Massive MIMO: Prototyping, Proof-of-Concept and Implementation - PhD Defence by Steffen Malkowsky
Author: Steffen Malkowsky, Department of EIT
Location: E:1406, E-building, Ole Römers väg 3, LTH, Lund University
Faculty opponent: Professor Joseph R. Cavallaro
Wireless communication is evolving rapidly with ever more connected devices and significantly increasing data rates. Since the invention of the smartphone and the mass introduction of mobile apps, users demand more and more traffic to stream music, watch high-definition video or to simply browse the internet. This tremendous growth is more pronounced by the introduction of the Internet of Things (IoT) in which small devices, such as sensors, are interconnected to exchange data for all sorts of applications. One example are smart homes in which a user can for instance, check temperature at home, verify if windows are closed or open, or simply turn on and off distributed loud speakers or even light bulbs in order to fake a busy household when on vacation. With all these additional devices demanding connectivity and data rates current standards such as 4G are getting to their limits. From the beginning 5G was developed in order to tackle these challenges by offering higher data rates, better coverage as well as higher energy and spectral efficiencies. Massive Multiple-Input Multiple-Output (MIMO) is a technology offering the benefits to overcome these challenges. By scaling up the number of antennas at the Base Station (BS) side by the order of hundred or more it allows separation of signals from User Equipments (UEs) not only in time and frequency but also in space. Exploiting the high spatial degrees-of-freedom it can focus energy with spotlight precision to the intended UE, thereby not only achieving higher energy being received per UE but also lowering the interference among different UEs. Utilizing this precision, massive MIMO may serve a multitude of UEs within the same time and frequency resource, thereby achieving both higher data rates and spectral efficiency. This is a very important feature as spectrum is very crowded and does not allow for much higher band-widths, and more importantly is also very expensive.
The promised gains, however, do come at a cost. Due to the significantly increased number of BS antennas, signal processing and data distribution at the BS become a challenging task. Signal processing complexity scales with the number of antennas, thus requiring to distribute different tasks properly to still achieve low-latency and energy efficient implementations. The same holds for data movement among different antennas and central processing units. Processing blocks have to be distributed in a manner to not exceed hardware limits, especially at points where many antennas do get combined to perform some kind of centralized processing.
The focus of this thesis can be divided into three different aspects, first, building a real-time prototype for massive MIMO, second, conducting measurement campaigns in order to verify theoretically promised gains, and third, implementing a fully programmable and flexible hardware platform to efficiently run software defined massive MIMO algorithms. In order to construct a prototype, challenges such as low-latency signal processing for huge matrix sizes as well as task distribution to lower pressure on the interconnection network are considered and implemented. By partitioning the overall system cleverly, it is possible to implement the system fully based on Commercial off-the-shelf (COTS) Hardware (HW). The working testbed was utilized in several measurement campaigns to prove the benefits of massive MIMO, such as increased spectral efficiency, channel hardening and improved resilience to power variations. Finally, a fully programmable Application-Specific Instruction Processor (ASIP) was designed. Extended with a systolic array this programmable platform shows high performance, when mapping a massive MIMO detection problem utilizing various algorithms, while post-synthesis results still suggest a relatively low-power consumption. Having the capability to be programmed with a high-level language as C, the design is flexible enough to adapt to upcoming changes in the recently released 5G standard.