Signal Processing for 5G: Algorithms and Implementations

Chapter 19Energy-efficient Resource Allocation in 5G with Application to D2D

Alessio Zappone, Francesco Di Stasio, Stefano Buzzi and Eduard Jorswieck

1. 19.1 Introduction
2. 19.2 Signal Model
   1. 19.2.1 I2D Communication
   2. 19.2.2 D2D Communication
3. 19.3 Resource Allocation
4. 19.4 Fractional Programming
   1. 19.4.1 Generalized Concavity
5. 19.5 Algorithms
   1. 19.5.1 Dinkelbach's Algorithm
   2. 19.5.2 Charnes–Cooper Transform
6. 19.6 Sequential Fractional Programming
7. 19.7 System Optimization
8. 19.8 Numerical Results
9. 19.9 Conclusion
10. References

19.1 Introduction

The fifth generation (5G) of wireless communication systems will have to cope with an unprecedented number of connected devices, which is expected to reach 50 billion by 2020. On the one hand, this will require the data rates to grow by a factor of 1000 in order to serve such a massive number of devices, and to provide many new services, including e-health, e-banking, e-learning, and so on. On the other hand, such a data-rate increase cannot be achieved by simply scaling up the transmit powers, due to sustainable-growth, environmental and economic reasons. Instead, the thousand-fold data-rate increase will have to be achieved at a similar or lower level of energy consumption as present wireless networks [1]. It is therefore recognized that bit/Joule energy efficiency is a central design principle of 5G [2].

In the NGMN white paper for 5G [1], energy efficiency is identified as a key performance indicator of 5G, and is defined as the number of bits that can ...