Name
Abstract | ||
|---|---|---|
While linear equalization schemes like zero forcing or minimum mean-square error achieve a near optimal uplink signal estimation performance in large-scale multi-user multiple-input multiple-output systems, the corresponding algorithms lean on centralized processing. To avoid disproportionate interconnect data rates due to the centralized signal estimation, performing a decentralized equalization can mitigate these effects. In this paper, we present a decentralized signal estimation architecture, which combines the ideas of existing decentralized architectures to (i) reduce the overall latency of the signal estimation and (ii) maintain a high data detection performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IEEECONF44664.2019.9048772 | CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS |
Keywords | DocType | ISSN |
Massive MIMO, MMSE, Signal Estimation, Distributed Processing, Binary Tree | Conference | 1058-6393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pascal Seidel | 1 | 5 | 2.69 |
| Steffen Paul | 2 | 142 | 40.96 |
| Jochen Rust | 3 | 32 | 12.51 |