This is a code package is related to the following overview article:

Emil Björnson, Eduard Jorswieck, Mérouane Debbah, Björn Ottersten, “Multi-Objective Signal Processing Optimization: The Way to Balance Conflicting Metrics in 5G Systems,” IEEE Signal Processing Magazine (Special Issue on Signal Processing for the 5G Revolution), vol. 31, no. 6, pp. 14-23, November 2014.

The package contains a simulation environment, based on Matlab, that reproduces all the numerical results and figures in the article. We encourage you to also perform reproducible research!

The evolution of cellular networks is driven by the dream of ubiquitous wireless connectivity: Any data service is instantly accessible everywhere. With each generation of cellular networks, we have moved closer to this wireless dream; first by delivering wireless access to voice communications, then by providing wireless data services, and recently by delivering a WiFi-like experience with wide-area coverage and user mobility management. The support for high data rates has been the main objective in recent years [1], as seen from the academic focus on sum-rate optimization and the efforts from standardization bodies to meet the peak rate requirements specified in IMT-Advanced. In contrast, a variety of metrics/objectives are put forward in the technological preparations for 5G networks: higher peak rates, improved coverage with uniform user experience, higher reliability and lower latency, better energy efficiency, lower-cost user devices and services, better scalability with number of devices, etc. These multiple objectives are coupled, often in a conflicting manner such that improvements in one objective lead to degradation in the other objectives. Hence, the design of future networks calls for new optimization tools that properly handle the existence and tradeoffs between multiple objectives.

In this article, we provide a review of multi-objective optimization (MOO), which is a mathematical framework to solve design problems with multiple conflicting objectives [2]-[6]. In contrast to conventional heuristic approaches where some objectives are converted into constraints, MOO enables a rigorous network design. MOO has been applied in many engineering and economic related fields, but has received little attention from the signal processing and wireless communication communities. We provide a survey of the basic definitions, properties, and algorithmic tools in MOO. This reveals how signal processing algorithms are used to visualize the inherent conflicts between 5G performance objectives, thereby allowing the network designer to understand the possible operating points and how to balance the objectives in an efficient and satisfactory way. For clarity, we provide a case study on massive multiple-input multiple-output (MIMO) systems, which is one of the key enablers of 5G cellular networks.

The paper contains 3 simulation figures, Figure 6, Figure 7, and Figure 8. These are generated by the Matlab script simulationAllFigures.m.

This article has been supported by the International Postdoc Grant 2012-228 from the Swedish Research Council and the ERC Starting Grant 305123 MORE (Advanced Mathematical Tools for Complex Network Engineering).

This code package is licensed under the GPLv2 license. If you in any way use this code for research that results in publications, please cite our original article listed above.