Cell-Free Massive MIMO and Millimeter Wave Channel Modelling for 5G and Beyond

Huge demand for wireless throughput and number of users which are connected to the base station (BS) has been observed in the last decades. Massive multiple-input multiple-output (MIMO) is a promising technique for 5G for the following reasons; 1) high throughput; 2) serving large numbers of users at the same time; 3) energy efficiency. However, the low throughput of cell-edge users remains a limitation in realistic multi-cell massive MIMO systems. In cell-free massive MIMO, on the other hand, distributed access points (APs) are connected to a central processing unit (CPU) and jointly serve distributed users. This thesis investigates the performance of cell-free Massive MIMO with limited-capacity fronthaul links from the APs to the CPU which will be essential in practical 5G networks. To model the limited-capacity fronthaul links, we exploit the optimal uniform quantization. Next, closed-form expressions for spectral and energy efficiencies are presented. Numerical results investigate the performance gap between limited fronthaul and perfect fronthaul cases, and demonstrate that exploiting a relatively few quantization bits, the performance of limited-fronthaul cell-free Massive MIMO closely approaches the perfect fronthaul performance. Next, the energy efficiency maximization problem and max-min fairness problems are considered with per-user power and fronthaul capacity constraints. We propose an iterative procedure which exploits a generalized eigen vector problem and geometric programming (GP) to solve the max-min optimization problem. Numerical results indicate the superiority of the proposed algorithms over the case of equal power allocation. On the other hand, the performance of communication systems depends on the propagation channel. To investigate the performance of MIMO systems, an accurate small scale fading channel model is necessary. Geometry-based stochastic channel models (GSCMs) are mathematically tractable models to investigate the performance of MIMO systems

Cell Free Massive MIMO with Limited Capacity Fronthaul

Massive MIMO has shown to be a promising candidate for the next generation of mobile communications 5G. Cell free massive MIMO is an implementation in which antennas are distributed throughout the coverage area and the whole area is considered as a single cell. Despite increasing spectral efficiency, cell free massive MIMO has some practical challenges. One challenge is the limited capacity of fronthaul links connecting antennas to the central unit. In this paper, the problem of power weight allocation for a cell free massive MIMO with limited capacity fronthaul links is formulated and solved. The precoded signals for all of the distributed antennas are calculated at the central unit. These signals are then compressed and sent through the limited capacity links to the antennas. Several scenarios are explored, including joint power weight allocation and compression, separate power weight allocation and compression, and no compression. Results show that for limited capacity fronthaul, including compression effect rather than ignoring it, can enhance the performance of power weight allocation considerably

Centralized Cell-Free Massive MIMO with Low-Resolution Fronthaul

The increasingly new data-hungry applications in our digital society now might no longer be handled efficiently by the current cellular networks. Cell-free massive MIMO network comes to resolve the traditional way of deploying wireless networks by blurring the cell boundaries. The network comprises a large number of access points (APs) which connect the users to a central processing unit (CPU) via fronthauls for coherent transmission and reception. It is expected that this network can provide a uniformly high data rate per user and per unit area. In this thesis, we study a centralized approach to cell-free massive MIMO that can further exploit its potential with considering a practical issue of limited-capacity fronthauls. We develop different schemes as well as strategies that make the centralized approach feasible. Thereby, we propose the use of low-resolution fronthauls and analyse its performance by making use of Bussgang theorem. The first part of this thesis considers a cell-free network with single-antenna APs, where a coarse scalar uniform quantizer is devised as an interface to the fronthauls. In the second part of this thesis, we extend the network to the case of multi-antenna APs, where two different processing schemes at the APs are studied: individual processing and joint processing. For each part, two strategies for acquiring the channel state information (CSI) under low-resolution fronthaul constraint are developed: estimate-and-quantize (EQ) and quantize-and-estimate (QE). We analyse the performance of both strategies and take them into account for deriving the achievable rate of the systems. Moreover, the scalability of the centralized approach is also discussed in terms of fronthaul load and AP processing. In the last part, we propose the use of a lattice vector quantizer at multi-antenna APs for the high-mobility and high-density scenario, in which two procedures for constructing the lattice codebook are developed