Efficient Detectors for LTE Uplink Systems: From Small to Large System

3GPP Long Term Evolution (LTE) is currently the most popular cellular wireless communication standard. Future releases of the 3GPP specifications consider large-scale (or massive) multiple-input multiple-output (MIMO), an emerging technology where the base station (BS) is equipped with hundreds of antennas. Although large-scale MIMO improves spectral efficiency, link reliability, and coverage over conventional (small-scale) MIMO systems, the dimensionality of large-scale systems increases the computational complexity of uplink data detection significantly.;I present efficient data detection algorithms for the LTE uplink and analyze the performance-complexity tradeoff for small to large-scale multiple-input multiple-output (MIMO) systems. I propose an iterative detection and decoding (IDD) scheme which combines frequency domain minimum mean-square error (FD-MMSE) equalization with parallel interference cancellation (PIC) to achieve near-optimal performance and show this scheme achieves near-optimal detection performance if the number of BS antennas exceeds the number of users by roughly 2x. For (symmetric) small-scale MIMO systems, IDD significantly reduces the frame error rate (FER) while the gains with large-scale MIMO are comparably smaller, which suggests MMSE detection is sufficient for large-scale MIMO systems.;Linear MMSE detection still requires a computationally complex matrix inversion. For systems with very large ratios between the number of BS and user antennas, matrix inversion is performed on a strongly diagonally dominant matrix. I investigate a variety of exact and approximate equalization schemes that solve the system of linear equations either explicitly (requiring the computation of a matrix inverse) or implicitly (by directly computing the solution vector), and we analyze the associated performance/complexity trade-offs. I show that for small base-station (BS)-to-user-antenna ratios, exact and implicit data detection using the Cholesky decomposition achieves near-optimal performance at low complexity; for large BS-to-user-antenna ratios, implicit data detection using approximate equalization methods results in the best trade-off.;Finally, I show by combining the advantages of exact, approximate, implicit, and explicit matrix inversion, I develop a new frequency-adaptive equalizer (FADE), which outperforms existing linear data-detection methods in terms of performance and complexity and can scale from small-scale MIMO systems to large-scale MIMO systems.