The paper proposes a closed-form power map that adapts a sensor's transmit power to its battery state and quantized channel gain to maximize J-divergence at a fusion center, but the battery-state update in the derivation neglects local detection errors.
Beam Selection and Discrete Power Allocation in Opportunistic Cognitive Radio Systems with Limited Feedback Using ESPAR Antennas
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abstract
We consider an opportunistic cognitive radio (CR) system consisting of a primary user (PU), secondary transmitter (SUtx), and secondary receiver (SUrx), where SUtx is equipped with an electrically steerable parasitic array radiator (ESPAR) antenna with beam steering capability for sensing and communication, and there is a limited feedback channel from SUrx to SUtx. Taking a holistic approach, we develop a framework for integrated sector-based spectrum sensing and sector-based data communication. Upon sensing the channel busy, SUtx determines the beam corresponding to PU's orientation. Upon sensing the channel idle, SUtx transmits data to SUrx, using the selected beam corresponding to the strongest channel between SUtx and SUrx. We formulate a constrained optimization problem, where SUtx-SUrx link ergodic capacity is maximized, subject to average transmit power and interference constraints, and the optimization variables are sensing duration, thresholds of channel quantizer at SUrx, and transmit power levels at SUtx. Since this problem is non-convex we develop a suboptimal computationally efficient iterative algorithm to find the solution. Our numerical results quantify the capacity improvement provided by the ESPAR antenna and demonstrate that our CR system yields lower outage and symbol error probabilities, compared with a CR system that its SUtx has an omni-directional antenna.
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2019 1verdicts
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Power Adaptation for Distributed Detection in Energy Harvesting WSNs with Finite-Capacity Battery
The paper proposes a closed-form power map that adapts a sensor's transmit power to its battery state and quantized channel gain to maximize J-divergence at a fusion center, but the battery-state update in the derivation neglects local detection errors.