5G Millimeter Wave Cellular System Capacity with Fully Digital Beamforming

Due to heavy reliance of millimeter-wave (mmWave) wireless systems on directional links, Beamforming (BF) with high-dimensional arrays is essential for cellular systems in these frequencies. How to perform the array processing in a power efficient manner is a fundamental challenge. Analog and hybrid BF require fewer analog-to-digital converters (ADCs), but can only communicate in a small number of directions at a time,limiting directional search, spatial multiplexing and control signaling. Digital BF enables flexible spatial processing, but must be operated at a low quantization resolution to stay within reasonable power levels. This paper presents a simple additive white Gaussian noise (AWGN) model to assess the effect of low resolution quantization of cellular system capacity. Simulations with this model reveal that at moderate resolutions (3-4 bits per ADC), there is negligible loss in downlink cellular capacity from quantization. In essence, the low-resolution ADCs limit the high SNR, where cellular systems typically do not operate. The findings suggest that low-resolution fully digital BF architectures can be power efficient, offer greatly enhanced control plane functionality and comparable data plane performance to analog BF.Comment: To appear in the Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 201

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