Hardware Implementation of Baseband Processing for Massive MIMO

• In the near future, the number of connected mobile devices and data-rates are expected to dramatically increase. Demands exceed the capability of the currently deployed (4G) wireless communication systems. Development of 5G systems is aiming for higher data-rates, better coverage, backward compatibility, and conforming with “green communication” to lower energy consumption. Massive Multiple-Input Multiple-Output (MIMO) is a technology with the potential to fulfill these requirements. In massive MIMO systems, base stations are equipped with a very large number of antennas compared to 4G systems, serving a relatively low number of users simultaneously in the same frequency and time resource. Exploiting the high spatial degrees-of-freedom allows for aggressive spatial multiplexing, resulting in high data-rates without increasing the spectrum. More importantly, achieving high array gains and eliminating inter-user interference results in simpler mobile terminals.These advantages of massive MIMO requires handling a large number of antennas efficiently, by performing baseband signal processing. Compared to small-scale MIMO base stations, the processing can be much more computationally intensive, in particular considering the large dimensions of the matrices. In addition to computational complexity, meeting latency requirements is also crucial. Another aspect is the power consumption of the baseband processing. Typically, major contributors of power consumption are poweramplifiers and analog components, however, in massive MIMO, the transmit power at each antenna can be lowered drastically (by the square of the number of antennas). Thus, the power consumption from the baseband processing becomes more significant in relation to other contributions. This puts forward the main challenge tackled in this thesis, i.e., how to implement low latency baseband signal processing modules with high hardware and energy efficiency.The focus of this thesis has been on co-optimization of algorithms and hardware implementations, to meet the aforementioned challenges/requirements. Algorithm optimization is performed to lower computational complexity, e.g., large scale matrix operations, and also on the system-level to relax constraints on analog/RF components to lower cost and improve efficiency. These optimizations were evaluated by taking into consideration the hardware cost and device level parameters. To this end, a massive MIMO central baseband pre-coding/detection chip was fabricated in 28 nm FD-SOI CMOS technology and measured. The algorithm and hardware co-optimization resulted in the highest reported pre-coding area and energy efficiency of 34.1QRD/s/gate and 6.56nJ/QRD, respectively. For detection, compared to small scale MIMO systems, massive MIMO with linear schemes provided superior performance, with area and energy efficiency of 2.02Mb/s/kGE and 60 pJ/b.The array and spatial multiplexing gains in massive MIMO, combined with high hardware efficiency and schemes to lower constraints on RF/analog components, makes it extremely promising for future deployments.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

Massive MIMO

Hardware architectures

ASIC design

precoding/detection

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