Optimal Steerable mmWave Mesh Backhaul Reconfiguration

Abstract

Future 5G mobile networks will require increased backhaul (BH) capacity to connect a massive amount of high capacity small cells (SCs) to the network. Because having an optical connection to each SC might be infeasible, mmWavebased (e.g. 60 GHz) BH links are an interesting alternative due to their large available bandwidth. To cope with the increased path loss, mmWave links require directional antennas that should be able to direct their beams to different neighbors, to dynamically change the BH topology, in case new nodes are powered on/off or the traffic demand has changed. Such BH adaptation needs to be orchestrated to minimize the impact on existing traffic. This paper develops a Software-defined networking-based framework that guides the optimal reconfiguration of mesh BH networks composed by mmWave links, where antennas need to be mechanically aligned. By modelling the problem as a Mixed Integer Linear Program (MILP), its solution returns the optimal ordering of events necessary to transition between two BH network configurations. The model creates backup paths whenever it is possible, while minimizing the packet loss of ongoing flows. A numerical evaluation with different topologies and traffic demands shows that increasing the number of BH interfaces per SC from 2 to 4 can decrease the total loss by more than 50%. Moreover, when increasing the total reconfiguration time, additional backup paths can be created, consequently reducing the reconfiguration impact on existing traffic.

Fast Steerable Wireless Backhaul Reconfiguration

“…In [11], we proposed an exact MILP for the described steerable wireless BH reconfiguration problem. Since the problem is NP-hard, herein we propose scalable greedy-based heuristics which can solve larger instances in reduced time.…”

“…For each topology, a link is formed when the alignment values from each interface pair d n and d ′ n ′ are the same as V dd ′ and V d ′ d , if δ dd ′ = 1. For all K topologies, we then compute the optimal routing using an LP to minimize the total reconfiguration packet loss (line 33), according to ρ and R. The LP problem uses three continuous variables that specify the input rate at each node d ∈ I, the data rate between each node pair d and d ′ , and the loss on each node d ∈ D. It uses flow conservation constraints, as in Equation 8 from [11], which guarantee that the total input rate and packet loss of each node are equal to their total output rate and traffic demand.…”

“…To compare the solution quality of the greedy algorithms, we benchmark against the following algorithms: • Optimal results (MILP): To calculate the optimal reconfiguration sequence that minimizes packet loss, we use the exact MILP from [11]. Because the problem is NP-hard, we can only solve topologies with low K time slots (e.g.…”

“…• PVF-MILP: To solve larger problem instances in reasonable time, we run a partial variable fixing MILP (PVF-MILP), which finds sub-optimal solutions. It is derived from the MILP in [11], by fixing the movement decision variables for the interfaces from the links in X end , having them reach their final destination as early as possible.…”

“…Such orchestration is an NP-hard problem which involves coordinating the alignment of the mmWave transceivers, establishing new links, and rerouting existing BH traffic. In [11], we proposed an exact mixed integer linear program (MILP) for optimal BH reconfiguration to minimize the total packet loss. Although the proposed MILP optimally orchestrates the reconfiguration, it does not scale due to the problem complexity.…”