@InProceedings{6952625,
  author = {B. Knoop and F. Monsees and C. Bockelmann and D. Peters-Drolshagen and S. Paul and A. Dekorsy},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Compressed sensing K-best detection for sparse multi-user communications},
  year = {2014},
  pages = {1726-1730},
  abstract = {Machine-type communications are quite often of very low data rate and of sporadic nature and therefore not well-suited for nowadays high data rate cellular communication systems. Since signaling overhead must be reasonable in relation to message size, research towards joint activity and data estimation was initiated. When the detection of sporadic multi-user signals is modeled as a sparse vector recovery problem, signaling concerning node activity can be avoided as it was demonstrated in previous works. In this paper we show how well-known K-Best detection can be modified to approximately solve this finite alphabet Compressed Sensing problem. We also demonstrate that this approach is robust against parameter variations and even works in cases where fewer measurements than unknown sources are available.},
  keywords = {cellular radio;compressed sensing;multiuser detection;sporadic multiuser signal detection;sparse multiuser communications;cellular communication systems;finite alphabet;sparse vector recovery problem;machine-type communications;K-best detection;compressed sensing;Detectors;Complexity theory;Measurement;Vectors;Signal to noise ratio;Compressed sensing;Robustness;K-Best algorithm;multi-user detection;sparse signal processing;Compressed Sensing},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569923053.pdf},
}

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Since signaling overhead must be reasonable in relation to message size, research towards joint activity and data estimation was initiated. When the detection of sporadic multi-user signals is modeled as a sparse vector recovery problem, signaling concerning node activity can be avoided as it was demonstrated in previous works. In this paper we show how well-known K-Best detection can be modified to approximately solve this finite alphabet Compressed Sensing problem. 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Dekorsy},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Compressed sensing K-best detection for sparse multi-user communications},\n year = {2014},\n pages = {1726-1730},\n abstract = {Machine-type communications are quite often of very low data rate and of sporadic nature and therefore not well-suited for nowadays high data rate cellular communication systems. Since signaling overhead must be reasonable in relation to message size, research towards joint activity and data estimation was initiated. When the detection of sporadic multi-user signals is modeled as a sparse vector recovery problem, signaling concerning node activity can be avoided as it was demonstrated in previous works. In this paper we show how well-known K-Best detection can be modified to approximately solve this finite alphabet Compressed Sensing problem. 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