# Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
## 3 Problem Definition
### System Model  
The scope of this work is focused on how radio resources can be reused for V2V communications underlying cellular network, where in \(M\) V2V communications coexist with \(N\) cellular C-UE communications. In this system, C-UEs and V-UEs share the available uplink resources and only V-UEs utilize D2D communications. Through our research, we propose to share uplink resources as the interference generated on the cellular links affects only to the eNodeB-side which is assumed to possess advanced signal processing capabilities to mitigate such detrimental phenomena. Furthermore, the interference suffered by the V2X links can be handled through spatial separation. On the other hand, if the downlink reuse was implemented, the transmission from the eNodeB could have serious negative impacts on the V-UE receivers, because it could violate the reliability requirement of safety applications, which are the main focus of this paper. Additionally, most of the existing studies consider the UL (uplink) resources to support D2D communications since there are some regulatory restrictions in certain regions for reusing the DL (downlink) resources [26]. The proposed system, shown in Figure 1, can be decomposed into three main components: (i) users; (ii) scenario and (iii) network systems, whose properties vary over a number of dimensions. There are two different type of users: (1) C-UEs which are the typical cellular users and (2) V-UEs which are the vehicles. These users move in the given scenario and have access to network infrastructure resources, called RBs (Resource Blocks). They use these RBs to send their messages to other users but, depending on the type of user and the type of message, they send them though the network infrastructure or directly, by means of V2V communication link. One feature of our proposal is that V-UEs are organized into clusters. Considering that clustering is a technique for grouping devices in the geographical vicinity to make the network more robust and scalable, it is very common in VANETs to find vehicles organized in clusters for deploying ITS applications. These clusters are usually defined based on some common characteristics such as vehicle movement, velocity, direction or transmission range, among others [27]. In our proposed method, as the clustering creation is not the aim of our research, we define a simple centralized clustering algorithm based on the vehicle with the highest number of neighbor  
Figure 1: Overview of the system. vehicles, meaning a neighboring vehicle in its coverage area. The steps to define the CHs, and then, the CMs, are represented by means of Algorithm 1. Therefore, using the information about the V-UEs's position which is known by a central server (called GeoServer), our algorithm organizes V-UEs into clusters. Each cluster is formed by one CH, which is the most famous vehicle, and some CMs, its neighbors. ```
Data:\(V_{t},radio\) Result:\(Clu_{t}\) distances = calculateInterVehicleDistances(\(V_{t}\));  popularity = countNumberOfNeighbours(distances); whilethere are vehicles without clusterdo  ch = getMostFamousVehicle(popularity); \(Clu_{t}\) <- ch, clusterAlgeneighbors(ch);  deleteFromVehicles(ch, neighbors(ch));  deleteFromPopularity(ch, neighbors(ch));  end while Return \(Clu_{t}\);
```  
**Algorithm 1**Clustering algorithm. At this point, each cluster is formed by one CH and some CMs. The CH is responsible of updating the infrastructure with relevant information of the cluster and updating its CMs with relevant information received from the infrastructure. In this way, the novelty of this clustering strategy comes from the fact that in this case, instead of each V-UE requests for resources to the eNodeB, the CH (Cluster Head) is the user in charge of requesting RB for all its CMs' transmissions. Therefore, let \(RB_{t}:=\{rb_{1},rb_{2},...,rb_{k}\}\) be the set of uplink orthogonal RBs that could be assigned in one frame.