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CURATOR
A pinboard by
Kalpant Pathak

PhD Student, Indian Institute of Technology Kanpur

PINBOARD SUMMARY

Resource allocation algorithms for wireless networks which harvest energy from the environment

"Internet of things (IoT) is the future". IoT is the next generation concept in which billions of everyday objects are connected to the internet without requiring human interaction. Such devices are enabled with sensors to gather relevant information and share it with the intended receiver over the internet. These devices require energy for their sustained operation and frequent battery replacement is not possible in most of the cases. Therefore, energy harvesting techniques can be used to power IoT devices where the device harvests energy from the environmental sources such as solar energy, vibration energy, ambient RF energy etc. Energy harvesting ensures prolonged and low maintenance operation of these devices. However, the environmental energy is completely random in nature and therefore the design of optimal energy usage techniques is desirable, which not only gives the best data rate but uses the harvested energy efficiently as well. We are working on obtaining such optimal resource allocation algorithms which can be used in future wireless networks to maximize the data rate and energy efficiency simultaneously.

10 ITEMS PINNED

Optimal harvest-use-store policy for energy-harvesting wireless systems in frequency-selective fading channels

Abstract: Recent advances in energy-harvesting (EH) technology have enabled the realization of wireless systems composed of rechargeable devices. In this paper, we analyze the problem of maximizing the data transmission for the point-to-point (P2P) wireless communication systems which the transmitter is able to harvest energy from ambient environment. To be more general, we consider the EH optimal problem under the quasi-static frequency-selective fading channel. Our optimization work also includes energy storage loss constraint of the battery; therefore, we apply an efficient harvesting architecture, i.e., harvest-use-store (HUS), where the harvested energy is prioritized for use in data transmission. To balance the energy stored in or extracted from the battery for maximization throughput with the randomly arrival harvesting energy constraint, we first characterize the amazing properties of our optimal policy, implying a double-threshold structure of the solution, then investigate a dynamic programming (DP)-based double-layer optimal allocation policy. Further, we tend to analyze the online solution. First, the optimal policy is provided by using the continuous time stochastic dynamic programming. Then, building on the intuition from the optimal offline policy (i.e., double-threshold structure), a heuristic online policy is proposed, which is simple to be implemented. Numerical results are presented to validate the theoretical analysis and to demonstrate the superior performance over the existing counterparts in the previous literatures and show that the proposed online policies track well to the optimal solution.

Pub.: 10 Mar '15, Pinned: 30 Jul '17

Performance Analysis of Wireless Powered Communication with Finite/Infinite Energy Storage

Abstract: In this paper, we consider an energy harvesting (EH) node which harvests energy from a radio frequency (RF) signal broadcasted by an access point (AP) in the downlink (DL). The node stores the harvested energy in an energy buffer and uses the stored energy to transmit data to the AP in the uplink (UL). We consider a simple transmission policy, which accounts for the fact that in practice the EH node may not have knowledge of the EH profile nor of the UL channel state information. In particular, in each time slot, the EH node transmits with either a constant desired power or a lower power if not enough energy is available in its energy buffer. For this simple policy, we use the theory of discrete-time continuous-state Markov chains to analyze the limiting distribution of the stored energy for finite- and infinite-size energy buffers. Moreover, we take into account imperfections of the energy buffer and the circuit power consumption of the EH node. For a Rayleigh fading DL channel, we provide the limiting distribution of the energy buffer content in closed form. In addition, we analyze the average error rate and the outage probability of a Rayleigh faded UL channel and show that the diversity order is not affected by the finite capacity of the energy buffer. Our results reveal that the optimal desired transmit power by the EH node is always less than the average harvested power and increases with the capacity of the energy buffer.

Pub.: 08 Oct '14, Pinned: 30 Jul '17

On Optimal Policies in Full-Duplex Wireless Powered Communication Networks

Abstract: The optimal resource allocation scheme in a full-duplex Wireless Powered Communication Network (WPCN) composed of one Access Point (AP) and two wireless devices is analyzed and derived. AP operates in a full-duplex mode and is able to broadcast wireless energy signals in downlink and receive information data in uplink simultaneously. On the other hand, each wireless device is assumed to be equipped with Radio-Frequency (RF) energy harvesting circuitry which gathers the energy sent by AP and stores it in a finite capacity battery. The harvested energy is then used for performing uplink data transmission tasks. In the literature, the main focus so far has been on slot-oriented optimization. In this context, all the harvested RF energy in a given slot is also consumed in the same slot. However, this approach leads to sub-optimal solutions because it does not take into account the Channel State Information (CSI) variations over future slots. Differently from most of the prior works, in this paper we focus on the long-term weighted throughput maximization problem. This approach significantly increases the complexity of the optimization problem since it requires to consider both CSI variations over future slots and the evolution of the batteries when deciding the optimal resource allocation. We formulate the problem using the Markov Decision Process (MDP) theory and show how to solve it. Our numerical results emphasize the superiority of our proposed full-duplex WPCN compared to the half-duplex WPCN and reveal interesting insights about the effects of perfect as well as imperfect self-interference cancellation techniques on the network performance.

Pub.: 16 Mar '16, Pinned: 30 Jul '17