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

Wireless communication is evolving rapidly with ever more connected devicesand significantly increasing data rates. Since the invention of the smartphoneand the mass introduction of mobile apps, users demand more andmore traffic to stream music, watch high-definition video or to simply browsethe internet. This tremendous growth is more pronounced by the introductionof the Internet of Things (IoT) in which small devices, such as sensors,are interconnected to exchange data for all sorts of applications. One exampleare 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 distributedloud speakers or even light bulbs in order to fake a busy household when onvacation. With all these additional devices demanding connectivity and datarates current standards such as 4G are getting to their limits. From the beginning5G was developed in order to tackle these challenges by offering higherdata rates, better coverage as well as higher energy and spectral efficiencies.Massive Multiple-Input Multiple-Output (MIMO) is a technology offering thebenefits to overcome these challenges. By scaling up the number of antennasat the Base Station (BS) side by the order of hundred or more it allows separationof signals from User Equipments (UEs) not only in time and frequencybut also in space. Exploiting the high spatial degrees-of-freedom it can focusenergy with spotlight precision to the intended UE, thereby not only achievinghigher energy being received per UE but also lowering the interference amongdifferent UEs. Utilizing this precision, massive MIMO may serve a multitudeof UEs within the same time and frequency resource, thereby achieving bothhigher data rates and spectral efficiency. This is a very important feature asspectrum 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 significantlyincreased number of BS antennas, signal processing and data distribution atthe BS become a challenging task. Signal processing complexity scales withthe number of antennas, thus requiring to distribute different tasks properlyto still achieve low-latency and energy efficient implementations. The sameholds for data movement among different antennas and central processingunits. Processing blocks have to be distributed in a manner to not exceedhardware limits, especially at points where many antennas do get combinedto 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 measurementcampaigns in order to verify theoretically promised gains, and third,implementing a fully programmable and flexible hardware platform to efficientlyrun software defined massive MIMO algorithms. In order to constructa prototype, challenges such as low-latency signal processing for huge matrixsizes as well as task distribution to lower pressure on the interconnectionnetwork are considered and implemented. By partitioning the overall systemcleverly, it is possible to implement the system fully based on Commercialoff-the-shelf (COTS) Hardware (HW). The working testbed was utilized inseveral measurement campaigns to prove the benefits of massive MIMO, suchas increased spectral efficiency, channel hardening and improved resilienceto power variations. Finally, a fully programmable Application-Specific InstructionProcessor (ASIP) was designed. Extended with a systolic array thisprogrammable platform shows high performance, when mapping a massiveMIMO detection problem utilizing various algorithms, while post-synthesisresults still suggest a relatively low-power consumption. Having the capabilityto be programmed with a high-level language as C, the design is flexibleenough to adapt to upcoming changes in the recently released 5G standard. (Less)