Massive MIMO: Prototyping, Proof-of-Concept and Implementation

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.