SAPOUNTZIS Nikolaos

Thesis

Network Layer Optimization for Next Generation Heterogeneous Networks

By 2016, it is well-known that mobile networking has dominated our lives. We use our mobile cell phones for almost everything: from social networking to streaming, finding accommodation or banking. Nevertheless, it seems that operators have not understood yet this domination, since their networks consist of nodes that: (i) suffer from enormous load fluctuations, (ii) waste their resources, and (iii) are blamed to be a major energy-killer worldwide. Such shortcomings hurt: load-balancing, spectral and energy efficiency, respectively. The goal of this dissertation is to carefully study these efficiencies and achieve a good trade-off between them for future mobile 5G heterogeneous networks (HetNets).

 

Towards this direction, we firstly focus on (i) the user and traffic differentiation, emerging from the Machine Type Communications (MTC) and Internet of Things (IoT) applications, and (ii) the Radio Access Network (RAN). Specifically, we perform appropriate modeling, performance analysis and optimization for a family of objectives, using tools mostly coming from (non) convex optimization, probability and queueing theory. Our initial consideration is on network-layer optimizations (e.g. studying the user association problem). Then, we analytically show that cross-layer optimization is key for the success of future HetNets, as one needs to jointly study other problems coming from the layers below (e.g. the TDD allocation problem from the MAC, or the cross-interference management from the PHY) to avoid performance degradation.  

 

In the second part, we highlight that optimizing RAN functionalities, followed by a tremendous capacity crunch, pose tough constraints in the backhaul network by emerging it into a performance bottleneck. Thus, we have included into our framework: (heterogeneous) capacity constraints for the backhaul links along with generic “mesh” topologies, to avoid congestion events in the backhaul while (jointly) optimizing RAN. Through extensive evaluation we (i) corroborate the correctness of our framework, (ii) show impressive performance improvements when cross-layer/network optimization is performed, and (iii) reveal interesting arising tradeoffs.