A close-to-capacity dirty paper coding scheme

By Uri Erez, Stephan ten Brink. IEEE Transactions on Information Theory, 2004

315 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8e52c2767d6113a1ee4ed3cdade3d8c5a95852ca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Giuseppe Caire
2. Nihar Jindal
3. Satish Babu Korada
4. D. P. Palomar
5. Rüdiger L. Urbanke
6. Wei Yu
7. Mari Kobayashi
8. Yi Jiang

In Electrical and Electronic Engineering

1. Giuseppe Caire
2. Nihar Jindal
3. Wei Yu
4. Yi Jiang
5. D. P. Palomar
6. Linglong Dai
7. Tian Lan
8. Mari Kobayashi

Citations in the corpus

• MIMO Transceiver Design via Majorization Theory (2007, methodology): “The improved DPCs are proposed in [40] and [173].”
• Transmitter Optimization for the Multi-Antenna Downlink With Per-Antenna Power Constraints (2007, methodology): “Dirty-paper may be implemented in practice using Tomlinson–Harashima precoding-like techniques [20], [21].”
• Multiuser MIMO Achievable Rates With Downlink Training and Channel State Feedback (2007, result): “Finally, recent results [33], [45] show that the dependence on CSI quality is roughly the same even when user selection is performed.”
• Achieving 1/2 log (1+SNR) on the AWGN channel with lattice encoding and decoding (2004, background): “As a final remark, we note that, in practice, the folded Euclidean metric (46) may allow to approach capacity with a less demanding nested lattice construction and may be advantageous in practice, see [13], [14].”
• MIMO broadcast channels with finite rate feedback (2006, background): “Linear Precoding Though dirty paper coding is capacity achieving for the MIMO broadcast channel, the technique requires considerab le complexity and practical implementations are still being a ctively pursued [24], [25], [26].”
• Polar Codes for Channel and Source Coding (2009, methodology): “A similar combination was considered in [62, 63, 64] for the Gelfand-Pinsker problem.”
• Millimeter-Wave Massive MIMO Communication for Future Wireless Systems: A Survey (2018, methodology): “algorithms such as the DPC and the greedy user selection method based on ZF beamforming and DPC (gZF-DP) [132] have been proposed and developed, and more recently the low-complexity random beamforming (RBF) [121] and semiorthogonal user selection (SUS) [122] which are easier for practical implementa”