Bender's Decomposition for Optimization Design Problems in Communication Networks

Various types of communication networks are constantly emerging to improve connectivity services and facilitate the interconnection of various types of devices. This involves the development of several technologies, such as device-to-device communications, wireless sensor networks and vehicular communications. The various services provided have heterogeneous requirements on the quality metrics such as throughput, end-to-end latency and jitter. Furthermore, different network technologies have inherently heterogeneous restrictions on resources, for example, power, interference management requirements, computational capabilities, and so on. As a result, different network operations such as spectrum management, routing, power control and offloading need to be performed differently. Mathematical optimization techniques have always been at the heart of such design problems to formulate and propose computationally efficient solution algorithms. One of the existing powerful techniques of mathematical optimization is Benders Decomposition (BD), which is the focus of this article. Here, we briefly review different BD variants that have been applied in various existing network types and different design problems. These main variants are the classical, the combinatorial, the multi-stage, and the generalized BD. We discuss compelling BD applications for various network types including heterogeneous cellular networks, infrastructure wired wide area networks, smart grids, wireless sensor networks, and wireless local area networks. Mainly, our goal is to assist the readers in refining the motivation, problem formulation, and methodology of this powerful optimization technique in the context of future networks. We also discuss the BD challenges and the prospective ways these can be addressed when applied to communication networks' design problems

User-Base Station Association in HetSNets: Complexity and Efficient Algorithms

This work considers the problem of user association to small-cell base stations (SBSs) in a heterogeneous and small-cell network (HetSNet). Two optimization problems are investigated, which are maximizing the set of associated users to the SBSs (the unweighted problem) and maximizing the set of weighted associated users to the SBSs (the weighted problem), under signal-to-interference-plus-noise ratio (SINR) constraints. Both problems are formulated as linear integer programs. The weighted problem is known to be NP-hard and, in this paper, the unweighted problem is proved to be NP-hard as well. Therefore, this paper develops two heuristic polynomial-time algorithms to solve both problems. The computational complexity of the proposed algorithms is evaluated and is shown to be far more efficient than the complexity of the optimal brute-force (BF) algorithm. Moreover, the paper benchmarks the performance of the proposed algorithms against the BF algorithm, the branch-and-bound (B\&B) algorithm and standard algorithms, through numerical simulations. The results demonstrate the close-to-optimal performance of the proposed algorithms. They also show that the weighted problem can be solved to provide solutions that are fair between users or to balance the load among SBSs

FluidRAN: Optimized vRAN/MEC Orchestration

Proceeding of: IEEE Conference on Computer Communications, INFOCOM 2018, Honolulu, Hawai, USA, 16-19 April 2018Virtualized Radio Access Network (vRAN) architectures constitute a promising solution for the densification needs of 5G networks, as they decouple Base Stations (BUs) functions from Radio Units (RUs) allowing the processing power to be pooled at cost-efficient Central Units (CUs). vRAN facilitates the flexible function relocation (split selection), and therefore enables splits with less stringent network requirements compared to state-of-the-art fully Centralized (C-RAN) systems. In this paper, we study the important and challenging vRAN design problem. We propose a novel modeling approach and a rigorous analytical framework, FluidRAN, that minimizes RAN costs by jointly selecting the splits and the RUs-CUs routing paths. We also consider the increasingly relevant scenario where the RAN needs to support multi-access edge computing (MEC) services, that naturally favor distributed RAN (D-RAN) architectures. Our framework provides a joint vRAN/MEC solution that minimizes operational costs while satisfying the MEC needs. We follow a data-driven evaluation method, using topologies of 3 operational networks. Our results reveal that (i) pure C-RAN is rarely a feasible upgrade solution for existing infrastructure, (ii) FluidRAN achieves significant cost savings compared to D-RAN systems, and (iii) MEC can increase substantially the operator’s cost as it pushes vRAN function placement back to RUs.This work has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 671598 (5G-Crosshaul project) and 761536 (5G-Transformer project), and from Science Foundation Ireland (SFI) under Grant Number 17/CDA/476

Joint Optimization of Edge Computing Architectures and Radio Access Networks

Virtualized radio access network (vRAN) architectures and multiple-access edge computing (MEC) systems constitute two key solutions for the emerging Tactile Internet applications and the increasing mobile data traffic. Their efficient deployment, however, requires a careful design tailored to the available network resources and user demand. In this paper, we propose a novel modeling approach and a rigorous analytical framework, MEC-vRAN joint design problem (MvRAN), that minimizes vRAN costs and maximizes MEC performance. Our framework selects jointly the base-station function splits, the fronthaul routing paths, and the placement of MEC functions. We follow a data-driven evaluation method, using topologies of three operational networks and experiments with a typical face-recognition MEC service. Our results reveal that MvRAN achieves significant cost savings (up to 2.5 times) compared to non-optimized centralized RAN or decentralized RAN systems, and MEC pushes the vRAN functions to radio units and hence can increase substantially the network cost.Work supported by the EC under Grant No 761536 (5GTransformer) and by SFI under Grant No 17/CDA/4760