combining ns-3 and SUMO. The results, derived under real-world scenarios, show that our solution provides much shorter travel times than when decisions are made by individual vehicles.

Given the proven benefits cell densification brings in terms of capacity and coverage, it is certain that 5G networks will be even more heterogeneous and dense. However, as smaller cells are introduced in the network, interference will inevitably become a serious problem as they are expected to share the same radio resources. Another central feature envisioned for future cellular networks is carrier aggregation (CA), which allows users to simultaneously use several component carriers of various widths and frequency bands. By exploiting the diversity of the different carriers, CA can also be used to effectively mitigate the interference in the network. In this paper, we leverage the above key features of next-generation cellular networks and formulate a downlink power setting problem for the different available carriers. Using game theory, we design a distributed algorithm that lets cells dynamically adjust different transmit powers for the different carriers. The proposed solution greatly improves network performance by reducing interference and power consumption, while ensuring coverage for as many users as possible. We compare our scheme with other interference mitigation techniques, in a realistic large-scale scenario. Numerical results show that our solution outperforms the existing schemes in terms of user throughput, energy, and spectral efficiency.

Carrier aggregation, which allows users to aggregate several component carriers to obtain up to 100 MHz of bandwidth, is one of the central features envisioned for next generation cellular networks. While this feature will enable support for higher data rates and improve quality of service, it may also be employed as an effective interference mitigation technique, especially in multi-tier heterogeneous networks. Having in mind that the aggregated component carriers may belong to different frequency bands and, hence, have varying propagation profiles, we argue that it is not necessary, indeed even harmful, to transmit at maximum power at all carriers, at all times. Rather, by using game theory, we design a distributed algorithm that lets eNodeBs and micro base stations dynamically adjust the downlink transmit power for the different component carriers. We compare our scheme to different power strategies combined with popular interference mitigation techniques, in a typical large-scale scenario, and show that our solution significantly outperforms the other strategies in terms of global network utility, power consumption and user throughput.

Optimal resource allocation in LTE networks is known to be a hard problem, and is further exacerbated when support for advanced features such as heterogeneity and carrier aggregation are also considered. In particular, in LTE heterogeneous networks (HetNets) where radio resources are shared between different layers of base stations, interference management can be a daunting task. Carrier aggregation (CA), which allows the simultaneous use of several LTE component carriers to achieve high user data rates, also adds to the complexity. In this paper, we propose an interference-aware heuristic algorithm that jointly performs carrier selection and resource allocation to serve a mix of users with CA-enabled and legacy terminals. We evaluate the performance of our approach in a large-scale scenario and compare it with other widely used heuristic algorithms such as Proportional-Fair scheduling and Enhanced Inter Cell Interference Coordination (eICIC) techniques. Simulation results show that the solution we propose increases system throughput, minimises energy consumption and improves spectrum utilisation, while also ensuring better fairness between CA-enabled and legacy user terminals.