Achievable Throughput Optimization in Energy Harvesting Cognitive Radio Systems - Yin, Sixing; Qu, Zhaowei; Li, Shufang (IEEE journal on selected areas in communications, 2015)
In this paper, we consider an energy harvesting cognitive radio (CR) system operating in slotted mode, where the secondary user (SU) has no wired power supplies and is powered exclusively by energy harvested from ambient environment. The SU can only perform either energy harvesting, spectrum sensing or data transmission at a time due to hardware limitation such that a timeslot is segmented into three non-overlapping fractions. Considering a generalized multi-slot spectrum sensing paradigm and two types of fusion rules: data fusion and decision fusion, we focus on the "harvesting-sensing-throughput" tradeoff and joint optimization for save-ratio, sensing duration, sensing threshold as well as fusion rule to maximize the SU's expected achievable throughput while keeping primary users (PUs) sufficiently protected. For data-fusion spectrum sensing, we translate the original problem into a convex one and show that the optimal solutions for sample number, mini-slot number as well as sensing threshold are non-unique. For decision-fusion spectrum sensing, we propose a two-level algorithm to solve the original problem with in-depth analysis on the convexity of a simplified problem and experiments show that the proposed algorithm is more efficient than differential evolution algorithm. We find that despite the inherent difference between the two types of fusion rules, the optimal data-fusion and decision-fusion strategies both converge to single-slot spectrum sensing while the SU's maximal expected achievable throughput is attained. Simulation results show that the optimal single-slot spectrum sensing strategy outperforms three other multi-slot strategies as well as two existing strategies while the empirical probability of detection is limited under a predefined level.