The fifth-generation mobile evolution introduces Next-Generation Radio Access Networks (NG-RAN), splitting the RAN protocol stack into the eight disaggregated options combined into three network units, i.e., Central, Distributed, and Radio. The disaggregated units reach full interoperability on Open RAN. Further advances allow the RAN software to be virtualized (vNG-RAN) on top of general-purpose hardware, enabling the management of disaggregated units and protocols as radio functions. The placement of these functions is challenging since the best decision must be based on multiple constraints, e.g., the RAN protocol stack split, routing paths in network topologies with restricted bandwidth and latency, asymmetric computational resources, etc. The literature does not deal with general placement problems with high functional split options and protocol stack analysis. This article proposes the first exact model for positioning radio functions for vNG-RAN planning, named PlaceRAN, as a Binary Integer Linear Programming (BILP) problem. The objective is to minimize the computing resources and maximize the aggregation of radio functions. The evaluation considered two realistic network topologies, and the results reveal that PlaceRAN achieves an optimized high-performance aggregation level. It is flexible for RAN deployment overcoming the network restrictions, and up to date with the most advanced vNG-RAN design and development.

Openness, virtualization and disaggregation of functions represent the state-of-the-art (SOTA) for optimal management and orchestration of Radio Access Network (RAN) resources. However, in 5G and beyond networks, virtualized RAN (vRAN) functions may commonly share computing resources with Multi-access Edge Computing (MEC) services. This paper introduces a new problem formulation that jointly optimizes vRAN functions and MEC services respecting the maximum acceptable delay of the applications. We show that our model achieves better solutions than a SOTA approach and it is also more flexible. We also present a heuristic solution that is able to achieve near optimal results for real-world networks.

While energy efficiency is an important issue in virtualized RAN due to its impact on OPEX, the centralization level of virtualized RAN functions is another relevant concern that can conflict with the former. In this paper, we introduce a bi-objective problem formulation representing these two objectives and solution strategy based on the ϵ-constraint approach to generate the minimal complete set of Pareto-optimal solutions. We investigate the trade-off between energy efficiency and centralization level in traditional and next-generation RAN topologies. We show scenarios allowing noticeable improvement in the centralization level (e.g., from near 10% to 30%) without impacting energy consumption. However, after a certain value of centralization level, the impact in the energy consumption may become high and hard to justify.

Wireless Mesh Networks (WMN) provide ubiquitous Internet access for mobile users by integrating wired and wireless networks. As gateways towards wired networks are potential bottlenecks, load balancing routing plays a central role in the performance of WMN. With regard to this matter, a number of load balancing routing heuristics have been proposed. However, none of them tackles four key aspects of the load balancing routing problem at the same time, which are achieving low computational costs, and reducing the average path length and routing overhead, while distributing the flows uniformly. To fill this gap, the aim of this paper is to introduce a new load balancing routing heuristic, called BPR - Bottleneck, Path length and Routing overhead, which offers an efficient online solution by taking into consideration all these aspects of the load balancing routing problem. We have carried out a simulation study to compare the performance of BPR with a recent related work. BPR obtains bottleneck values within desirable bounds, while reducing the average path length. As a result, BPR also notably reduces the number of route updates in the network, i.e. the routing overhead. Finally, we show that BPR is simple and has low demand for processing requirements.