Name
Title | ||
|---|---|---|
Soft-Output Detection Methods for Sparse Millimeter Wave MIMO Systems with Low-Precision ADCs. |
Abstract | ||
|---|---|---|
In this paper, we propose computationally efficient yet near-optimal soft-output detection methods for coded millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) systems with low-precision analog-to-digital converters (ADCs). The underlying idea of the proposed methods is to construct an extremely sparse inter-symbol-interference channel model by jointly exploiting the delay-domain sparsity in mmWave channels and a high quantization noise caused by low-precision ADCs. Then, we harness this sparse channel model to create a trellis diagram with a reduced number of states and a factor graph with very sparse edge connections, which are used for the computationally efficient soft-output detection methods. Using the reduced trellis diagram, we present a soft-output detection method that computes the log-likelihood ratios (LLRs) of coded bits by optimally combining the quantized received signals obtained from multiple receive antennas using a forward-and-backward algorithm. To reduce the computational complexity further, we also present a low-complexity detection method using the sparse factor graph to compute the LLRs in an iterative fashion based on a belief propagation algorithm. Simulations results demonstrate that the proposed soft-output detection methods provide significant frame-error-rates gains compared with the existing frequency-domain equalization techniques in a coded mmWave MIMO system using one- or two-bit ADCs. |
Year | Venue | Field |
|---|---|---|
2018 | IEEE Transactions on Communications | Flight dynamics (spacecraft),Factor graph,Equalization (audio),Computer science,MIMO,Algorithm,Communication channel,Electronic engineering,Quantization (signal processing),Computational complexity theory,Belief propagation |
DocType | Volume | Citations |
Journal | abs/1811.11923 | 1 |
PageRank | References | Authors |
0.37 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yo-Seb Jeon | 1 | 54 | 9.12 |
| Heedong Do | 2 | 1 | 0.37 |
| Song-Nam Hong | 3 | 244 | 33.84 |
| Namyoon Lee | 4 | 857 | 62.30 |