4.7 Article

Achievable Rates of Opportunistic Cognitive Radio Systems Using Reconfigurable Antennas With Imperfect Sensing and Channel Estimation

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCCN.2021.3056691

关键词

Sensors; Channel estimation; Training; Interference; Data communication; Transmitting antennas; Cognitive radio; Achievable rates; beam detection; beam selection; channel estimation; imperfect spectrum sensing; opportunistic cognitive radio system; optimal and sub-optimal transmit power; reconfigurable antennas; training and data symbols

资金

  1. NSF [ECCS-1443942, CISE-CNS-2006683]

向作者/读者索取更多资源

The paper introduces a design framework for a cognitive radio system utilizing antenna beam steering capability, and explores optimization methods for spectrum sensing, channel estimation, and data transmission.
We consider an opportunistic cognitive radio (CR) system in which secondary transmitter (SUtx) is equipped with a reconfigurable antenna (RA). Utilizing the beam steering capability of the RA, we regard a design framework for integrated sector-based spectrum sensing and data communication. In this framework, SUtx senses the spectrum and detects the beam corresponding to active primary user's (PU) location. SUtx also sends training symbols (prior to data symbols), to enable channel estimation at secondary receiver (SUrx) and selection of the strongest beam between SUtx- SUrx for data transmission. We establish a lower bound on the achievable rates of SUtx- SUrx link, in the presence of spectrum sensing and channel estimation errors, and errors due to incorrect detection of the beam corresponding to PU's location and incorrect selection of the strongest beam for data transmission. We formulate a novel constrained optimization problem, aiming at maximizing the derived achievable rate lower bound subject to average transmit and interference power constraints. We optimize the durations of spatial spectrum sensing and channel training as well as data symbol transmission power. Our numerical results demonstrate that between optimizing spectrum sensing and channel training durations, the latter is more important for providing higher achievable rates.

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