On energy cooperation in energy harvesting underlay cognitive radio network

Pathak, Kalpant; Banerjee, Adrish

doi:10.1109/ncc.2016.7561123

Cited by 5 publications

(10 citation statements)

References 14 publications

(21 reference statements)

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“…In [5], the authors obtained an optimal offline power control policy for EH-secondary transmitter (ST) using geometric water-filling with peak power constraints that maximized ST's throughput. In [6], the authors considered energy cooperation between the EH-ST and EH-primary transmitter (PT) and obtained myopic and offline cooperation protocols in single and multislot settings, respectively, that maximized the throughput of SU. In [7], the authors designed a robust transceiver for the simultaneous wireless information and power transfer (SWIPT) based multiple-input multiple-output (MIMO) underlay CRN.…”

Section: A Background Workmentioning

confidence: 99%

“…The aforementioned works on optimal transmission policies in underlay EH-CRNs focused mainly on myopic [6]- [8], [11]- [13], offline [5], [6], [9] and online policies [10], [14], [16]. The myopic policies aim to maximize the immediate throughput and do not care about the past and future channel conditions and energy arrivals, which is suboptimal in multislot scenarios.…”

Section: A Background Workmentioning

confidence: 99%

“…The transmit power of the PT is assumed constant in all slots. Different from the system model proposed in [6], [9], [10] where each slot was shared between energy harvesting and data transmission (time-sharing model), in our model, each slot is either used for energy harvesting or data transmission. Considering harvest-or-transmit model over the time-sharing model reduces the size of action space which reduces the complexity of dynamic programming algorithm for the online policy [31].…”

Section: B Motivation and Contributionmentioning

confidence: 99%

“…To employ dynamic programming algorithms for solving the discrete time MDPs, we need to quantize the action space (which might be transmit power and time-sharing parameter). For the time-sharing based models considered in [6], [9], [10], if time-sharing parameter and transmit power can take values from finite sets with N T and N P number of elements, respectively, the total size of the action set would be N T × N P . On the other hand, in the harvest-or-transmit model, the size of action set would be N P + 1.…”

Section: B Motivation and Contributionmentioning

confidence: 99%

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Harvest-or-Transmit Policy for Cognitive Radio Networks: A Learning Theoretic Approach

Pathak

Banerjee

2019

IEEE Trans. on Green Commun. Netw.

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We consider an underlay cognitive radio network where the secondary user (SU) harvests energy from the environment. We consider a slotted-mode of operation where each slot of SU is used for either energy harvesting or data transmission. Considering block fading with memory, we model the energy arrival and fading processes as a stationary Markov process of first order. We propose a harvest-or-transmit policy for the SU along with optimal transmit powers that maximize its expected throughput under three different settings. First, we consider a learning-theoretic approach where we do not assume any apriori knowledge about the underlying Markov processes. In this case, we obtain an online policy using Q-learning. Then, we assume that the full statistical knowledge of the governing Markov process is known apriori. Under this assumption, we obtain an optimal online policy using infinite horizon stochastic dynamic programming. Finally, we obtain an optimal offline policy using the generalized Benders decomposition algorithm. The offline policy assumes that for a given time deadline, the energy arrivals and channel states are known in advance at all the transmitters. Finally, we compare all policies and study the effects of various system parameters on the system performance.

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Section: A Background Workmentioning

confidence: 99%

Section: A Background Workmentioning

confidence: 99%

Section: B Motivation and Contributionmentioning

confidence: 99%

Section: B Motivation and Contributionmentioning

confidence: 99%

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Harvest-or-Transmit Policy for Cognitive Radio Networks: A Learning Theoretic Approach

Pathak

Banerjee

2019

IEEE Trans. on Green Commun. Netw.

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“…The promising solutions to prolonging the lifetime of power-constrained wireless nodes consist of power control design, energy harvesting and wireless power transfer (WPT) technique. To date, there are some literatures about the energy harvesting technique powering the wireless networks in the underlay spectrum sharing area [9][10][11][12]. The energy harvesting technology for underlay CRNs was considered [9] and the optimal transmission power and energy transfer policy was obtained in a single slot for maximizing the number of bits transmitted by SU under the primary sum-rate constraint in an offline setting.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of underlay two-way relay cooperation in cognitive radio networks with energy harvesting

Zeng

et al. 2018

Computer Networks

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Cognitive radio and energy harvesting are two important approaches to solve the problem of spectrum scarcity and energy constraint in wireless communications. In this work, we study a two-way relay cooperation scheme in underlay cognitive radio networks (CRNs) with energy harvesting in which two secondary users exchange information via an energy harvesting relay node. Since the relay node collects energy from the received signal and utilizes it to forward the received signal, the secondary transmission power can be markedly reduced. Hence the interference of the secondary networks to the primary networks can be substantially reduced. We derive the outage probability of the secondary networks and analyze the ergodic sum-rate of the secondary networks. For the relay selection scheme, we find the optimal relay by interior point method based on penalty function. Numerical results show that the proposed scheme gives higher throughput for the secondary networks than other strategies.

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Resource Allocation in Multi-Cluster Cognitive Radio Networks With Energy Harvesting for Hybrid Multi-Channel Access

Olawole

Takawira

2023

IEEE Access

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Cognitive radio and energy harvesting techniques have been able to provide insight for solutions to the inefficiency in spectrum utilization and the effects of the limited storage capacity of energy batteries. Nevertheless, the requirement for interference constraint for primary user (PU) protection, the quality of service (QoS) requirements of the SUs, and the low power density of the ambient electromagnetic waves have restricted the performance of the radio frequency powered cognitive radio networks (RF-CRNs) in terms of the achievable data rate for specific energy budget or sufficient energy budget for target data rate. We are therefore motivated to investigate a radio frequency powered cognitive radio (RF-CR) with the capacity for improved achievable throughput and energy harvesting. Our model consists of multiple PUs and SUs coexisting in a practical overlapping clustered network. In each time frame for the adopted time division multiple access (TDMA) technique, SUs can exploit the multi-user benefit to harvest energy from both the PU and SUs transmissions for improved active probability. Therefore, taken into consideration the heterogeneity of each SUs in terms of their signal-to-noise (SNR), the energy harvesting rates and the inter-and intra-cluster interference, the problem is formulated into a fractional nonlinear optimization to determine the sensing duration, and power allocations that maximize the average energy efficiency of the RF-CRN subject, to constraints on energy causality, and PU protection. The performance of the proposed hybrid multi-channel access scheme is studied in terms of the trade-off between the total achievable throughput by the secondary users and the interference generated among others. Simulation results show that a trade-off exists between achieving optimal spectral efficiency and energy efficiency and, the optimal transmit power therefore depends on the primary design objective. Results equally show that the proposed hybrid multi-channel access scheme outperforms the existing scheme in literature.INDEX TERMS Energy efficiency, hybrid multi-channel access, multi-cluster network, radio frequency powered cognitive radio network (RF-CRN), resource allocation (RA).

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On energy cooperation in energy harvesting underlay cognitive radio network

Cited by 5 publications

References 14 publications

Harvest-or-Transmit Policy for Cognitive Radio Networks: A Learning Theoretic Approach

Harvest-or-Transmit Policy for Cognitive Radio Networks: A Learning Theoretic Approach

Performance analysis of underlay two-way relay cooperation in cognitive radio networks with energy harvesting

Resource Allocation in Multi-Cluster Cognitive Radio Networks With Energy Harvesting for Hybrid Multi-Channel Access

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