In this paper, we investigate an energy harvesting scheme in a smart grid based on the cognitive relay protocol, where a primary transmitter scavenges energy from the nature sources and then employs the harvested energy to forward the primary signal. Depending on the intensity of the energy harvesting from nature, a secondary user dynamically acts as a relay node to assist the primary transmission or does not. When the energy is not enough powerful to support the direct transmission between two primary users, the secondary users share the spectrum by assisting the primary transmission. For the relaying scheme, both amplify-and-forward (AF) and decode-and-forward (DF) protocols are investigated. We analytically obtain the exact transmission rates for both primary and secondary networks and derive the exact expressions of the system outage probabilities for both primary and secondary users in the smart grid. Moreover, we develop the analytically optimal bandwidth allocation strategy to maximize the total sum rate of the proposed scheme. Numerical results are presented to demonstrate the performance gain of the proposed scheme over the nonoptimal scheme.
Spectral efficiency has attracted much attention in wireless smart grid communications networks due to the increasing demands of wireless services [
In smart grid, harvesting energy from nature such as solar, radio frequency, or wind provides a new free source of energy for the wireless nodes. With nature energy harvesting technique, wireless devices can utilize the free renewable energy sources to improve the energy efficiency [
In this paper, considering the massive potential of harvesting energy from nature resources, we adopt this technique to cognitive radio network where we assume the primary user lacks of energy and thus harvests energy from the nature resources. Due to the fluctuation of energy harvesting from nature sources, we model and propose an energy harvesting and relaying protocol with dynamic bandwidth allocation in cognitive relay networks. The protocol enables a secondary user dynamically sharing the primary spectrum by assisting the primary transmission if the direction transmission between two primary users cannot be effectively realized. In particular, the energy harvesting primary user first scavenges energy from ambient sources and then decides to cooperate with the secondary network or transmit information directly to its destination based on the intensity of the harvesting energy. If the direct transmission data rate meets the basic threshold requirement, the direct transmission between two primary users occurs and some bandwidth is allocated to the secondary network. If the direct transmission data rate is below the threshold, the secondary user acts as the relay to assist the primary transmission and the secondary network can share some bandwidth for its own transmission in return. We first derive the analytical expressions of the data rates for the primary and secondary networks. Accordingly, the system outage probabilities for PD and SD are then derived for different transmission schemes. In order to maximize the sum data rate, the optimal bandwidth allocation strategies are analyzed and proposed. Furthermore, the correspondingly analytical policies are derived to solve the optimization problems. Finally, numerical results provide us with the valuable insights into the performance under various system parameters, i.e., the energy arrival rate and the location of the relay.
An outline of the remainder of the paper is as follows. Section
In this section, we begin by describing the smart grid system with the cognitive relay model based on energy harvesting.
We consider a cognitive network which is composed of four nodes, as shown in Figure
System model of the energy harvesting and relaying protocol for the cognitive relay networks.
We suppose that the secondary user SS has a constant power supply, i.e.,
The time slot
Accordingly, the mean transmitted power can be expressed as
Because of the nature fluctuation, PS sometimes harvests high energy, and sometimes, it harvests low energy from the nature sources. Accordingly, we propose the following energy harvesting and relaying protocol: When the harvesting energy is powerful enough to support the direct transmission between two primary nodes, the primary network operates in the direct transmission mode and If the data rate of the direct transmission between two primary nodes cannot meet the threshold requirement, the secondary node SS acts as the relay to assist the primary transmission. As return, the primary network allocates some bandwidth to the secondary network for its own transmission and the rest bandwidth
At the beginning of each time slot, PS ascertains the availability of direct transmission between two primary users, and the test function is given as
If
If
During the second phase, SS uses half of its power to broadcast the received primary signals and employs the rest power to transfer signals intended for SD. The information transmitted in frequency band
The corresponding received signal at primary user PD in frequency band
Accordingly, we the data rates for PD and SD, respectively, as follows:
In fact, the noise introduced by the received antenna is much smaller than that of the baseband noise power. Without loss of generality, we assume the antenna noise power to be zero [
If
If
In this section, we investigate the system outage performance in Rayleigh fading environments. If the transmission rates of PD or SD is lower than the given target rate, the system occurs an outage event. Therefore, we have the following proposition for the primary user PD.
Accordingly, we have the outage probability
Let
Therefore, we have
Based on (
Similarly, the system outage probability for the secondary user SD is given by the following proposition.
Similar to the proof of Proposition 1, we can have this proposition.
In this section, we attempt to maximize the sum rate of the whole system for both the AF and DF scheme. However, this depends on the value of
The Optimization Problem 1 (OP1) for the AF scheme can be formulated as
In order to simplify the expression of
Symbol notations.
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The Optimization Problem 2 (OP2) for the DF scheme can be formulated as
For the sake of simplicity, we rewrite where where
In this section, we present some numerical results to verify the effectiveness of the proposed protocol and the analytical optimization solutions. Besides, the derived analytical results are employed to provide insights into the different parameter’s impact on the whole system. In the simulation, the distance between PS and PD is normalized. Let
In Figure
System outage probability vs.
In Figure
Sum data rate vs. distance
In Figure
Sum data rate vs. energy arrival rate
Sum data rate vs. the relay’s power
In Figure
Bandwidth allocation vs. energy arrival rate
In this paper, we have investigated an energy harvesting and relaying protocol in the smart grid with cognitive relay networks. The proposed protocol enables the secondary network opportunistically share the spectrum licensed to the primary network by assisting the primary transmission if the direct transmission between two primary users cannot meet the threshold requirement. The exact expressions of data rate for both primary and secondary networks are derived under different relaying schemes, namely, AF and DF schemes. We then derive the system outage probability of PD and SD for both two transmission schemes. Furthermore, in order to improve the performance of whole system, we formulate optimization problems which are in purpose of achieving the maximum sum data rates. For each scheme, we derive the correspondingly analytical solution. Subsequently, the numerical results verify the accuracy of theoretical derivations. Besides, simulation results demonstrate that the proposed spectrum sharing with energy harvesting in this protocol can achieve better transmission rate compared with the direct transmission. Finally, the numerical results provide us with valuable insights into the influence of various system parameters on the system performance.
The numerical results are directly calculated from the mathematical derivations we provide in this paper. All data are reliable and valid.
The work was partly presented in IEEE/CIC ICCC 2016 [
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This work was supported by State Grid Shanghai Municipal Electric Power Company (Grant: 52094014001V).