Abstract-RF energy harvesting can be used to power communication devices so that perpetual operation of such devices can be ensured. We consider a RF energy harvesting underlay cognitive radio system operating in slotted fashion. The primary user (PU) is equipped with a reliable power source and transmits with a constant power in all the slots. However, the secondary user (SU) harvests energy from primary's transmission and simultaneously transmits it's own data such that interference at the primary receiver (PR) remains below an acceptable threshold. At the secondary transmitter (ST), each time slot is divided into two phases: energy harvesting (EH) phase and information transfer (IT) phase. We formulated the problem of maximizing the achievable secondary sum rate under primary receiver's protection criteria as a convex optimization problem and obtained the optimal time sharing between EH and IT phase, and optimal secondary transmit power under offline setting. The optimal offline scheme is then compared with an online myopic policy, where the optimal time sharing between EH and IT phase, and optimal secondary transmit power are obtained based on instantaneous channel gains only.
Abstract-In this paper, we consider an energy harvesting cognitive radio network (EH-CRN), where a primary and a secondary user coexist in underlay mode. Both the transmitters have energy harvesting capability and are equipped with finite capacity battery to store the harvested energy. In addition, the secondary user (SU) has an independent energy transfer unit such that it can transfer some portion of it's harvested energy to the primary user (PU). We obtain an optimal transmit power and energy transfer policy for single-slot and a suboptimal policy for multi-slot scenario maximizing the number of bits transmitted by SU under the primary sum-rate constraint in an offline setting. For both cases, the effect of energy cooperation on the system performance is studied and it is observed that energy cooperation results in higher SU throughput.
We consider an underlay cognitive radio network where the secondary user (SU) harvests energy from the environment. We consider a slotted-mode of operation where each slot of SU is used for either energy harvesting or data transmission. Considering block fading with memory, we model the energy arrival and fading processes as a stationary Markov process of first order. We propose a harvest-or-transmit policy for the SU along with optimal transmit powers that maximize its expected throughput under three different settings. First, we consider a learning-theoretic approach where we do not assume any apriori knowledge about the underlying Markov processes. In this case, we obtain an online policy using Q-learning. Then, we assume that the full statistical knowledge of the governing Markov process is known apriori. Under this assumption, we obtain an optimal online policy using infinite horizon stochastic dynamic programming. Finally, we obtain an optimal offline policy using the generalized Benders decomposition algorithm. The offline policy assumes that for a given time deadline, the energy arrivals and channel states are known in advance at all the transmitters. Finally, we compare all policies and study the effects of various system parameters on the system performance.
This paper considers an energy harvesting underlay cognitive radio network operating in a slotted fashion. The secondary transmitter scavenges energy from environmental sources in half duplex fashion and stores it in finite capacity rechargeable battery. It splits each slot into two phases: harvesting phase and transmission phase. We model the energy availability at the secondary user as first order stationary Markov process. We propose a robust online transmission policy by jointly optimizing the time sharing between the two phases and transmit power of secondary user, which maximizes its average throughput by a given time deadline. We show the comparison of our proposed policy with the offline and myopic policies.
We consider a wireless network where multiple energy harvesting transmitters communicate with the common receiver in a time sharing manner. In each slot, a transmitter can either harvest energy or send its data to the receiver. Given a time deadline, the goal is to maximize the sum-rate of transmitters under random energy arrivals with both perfect and imperfect channel state information (CSI) at the receiver. The original sumrate maximization (SRM) problem is a non-convex mixed integer non-linear program (MINLP). To obtain the optimal scheduling policy, we first reduce the original optimization problem to a convex MINLP and solve it using the generalized Benders decomposition algorithm. We observe that the SRM problem results in an unfair rate allocation among transmitters, i.e., the transmitter closer to the receiver achieves a higher rate than that by the transmitter farther from the receiver. Hence, to induce fairness among transmitters, we consider the minimumrate maximization (MRM) problem. For the bounded channel estimation error, we obtain a robust scheduling policy by solving the worst-case SRM and MRM problems. Finally, we compare the proposed policies with myopic policies studied in the literature and show that the former outperform the latter in terms of achievable rates.
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