Traffic demand-aware topology control for enhanced energy-efficiency of cellular networks

Abstract

The service provided by current mobile networks is not adapted to spatio-temporal fluctuations in traffic demand, but such fluctuations offer opportunities for energy savings. In particular, significant gains in energy efficiency are realizable by disengaging temporarily redundant hardware components of base stations. We therefore propose a novel optimization framework that considers both the load-dependent energy radiated by the antennas and the remaining forms of energy needed for operating the base stations. The objective is to reduce the energy consumption of mobile networks, while ensuring that the data rate requirements of the users are met throughout the coverage area. Building upon sparse optimization techniques, we develop a majorization-minimization algorithm with the ability to identify energy-efficient network configurations. The iterative algorithm is load-aware, has low computational complexity, and can be implemented in an online fashion to exploit load fluctuations on a short time scale. Simulations show that the algorithm can find network configurations with the energy consumption similar to that obtained with global optimization tools, which cannot be applied to real large networks. Although we consider only one currently deployed cellular technology, the optimization framework is general, potentially applicable to a large class of access technologies.

Low Complexity Iterative Algorithms for Power Estimation in Ultra-Dense Load Coupled Networks

“…In principle, we could improve the rates shown here with resource management techniques that do not use the assumptions on homogeneity and uniform power. However, note that, even with the homogeneity assumption, many network optimization problems are already NP-hard [20], and principled approaches designed to handle these problems often do not scale to large networks because of the huge number of additional variables [21].…”

A Robust Machine Learning Method for Cell-Load Approximation in Wireless Networks

“…In this study, we consider a dense urban cellular base station (BS) deployment. The service area is represented by a grid of pixels, each occupying a small area which we refer to as a test point (TP) (see [1,10], and [7]). The concept of test points provides a network-layer view of qualityof-service (QoS) and large-scale channel conditions in a network.…”

“…It is assumed that if the rate requirement of each TP is met, then the QoS requirements of all users in the network are also met. Under this assumption, small-scale fluctuations in individual user rates are averaged out and are compensated by the lower layers of the protocol stack [7]. We use M = {1, 2, ..., M } and N = {1, 2, ..., N } to denote the set of BSs and TPs, respectively.…”

“…The cell-load estimation algorithm is shown in Algorithm 1. Note that, for a given r ∈ X each BS i ∈ M can calculate the component g i (r) independently of other BSs using (7). Therefore, Algorithm 1 is scalable to a larger dense network and is amenable to distributed implementation.…”

“…The load-coupling model (see, e.g., [1,2] and [3]) is widely used when designing networks according to the long-term evolution (LTE) standard and has also attracted attention in the context of fifth-generation (5G) networks. More specifically, the load-coupling model has been used in various optimization frameworks dealing with different aspects of network design including, but not limited to, data offloading [4], proportional fairness [5], energy optimization [6,7], and load balancing [8].…”