Signal Processing for Massive MIMO Systems

<p dir="ltr">Massive multiple-input-multiple-output (MIMO) is a wireless communication technology that employs many antennas at the base station (BS) to simultaneously serve multiple user terminals (UTs). In a typical MIMO system, multiple antennas are used at both the transmitter and receiver to improve the spectral efficiency (SE) and reliability of the wireless communication system. In a massive MIMO system, the number of antennas is scaled up significantly, typically in hundreds, which allows for a significant increase in SE and can further improve reliability. Therefore, massive MIMO technology is one of the enablers for the fifth-generation (5G) networks and can be used in a range of frequency bands, including the sub-6 GHz band and the millimetre Wave band.</p><p dir="ltr">Massive MIMO has the potential to revolutionize wireless communication systems by providing a more efficient and reliable way to transmit data. It is being considered for use in 5G and beyond. However, the implementation of massive MIMO is complex and requires the use of advanced signal processing algorithms and hardware, which can be costly and challenging. The key signal processing challenges in massive MIMO systems are: signal detection, channel estimation, computational complexity, hardware constraints, pilot contamination and interference management techniques such as beamforming and precoding. In this thesis, we focus on addressing the signal processing challenges such as signal detection and channel estimation when massive MIMO systems are impaired by some hardware constraints such as phase noise and low-resolution ADCs.</p><p dir="ltr">The thesis also presents considerations on future directions and works in the area of signal processing for massive MIMO systems. The extension of existing works to more practical channels is worthy of study as the i.i.d Rayleigh fading channel may not always hold in practice. For message passing algorithms, the rank of the channel matrix and transmitted signal matrix impact on the performance of the algorithms is worth of investigating and analyzing. Careful design or management with appropriate signal processing techniques for handling some other hardware impairments such as I/Q imbalance and amplifier non-linearity is essential to minimize their negative impacts and achieve optimal system performance.</p>