Multi-Objective Signal Processing Optimization: The way to Balance Conflicting Metrics in 5G Systems

Publication Type:

Journal Article


IEEE Signal Processing Magazine (Special Issue on Signal Processing for the 5G Revolution), Volume 31, Number 4, p.142-148 (2014)


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.

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