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

• Published in: IEEE transactions on cognitive communications and networking Vol. 7; no. 3; pp. 802 - 817
• Main Authors: Yazdani, Hassan, Vosoughi, Azadeh, Gong, Xun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Achievable rates; Antennas; beam detection; beam selection; Beam steering; Channel estimation; Cognitive radio; Constraints; Data communication; Data transmission; imperfect spectrum sensing; Interference; Lower bounds; opportunistic cognitive radio system; optimal and sub-optimal transmit power; Optimization; reconfigurable antennas; Reconfiguration; Sensors; Symbols; Training; training and data symbols; Transmitting antennas
• ISSN: 2332-7731; 2332-7731
• DOI: 10.1109/TCCN.2021.3056691

We consider an opportunistic cognitive radio (CR) system in which secondary transmitter (SU tx ) 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, SU tx senses the spectrum and detects the beam corresponding to active primary user's (PU) location. SU tx also sends training symbols (prior to data symbols), to enable channel estimation at secondary receiver (SU rx ) and selection of the strongest beam between SU tx - SU rx for data transmission. We establish a lower bound on the achievable rates of SU tx - SU rx 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.