1,649 research outputs found
Cognitive and Energy Harvesting-Based D2D Communication in Cellular Networks: Stochastic Geometry Modeling and Analysis
While cognitive radio enables spectrum-efficient wireless communication,
radio frequency (RF) energy harvesting from ambient interference is an enabler
for energy-efficient wireless communication. In this paper, we model and
analyze cognitive and energy harvesting-based D2D communication in cellular
networks. The cognitive D2D transmitters harvest energy from ambient
interference and use one of the channels allocated to cellular users (in uplink
or downlink), which is referred to as the D2D channel, to communicate with the
corresponding receivers. We investigate two spectrum access policies for
cellular communication in the uplink or downlink, namely, random spectrum
access (RSA) policy and prioritized spectrum access (PSA) policy. In RSA, any
of the available channels including the channel used by the D2D transmitters
can be selected randomly for cellular communication, while in PSA the D2D
channel is used only when all of the other channels are occupied. A D2D
transmitter can communicate successfully with its receiver only when it
harvests enough energy to perform channel inversion toward the receiver, the
D2D channel is free, and the at the receiver is above the
required threshold; otherwise, an outage occurs for the D2D communication. We
use tools from stochastic geometry to evaluate the performance of the proposed
communication system model with general path-loss exponent in terms of outage
probability for D2D and cellular users. We show that energy harvesting can be a
reliable alternative to power cognitive D2D transmitters while achieving
acceptable performance. Under the same outage requirements as
for the non-cognitive case, cognitive channel access improves the outage
probability for D2D users for both the spectrum access policies.Comment: IEEE Transactions on Communications, to appea
Optimization of the overall success probability of the energy harvesting cognitive wireless sensor networks
Wireless energy harvesting can improve the performance of cognitive wireless sensor networks (WSNs). This paper considers radio frequency (RF) energy harvesting from transmissions in the primary spectrum for cognitive WSNs. The overall success probability of the energy harvesting cognitive WSN depends on the transmission success probability and energy success probability. Using the tools from stochastic geometry, we show that the overall success probability can be optimized with respect to: 1) transmit power of the sensors; 2) transmit power of the primary transmitters; and 3) spatial density of the primary transmitters. In this context, an optimization algorithm is proposed to maximize the overall success probability of the WSNs. Simulation results show that the overall success probability and the throughput of the WSN can be significantly improved by optimizing the aforementioned three parameters. As RF energy harvesting can also be performed indoors, hence, our solution can be directly applied to the cognitive WSNs that are installed in smart buildings
On Spectrum Sharing Between Energy Harvesting Cognitive Radio Users and Primary Users
This paper investigates the maximum secondary throughput for a rechargeable
secondary user (SU) sharing the spectrum with a primary user (PU) plugged to a
reliable power supply. The SU maintains a finite energy queue and harvests
energy from natural resources and primary radio frequency (RF) transmissions.
We propose a power allocation policy at the PU and analyze its effect on the
throughput of both the PU and SU. Furthermore, we study the impact of the
bursty arrivals at the PU on the energy harvested by the SU from RF
transmissions. Moreover, we investigate the impact of the rate of energy
harvesting from natural resources on the SU throughput. We assume fading
channels and compute exact closed-form expressions for the energy harvested by
the SU under fading. Results reveal that the proposed power allocation policy
along with the implemented RF energy harvesting at the SU enhance the
throughput of both primary and secondary links
RF-Powered Cognitive Radio Networks: Technical Challenges and Limitations
The increasing demand for spectral and energy efficient communication
networks has spurred a great interest in energy harvesting (EH) cognitive radio
networks (CRNs). Such a revolutionary technology represents a paradigm shift in
the development of wireless networks, as it can simultaneously enable the
efficient use of the available spectrum and the exploitation of radio frequency
(RF) energy in order to reduce the reliance on traditional energy sources. This
is mainly triggered by the recent advancements in microelectronics that puts
forward RF energy harvesting as a plausible technique in the near future. On
the other hand, it is suggested that the operation of a network relying on
harvested energy needs to be redesigned to allow the network to reliably
function in the long term. To this end, the aim of this survey paper is to
provide a comprehensive overview of the recent development and the challenges
regarding the operation of CRNs powered by RF energy. In addition, the
potential open issues that might be considered for the future research are also
discussed in this paper.Comment: 8 pages, 2 figures, 1 table, Accepted in IEEE Communications Magazin
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