## 5 Precoding as Cooperative and Hybrid Networks
### User-Centric VLC Networks  
The concept of user-centric clustering was firstly proposed in the framework of the heterogeneous networks. Specifically, in [175], a novel user-centric approach is proposed for CoMP dense cellular networks. Considering a multiple tier heterogeneous cellular network, the performance of user-centric clustering applied to the small cell tiers is analyzed in [176]. It is shown that the user-centric clustering approach outperforms the spectral efficiency achieved by the network-centric approaches. However, the works [175,176] are focused  
Figure 19: Network centric clustering. The clusters 1 and 2 are obtained by grouping the sets of optical APs {AP1, AP2, AP3} and {AP4, AP5, AP6} represented by blue and green colours, respectively. Red color represents the inter-cluster interference.  
on heterogeneous networks following a Poisson point process. On the other hand, VLC systems do not interfere with other tiers, e.g., macro or micro cells, and the optical APs are typically distributed uniformly.  
For VLC, the applications of user-centric clustering are firstly proposed in [35]. This work introduces the concept of an amorphous optical cell based on merging several optical APs cooperating among them so that the intra-cluster interference is managed by the considered cell. In [177], the authors present a novel methodology for generating amorphous optical cells for VLC, with the aim of improving the energy efficiency. It is worth noting that the authors of [177] combine OFDM transmission, cooperation among optical cells and precoding within each cell. Focusing on the intra-cluster and inter-cluster interference management, the formation of clusters from a user-centric perspective considering cooperation and precoding schemes is proposed in [106]. Those schemes can be considered multi footprint. The use of BIA schemes combined with user-centric clustering for VLC networks is proposed in [178; 179]. It is shown that generating amorphous clusters with a limited number of optical APs and users reduces the demands on SNR and coherence time, which is given by the mobility of the users, and therefore, reducing the system requirements for implementing BIA.  
For illustrative purposes, the cluster formation following a user-centric approach in a VLC network comprising \(L=16\) optical APs is shown in Figure 20. First, notice that a network-centric approach forming 4 clusters comprising 4 optical APs each generates uniform cells that do not adapt to the distribution of the users. On the other hand, for the user-centric approach, the optical cells are formed in an elastic fashion, i.e., each cell corresponds to a cluster of optical APs that provides an amorphous coverage area that adapts to the user distribution. It can be seen that the user-centric approach minimizes the inter-cluster interference. However, it may appear and should be managed according to a specific strategy. At this point, it is worth recalling that any cooperative or precoding scheme can be implemented in order to manage the intra-cluster interference.  
The formation of the user-centric clusters must consider the network topology, i.e., the position of the optical APs and the users. In [106], graph theory is applied to form these user-centric clusters [180]. The K-means algorithm is proposed as a means of generating clusters adapted to the distribution of the users in [178; 179]. Moreover, the original K-means algorithm can be modified in order to satisfy constraints given by a specific transmission scheme, e.g., the limitations of BIA are considered in [178]. Focusing on the energy efficiency of the VLC networks using ZF precoding, a variation of the K-means algorithm is proposed  
Figure 20: Example of user-centric cell formation.  
in [181]. It is interesting to remark that the K-means algorithm is a primitive form of machine learning. At this point, notice that novel machine algorithms for clustering could be applied to VLC networks.