A framework using graph theory can help make digital communication networks more efficient. King Abdullah University of Science and Technology demonstrated how graph theory can be applied to optimize digital communications networks.
What is graph theory? It is usually used to illustrate how social networks work. The standard form of a social network is a graph. It incorporates a set of points with lines joining some of the points. The points are the network's members. The lines are - connections between them.
Researches from KAUST have found that graph theory can be usefully applied in communications and signal processing.
Their method is to formulate a given digital communication network as a graph and then find "cliques" within it. In graph theory, this is known as solving the "clique problem."
In any graph, a clique is a subset of points in which each point is connected to every other point. In a social network that means a group in which each member is friends with every other member in the group.
The research team has showed how communications networks can be optimised using the same approach. For instance, a base station feeding wireless data to passing cars can be programmed to send data packs for common use once instead of repeatedly to individual vehicles.
The complexity of any graph increases exponentially as it grows in size. It means the computers need clever algorithms to solve the clique problem for all but the smallest graphs.
Another bonus of the approach lies in its future applicability. As networks increase in size and complexity, so do the gains from optimization. IoT will feature many more users, with 5G enabling much larger volumes of data to be accommodated.
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