Decision making policy for RF energy harvesting enabled cognitive radios in decentralized wireless networks

Abstract

. Decision making policy for RF energy harvesting enabled cognitive radios in decentralized wireless networks. Digital Signal Processing, Elsevier, 2017Elsevier, , 60, pp.33-45. 10.1016Elsevier, /j.dsp.2016. hal-01436769 Highlights• Decision making policy for RF Energy Harvesting enabled Cognitive Radio.• Thompson Sampling algorithm based subband statistic estimation and subband access.• Proposed USRP testbed to validate the functionality of the various DMPs. Decision AbstractCognitive radio (CR) paradigm with radio frequency energy harvesting (RFEH) have significant potential to improve the network throughput by utilizing vacant spectrum using battery-operated self-sustainable radio terminals. Research efforts relevant to these paradigms are focused on the mode selection policies which decide when to switch from CR mode (i.e., opportunistic vacant spectrum access mode) to the RFEH mode (i.e., battery charging using ambient RF energy) and vice-versa. So far, very little attention has been paid to the dual but competing task of frequency band selection in CR and RFEH modes under partially observable environment in the decentralized wireless networks. Furthermore, the need of tunable bandwidth frequency band access for CRs and lower subband switching cost (SSC) for energy efficient implementation have made the design of the decision making policy (DMP) more challenging. In this paper, a new CR-RFEH DMP has been proposed for RFEH enabled CR terminals in the decentralized wireless networks. The proposed DMP consists of three sub-units: 1) Bayesian approach based tunable Thompson sampling algorithm for subband statistics estimation, 2) Thompson sampling algorithm based subband access scheme exploiting the past collision events to minimize collisions among CRs, and 3) Mode selection scheme. Simulation results show that the proposed DMP offers 10-35% improvement in the throughput of the decentralized network and 40-90% reduction in the number of subband switchings compared to existing DMPs. The simulation results are then validated using real radio signals on the proposed USRP testbed.

Distributed decision making policy for frequency band selection boosting RF energy harvesting rate in wireless sensor nodes

“…The DMPs for frequency band selection in RFEH enabled secondary users have been recently proposed in [22,23]. In [22,23], we have introduced RFEH in the context of D2D communications for the cognitive radio network.…”

“…The DMPs for frequency band selection in RFEH enabled secondary users have been recently proposed in [22,23]. In [22,23], we have introduced RFEH in the context of D2D communications for the cognitive radio network. The DMP in [22,23] improves the average spectrum utilization by minimizing collisions between users and dynamically switching between the D2D and RFEH modes.…”

“…In [22,23], we have introduced RFEH in the context of D2D communications for the cognitive radio network. The DMP in [22,23] improves the average spectrum utilization by minimizing collisions between users and dynamically switching between the D2D and RFEH modes. However, the usefulness and performance analysis of Bayesian online learning algorithms for frequency band characterization has not been explored yet in the literature.…”

“…Existing DMPs [13][14][15][17][18][19][20][21][22][23] consider RFEH from single RF source which uninterruptedly transmits in a fixed and known frequency band. In dynamic spectrum environment consisting of multiple ambient RF sources and limited bandwidth RFEH circuits, WSN nodes need intelligence, i.e.…”

Wi-Fi Backscatter System with Tag Sensors Using Multi-Antennas for Increased Data Rate and Reliability

“…Accordingly, powerless or ultra-low-power wireless communications have also been widely proposed because power saving is a very important issue in IoT devices [4,5]. There are various technologies to transmit data through ambient radio frequency (RF) radio signals [6][7][8]. One of these technologies is a Wi-Fi backscatter system released several years ago [9].…”