A Novel Approach for Combining Local Estimates for Fully Decentralized Feedforward Massive MIMO Equalization: The Multistep Fusion
| Autor: | Ludwig Karsthof, Pascal Seidel, Steffen Paul |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Bandwidth (signal processing) MIMO 0207 environmental engineering Equalization (audio) Feed forward 020206 networking & telecommunications 02 engineering and technology Base station Channel state information Telecommunications link 0202 electrical engineering electronic engineering information engineering Electronic engineering Wireless 020701 environmental engineering business |
| Zdroj: | ACSSC |
| DOI: | 10.1109/ieeeconf51394.2020.9443483 |
| Popis: | While centralized linear equalization algorithms can achieve a near optimal uplink detection performance for Massive MIMO systems at the base station, the required interconnect and off-chip input/output data rates surpass the bandwidth of existing interconnect standards. The fully decentralized feedforward equalization architecture can alleviate these bottlenecks by performing the signal equalization in decentralized clusters using only local channel state information. As the effectiveness of the decentralized equalization heavily depends on the fusion of the local signal estimates, we present a novel combiner algorithm based on the multistep method that is capable to reduce the performance loss in contrast to the centralized equalization approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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