2019
DOI: 10.1177/1550147719877630
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Energy-efficient collaborative transmission algorithm based on potential game theory for beamforming

Abstract: A group of collaborative nodes can efficiently complete spatial long-distance transmission tasks using beamforming technology. However, a high sidelobe level interferes with communication quality, and uneven energy consumption of nodes affects network lifetime. This paper proposes an energy-efficient collaborative transmission algorithm based on potential game theory for beamforming. First, the minimum number of cooperative nodes is determined in accordance with the energy consumption and spacing limitation co… Show more

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Cited by 6 publications
(5 citation statements)
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“…Thus, the other energy consumed by users is ignored. 28 Equation (3) represents the energy consumption of transmitting and receiving sensing data…”
Section: System Modelmentioning
confidence: 99%
“…Thus, the other energy consumed by users is ignored. 28 Equation (3) represents the energy consumption of transmitting and receiving sensing data…”
Section: System Modelmentioning
confidence: 99%
“…The expected number of sensors in an area I can be calculated as S = ρπd max . However, the output function of the area as f N (ℵ) = ρ κ , in the range κ ∈ 0 ≤ ℵ ≤ N of each of the ℵ of N grouped by clusters of collaborative sensors of probability of size 1 ℵ . Therefore, the expected number of collaborative sensors in each cluster is given by ℵ = S N Linear beamforming technique implies that every sensor directs its mainbeam towards the geometric center of the network, where the connection can be easily established if any two sensors are aligned within each other's mainbeam.…”
Section: Connectivity Modelmentioning
confidence: 99%
“…Moreover, CB distributes power consumption overall sensors when the collaboration involves ℵ κ sensors up to an N κ [41]. The gain G r is defined as the maximum in mainlobe depending on the network topology as well as the minimum value of an 1 N κ fold with the reduction in the received power everywhere else [41]. An example of IoT application is monitoring rural areas by deploying sensors.…”
Section: Connectivity Modelmentioning
confidence: 99%
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“…The energy consumption of MU i mainly includes the energy consumption of sending and receiving data during the sensing task, and other energy consumption used by MUs can be ignored [ 29 ]. Equation (6) represents the energy consumption of transmitting and receiving data.…”
Section: System Model and Game Formulationmentioning
confidence: 99%