Delay‐ and energy‐aware load balancing in ultra‐dense heterogeneous 5G networks

Abstract

In the context of ultra‐dense heterogeneous networks in 5G, we focus on the load balancing problem of elastic traffic in a single macrocell that is supported by a number of small cells. Each small cell is modeled by a single‐class processor sharing queue, whereas the macrocell is modelled by a multiclass processor sharing queue. We assume that the macrocell is always consuming energy, while a small cell can be switched off when it is idle with the setup delay penalty. As the main contribution, aimed at minimizing the weighted sum of the mean delay and the mean power consumption in the system, we develop static and dynamic load balancing policies that take into account the setup delay. All the obtained results are analytically founded. The performance of the static policy is evaluated using the achieved analytical expressions, which include the static optimal probabilities achieved before, whereas for the dynamic policy we need to perform simulations using previously obtained numerical results from the first policy iteration procedure. As concluded, numerical results indicate that the dynamic policy outperforms the static policy under realistic settings.

Dynamic load balancing in 5G HetNets for optimal performance-energy tradeoff

“…Service rate for a request originating from within a small cell is µ k = 18.73 s −1 if it is served by the small cell, and µ 0,k = 6.37 s −1 if offloaded to the macro. Additionally, the macro cell also serves users that are outside the coverage area of the small cells with a rate µ 0 = 12.34 s −1 , obtained by assuming file sizes of 5 Mb and typical measured mean channel qualities, see [10] for more detailed justifications. In the entire simulation study we set arrival rate in the macro cell to λ 0 = 2 s −1 , and systematically choose small cell arrival rates so that the load on the small cell is 0.05, 0.2, 0.32, and 0.5.…”

“…Before discussing the optimization, we state a necessary condition for the stability of the system. As in [10], the macrocell is the bottleneck in the system, because it serves as an overflow system for the arrivals in the small cells, and thus, the maximal stability condition for any load balancing policy is given by…”

“…In this case, the policy converged with 3 iterations. Then β is adapted according to (10) and the policy iteration is applied until convergence, see curve labeled 2, etc. In this small example, the policy iteration can be numerically evaluated until convergence.…”

“…Also, the system models are different to ours in these papers, or energy aspects are not considered. Note that in our earlier paper [10], we have applied the so-called FPI approach, to be discussed below, but the policy is obtained numerically and, in addition, the system model is slightly different (no idle timer in small cells and macrocell energy model is simpler).…”

Throughput enhanced scheduling (TES) scheme for ultra‐dense networks

“…These popular techniques can help to address QoS requirements by sacrificing some of the system throughput. In 5G and beyond, there is a need for much greater system throughput compared with 4G (1000 x increase); thus, new scheduling techniques that can significantly improve the system throughput and address the QoS and fairness requirements are needed …”