Game theory based spectrum sensing and transmission in energy harvesting hybrid cognitive radio networks (EH‐HCRN)

Abstract

Summary In an energy harvesting cognitive radio network (EH‐CRN), the correspondence among the primary transmitter (PT) and primary receiver (PR) for spectrum sensing and transmission becomes ineffective as a result of insufficient SNR, finite sensing duration and interferences. We recommend designing a suitable spectrum sensing structure based on game theory for energy harvesting hybrid CRN to resolve these complications. This comprises three phases: (i) co‐operative relay approach for primary user (PU) transmission; (ii) optimal sensing interval estimation; and (iii) co‐operative energy efficient spectrum sensing and transmission. Initially, a batch of suitable secondary transmitters (STs) are identified as relay nodes for the PTs in the first phase. Next, in the second phase, a sensing interval that maximizes a utility function devised concerning average throughput, collision probability regarding secondary user (SU) and energy detector threshold is determined by applying the game theory model. Finally, co‐operative transmission is utilized in the third phase if the PR could not decode the signal from PT via STs. The presented framework is simulated using NS2, and its efficiency is estimated in terms of the parameters like transmission latency, packet loss, residual energy and possibilities of successful data delivery.

Research on comprehensive energy metering acquisition and monitoring based on feature detector

“…Secondary transmitters can use different detectors for spectrum sensing to judge the presence of the main user signal [9][10][11]，Its detection probability 𝒲 𝒾 and false alarm probability 𝒲 𝒿 are discussed as follows: As can be seen from the spectrum of the master user network model above, 𝒴 𝓍 =1 is occupied and 𝒴 𝓍 =0 is idle.𝒴 𝓍 =0 However , ℱ ℛ 𝒴 𝒾 𝒴 .Then, the formula for the probability 𝒲 𝒾 of feature detector (MME) and the probability of false alarm 𝒲 𝒿 is:…”