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Exploiting quantization uncertainty for enhancing capacity of limited-feedback MISO ad hoc networks

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Exploiting quantization uncertainty for enhancing capacity of limited-feedback MISO ad hoc networks. / Khoshkholgh, Mohammad G.; Haghighi, Ali A.; Navaie, Keivan; Shin, K. G. ; C. M. Leung, Victor.

Global Communications Conference (GLOBECOM), 2016 IEEE. IEEE, 2016.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Khoshkholgh, MG, Haghighi, AA, Navaie, K, Shin, KG & C. M. Leung, V 2016, Exploiting quantization uncertainty for enhancing capacity of limited-feedback MISO ad hoc networks. in Global Communications Conference (GLOBECOM), 2016 IEEE. IEEE, 2016 IEEE Global Communications Conference, 4/12/16. https://doi.org/10.1109/GLOCOM.2016.7841830

APA

Khoshkholgh, M. G., Haghighi, A. A., Navaie, K., Shin, K. G., & C. M. Leung, V. (2016). Exploiting quantization uncertainty for enhancing capacity of limited-feedback MISO ad hoc networks. In Global Communications Conference (GLOBECOM), 2016 IEEE IEEE. https://doi.org/10.1109/GLOCOM.2016.7841830

Vancouver

Khoshkholgh MG, Haghighi AA, Navaie K, Shin KG, C. M. Leung V. Exploiting quantization uncertainty for enhancing capacity of limited-feedback MISO ad hoc networks. In Global Communications Conference (GLOBECOM), 2016 IEEE. IEEE. 2016 https://doi.org/10.1109/GLOCOM.2016.7841830

Author

Khoshkholgh, Mohammad G. ; Haghighi, Ali A. ; Navaie, Keivan ; Shin, K. G. ; C. M. Leung, Victor. / Exploiting quantization uncertainty for enhancing capacity of limited-feedback MISO ad hoc networks. Global Communications Conference (GLOBECOM), 2016 IEEE. IEEE, 2016.

Bibtex

@inproceedings{1d3ba441d5c64119829b29d54d449727,
title = "Exploiting quantization uncertainty for enhancing capacity of limited-feedback MISO ad hoc networks",
abstract = "In this paper we investigate the capacity of random wireless networks in which transmitters are equipped with multiantennas. A quantized version of channel direction information (CDI) is also available, provided by the associated single antenna receivers. We adopt tools of stochastic geometry and random vector quantization to incorporate the impacts of interference and quantization errors, respectively. We first study the capacity of Aloha, and channel quality information (CQI)-based scheduling, whereby the transmissions decision in each transceiver pair depends on the strength of the CQI against a prescribed threshold. We then propose a new scheduling scheme, namely modified CQI (MCQI), by which the quantization error is effectively incorporated in the scheduling. Further we obtain the capacity of MCQI-based scheduling. Simulation results confirm our analysis and show that the proposed MCQI-based scheduling improves the capacity compared to the CQI-based scheduling and Aloha. It is also seen that the performance boost is more significant where the feedback capacity is low and the network is dense. In comparison with the case of high feedback capacity, the network capacity is not reduced by low feedback capacity in the MCQI-based scheduling. This is of practical importance since the network designer can save the feedback resources by employing MCQI-based scheduling without compromising the capacity and increasing the receivers’ complexity.",
author = "Khoshkholgh, {Mohammad G.} and Haghighi, {Ali A.} and Keivan Navaie and Shin, {K. G.} and {C. M. Leung}, Victor",
note = "{\circledC}2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2016",
month = "12",
day = "4",
doi = "10.1109/GLOCOM.2016.7841830",
language = "English",
isbn = "9781509013296",
booktitle = "Global Communications Conference (GLOBECOM), 2016 IEEE",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Exploiting quantization uncertainty for enhancing capacity of limited-feedback MISO ad hoc networks

AU - Khoshkholgh, Mohammad G.

AU - Haghighi, Ali A.

AU - Navaie, Keivan

AU - Shin, K. G.

AU - C. M. Leung, Victor

N1 - ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2016/12/4

Y1 - 2016/12/4

N2 - In this paper we investigate the capacity of random wireless networks in which transmitters are equipped with multiantennas. A quantized version of channel direction information (CDI) is also available, provided by the associated single antenna receivers. We adopt tools of stochastic geometry and random vector quantization to incorporate the impacts of interference and quantization errors, respectively. We first study the capacity of Aloha, and channel quality information (CQI)-based scheduling, whereby the transmissions decision in each transceiver pair depends on the strength of the CQI against a prescribed threshold. We then propose a new scheduling scheme, namely modified CQI (MCQI), by which the quantization error is effectively incorporated in the scheduling. Further we obtain the capacity of MCQI-based scheduling. Simulation results confirm our analysis and show that the proposed MCQI-based scheduling improves the capacity compared to the CQI-based scheduling and Aloha. It is also seen that the performance boost is more significant where the feedback capacity is low and the network is dense. In comparison with the case of high feedback capacity, the network capacity is not reduced by low feedback capacity in the MCQI-based scheduling. This is of practical importance since the network designer can save the feedback resources by employing MCQI-based scheduling without compromising the capacity and increasing the receivers’ complexity.

AB - In this paper we investigate the capacity of random wireless networks in which transmitters are equipped with multiantennas. A quantized version of channel direction information (CDI) is also available, provided by the associated single antenna receivers. We adopt tools of stochastic geometry and random vector quantization to incorporate the impacts of interference and quantization errors, respectively. We first study the capacity of Aloha, and channel quality information (CQI)-based scheduling, whereby the transmissions decision in each transceiver pair depends on the strength of the CQI against a prescribed threshold. We then propose a new scheduling scheme, namely modified CQI (MCQI), by which the quantization error is effectively incorporated in the scheduling. Further we obtain the capacity of MCQI-based scheduling. Simulation results confirm our analysis and show that the proposed MCQI-based scheduling improves the capacity compared to the CQI-based scheduling and Aloha. It is also seen that the performance boost is more significant where the feedback capacity is low and the network is dense. In comparison with the case of high feedback capacity, the network capacity is not reduced by low feedback capacity in the MCQI-based scheduling. This is of practical importance since the network designer can save the feedback resources by employing MCQI-based scheduling without compromising the capacity and increasing the receivers’ complexity.

U2 - 10.1109/GLOCOM.2016.7841830

DO - 10.1109/GLOCOM.2016.7841830

M3 - Conference contribution/Paper

SN - 9781509013296

BT - Global Communications Conference (GLOBECOM), 2016 IEEE

PB - IEEE

ER -