Study of Block Diagonalization Precoding and Power Allocation for Multiple-Antenna Systems with Coarsely Quantized Signals

Abstract

In this work, we present block diagonalization and power allocation algorithms for large-scale multiple-antenna systems with coarsely quantized signals. In particular, we develop Coarse Quantization-Aware Block Diagonalization $\left(\mathrm{CQA}-\mathrm{BD}\right)$ and Coarse Quantization-Aware Regularized Block Diagonalization $\left(\mathrm{CQA}-\mathrm{RBD}\right)$ precoding algorithms that employ the Bussgang decomposition and can mitigate the effects of low-resolution signals and interference. Moreover, we also devise the Coarse Quantization-Aware Most Advantageous Allocation Strategy $\left(\mathrm{CQA}-\mathrm{MAAS}\right)$ power allocation algorithm to improve the sum rate of precoders that operate with low-resolution signals. An analysis of the sum-rate performance is carried out along with computational complexity and power consumption studies of the proposed and existing techniques. Simulation results illustrate the performance of the proposed $\mathrm{CQA}-\mathrm{BD}$ and $\mathrm{CQA}-\mathrm{RBD}$ precoding algorithms, and the proposed $\mathrm{CQA}-\mathrm{MAAS}$ power allocation strategy against existing approaches.

arXiv comment9 figures, 33 pages

References in corpus (9)

• Massive MIMO for Next Generation Wireless Systems
• Throughput Analysis of Massive MIMO Uplink with Low-Resolution ADCs
• Low-Complexity Design of Generalized Block Diagonalization Precoding Algorithms for Multiuser MIMO Systems
• Branch-and-Bound Precoding for Multiuser MIMO Systems with 1-Bit Quantization
• Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniques
• Multi-Branch Tomlinson-Harashima Precoding for MU-MIMO Systems: Theory and Algorithms
• Distributed Space-Time Coding Based on Adjustable Code Matrices for Cooperative MIMO Relaying Systems
• Adaptive Reduced-Rank Minimum Symbol-Error-Rate Receive Processing for Large-Scale Multiple-Antenna Systems
• Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals