Throughput of a cooperative energy harvesting secondary user in cognitive radio networks

Abstract

This paper investigates the maximum throughput of a rechargeable secondary user (SU) sharing the spectrum with a primary user (PU). The SU is assumed to be an energy harvesting node, which harvests energy packets from the environmental energy sources (e.g. solar, wind and acoustics) with a certain harvesting rate. The PU is equipped with a data buffer to store the randomly arrived data packets at its receiver. In addition to its own data buffer, the SU is equipped with another data buffer for storing the unsuccessfully decoded packets at the primary destination in a given time slot due to channel fading. Moreover, it has an energy buffer for storing the energy packets harvested from the environment. We propose a new cooperative cognitive relaying protocol that allows the SU to gain channel access when it relays a fraction of the undelivered primary packets. We consider an interference channel model [or a multipacket reception (MPR) channel model], where simultaneous transmissions can be decoded correctly at the corresponding destinations with certain decoding probabilities characterised by the complement of channel outages under interference. Our proposed protocol exploits the PU's data burstiness because of the random and sporadic packet arrival at its queue. Moreover, the proposed protocol takes advantage of the receivers' MPR capabilities. In addition, it efficiently expends the secondary energy packets under the objective of secondary throughput maximisation. Our numerical results show the benefits of cooperation, receivers' MPR capabilities and secondary energy queue arrival rate on the system's performance from a network layer standpoint.

Cognitive radio network with continuous energy‐harvesting

“…In such situation, energy harvesting could be a better choice and a greenery solution to make the system self sufficient in terms of energy. A CR node can harvest energy from non-radio frequency (RF) sources (ambient sources such as solar, temperature, wind, and so on) and RF sources (PU signal, down link signal of cellular base stations, and so on) to replenish the batteries with energy [9,10].…”

Improving the lifetime of multichannel cognitive radio sensor networks via new spectrum sensing method

“…In order to overcome the mentioned challenges, a number of research works have focused on the reduction of energy amount specifically consumed for CSS . In addition, in order to provide a green platform for CRNs, some of the existing researches proposed methods based on energy harvesting to enhance the battery lifetimes of CRNs . Moreover, a review on the energy efficient CSS methods is presented in the work of Althunibat et al The authors discuss the energy consumption as a main challenge of CRNs and suggest several important guidelines in the design of CSS process.…”

“…[31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47] In addition, in order to provide a green platform for CRNs, some of the existing researches proposed methods based on energy harvesting to enhance the battery lifetimes of CRNs. [48][49][50][51] Moreover, a review on the energy efficient CSS methods is presented in the work of Althunibat et al 52 The authors 52 discuss the energy consumption as a main challenge of CRNs and suggest several important guidelines in the design of CSS process. Considering more realistic assumptions like faded sensing and reporting channels, paying attention to transmission energy amount of the scheduled sensor in the absence of PU, and the provision of simplicity in the implementation are samples of these guidelines.…”

Wireless energy harvesting in cooperative decode‐and‐forward relaying networks over mixed generalized η‐μ and κ‐μ fading channels

“…() In the literature, the performance of SWIPT has been analyzed in (1) point‐to‐point SWIPT‐based systems() and (2) cooperative relaying SWIPT‐based systems. () Varshney studied the trade‐off between transmitting information and transmitting energy over some discrete channels and an additive white Gaussian noise (AWGN) channel with an amplitude constraint on the input. In addition, a communication system that simultaneously meets large information per unit time and large received energy per unit time is characterized assuming an AWGN channel.…”