Massive MIMO channel models for 5G wireless communication systems and beyond

The recently standardised 5th generation (5G) wireless communication technologies and their evolution towards the 6th generation (6G) will enable low-latency, highdensity, and high-capacity communications across a wide variety of scenarios under tight constraints on energy consumption and limited availability of radio electromagnetic spectrum. Massive multiple-input multiple-output (MIMO) technologies will be key to achieve some of these goals and cover the ever-growing demand of data rates, reliability and seamless connectivity. Nowadays, the design and evaluation of new wireless communication technologies heavily rely on computationally-efficient channel models that can accurately capture essential propagation phenomena and flexibly adapt to a wide variety of scenarios. Thus, this thesis aims at providing methods of analysis of massive MIMO channels and developing advanced massive MIMO channel models that will help assess the 5G wireless communication technologies and beyond. First, key aspects of massive MIMO channels are investigated through a stochastic transformation method capable of modelling the space-time varying (STV) distribution of the delay and angle of arrival (AoA) of multi-path components (MPCs). The proposed method is followed by a channel modelling approach based on STV parameters of the AoA distribution that leads to closed-form expressions of key massive MIMO channel statistical properties. These methods are employed to analyse widelyused channel models and reveal some of their limitations. This investigation provides fundamental insights about non-stationary properties of massive MIMO channels and paves the way for developing subsequent efficient and accurate channel models. Second, three-dimensional (3D) non-stationary wideband geometry-based stochastic models (GBSMs) for massive MIMO communication systems are proposed. These models incorporate a novel approach to capture near-field effects, namely, the parabolic wavefront, that presents a good accuracy-complexity trade-off when compared to other existing techniques. In addition to cluster of MPCs (re)appearance, a Log-normal cluster-level shadowing process complements the modelling of large-scale fading over the array. Statistical properties of the models are derived and validated through simulations and measurements extracted from the available literature. Third, a highly-flexible and efficient 3D space-time non-stationary wideband massive MIMO channel model based on an ray-level evolution approach is proposed as a candidate for the design and assessment of 5G and beyond 5G (B5G) massive MIMO wireless communication technologies. The model can capture near-field effects, (dis)appearance, and large-scale fading of both clusters and individual MPCs by employing a single approach. Its efficiency relies upon a more realistic wavefront selection criterion, namely, the effective Rayleigh distance, which accounts for the limited lifespan of MPCs over the array. This novel criterion can help improve the efficiency of both existing and B5G massive MIMO channel models by greatly reducing the need for spherical wavefronts

Uplink Performance of Time-Reversal MRC in Massive MIMO Systems Subject to Phase Noise

Multi-user multiple-input multiple-output (MU-MIMO) cellular systems with an excess of base station (BS) antennas (Massive MIMO) offer unprecedented multiplexing gains and radiated energy efficiency. Oscillator phase noise is introduced in the transmitter and receiver radio frequency chains and severely degrades the performance of communication systems. We study the effect of oscillator phase noise in frequency-selective Massive MIMO systems with imperfect channel state information (CSI). In particular, we consider two distinct operation modes, namely when the phase noise processes at the $M$ BS antennas are identical (synchronous operation) and when they are independent (non-synchronous operation). We analyze a linear and low-complexity time-reversal maximum-ratio combining (TR-MRC) reception strategy. For both operation modes we derive a lower bound on the sum-capacity and we compare their performance. Based on the derived achievable sum-rates, we show that with the proposed receive processing an $O(\sqrt{M})$ array gain is achievable. Due to the phase noise drift the estimated effective channel becomes progressively outdated. Therefore, phase noise effectively limits the length of the interval used for data transmission and the number of scheduled users. The derived achievable rates provide insights into the optimum choice of the data interval length and the number of scheduled users.Comment: 13 pages, 6 figures, 2 tables, IEEE Transactions on Wireless Communications (accepted