Network Throughput Optimization for Random Access Narrowband Cognitive Radio Internet of Things (NB-CR-IoT)

Li, Ting; Yuan, Jie; Torlak, Murat

doi:10.1109/jiot.2017.2789217

Cited by 89 publications

(44 citation statements)

References 28 publications

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“…Table 2 summarizes the simulated network parameters [27], [76]. 14 Fig. 2 illustrates the IoT network GoodPut as a function of the number of packets N in a frame, for N rep = 1 and T s = 2 ms. As can be seen, the GoodPut increases as N increases, and peaks at N = 7.…”

Section: Numerical Resultsmentioning

confidence: 99%

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Availability-Reliability-Stability Trade-Offs in Ultra-Reliable Energy-Harvesting Cognitive Radio IoT Networks

Amini

Baidas

2020

IEEE Access

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With the rise of the Internet-of-Things (IoT), new requirements have been brought into communication networks to make them more efficient, sustainable, and self-sufficient. Requirements, such as availability and ultra-reliability combined with the solutions of energy-harvesting and dynamic spectrum access, make the analyses of such networks more complex, while imposing different performance trade-offs. This paper analyzes the performance of ultra-reliable energy-harvesting cognitive radio Internet-of-Things (UR-EH-CR-IoT) networks, and provides analytical derivations for different IoT network metrics, such as GoodPut, reliability, collision probability, availability, and stability, so as to investigate their trade-offs. A new metric for network availability is defined based on energy availability and spectrum accessibility for UR-EH-CR-IoT networks, while incorporating transmission diversity. The effect of IoT network parameters, such as sensing time, diversity transmission, and number of packets in a data frame, is examined on the IoT network performance metrics. Lastly, the derived expressions are utilized to optimize the GoodPut, subject to various practical constraints. INDEX TERMS Availability, cognitive radio, collision probability, diversity transmission, energyharvesting, Internet-of-Things, stability, ultra-reliability. 1 This can be seen by noting that diversity transmission is only based on transmitting a fixed number of packet replicas. To the best of our knowledge, there are no existing results on the implementation complexity of diversity transmission.

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Section: Numerical Resultsmentioning

confidence: 99%

“…For instance, a narrowband IoT network with random-access CR users has been considered in [14], where the authors derived a unique set of optimal sensing parameters to maximize network throughput. In [15], network metrics such as GoodPut, collision probability and network stability have been investigated in an EH-CR-IoT network.…”

Section: A Related Workmentioning

confidence: 99%

Availability-Reliability-Stability Trade-Offs in Ultra-Reliable Energy-Harvesting Cognitive Radio IoT Networks

Amini

Baidas

2020

IEEE Access

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“…The demand for the radio frequency (RF) spectrum is rising due to the development of new devices and services [1]. The evolving communication technologies require the use of the RF spectrum for their functionality.…”

Section: Introductionmentioning

confidence: 99%

Analysis of radio frequency spectrum usage using cognitive radio

Shuaibu-Sadiq

Anyasi

2020

J Electri Contr Telecom Res

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This paper presents the analysis of radio frequency (RF) spectrum usage using cognitive radio. The aim was to determine the unused spectrum frequency bands for efficiently utilization. A program was written to reuse a range of vacant frequency with different model element working together to produce a spectrum sensing in MATLAB/Simulink environment. The developed Simulink model was interfaced with a register transfer level - software defined radio, which measures the estimated noise power of the received signal over a given time and bandwidth. The threshold estimation performed generates a 1\0 output for decision and prediction. It was observed that some spectrum, identified as vacant frequency, were underutilized in FM station in Benin City. The result showed that when cognitive radio displays “1” output, which is decision H1, the channel is occupied and cannot be used by the cognitive radio for communication. Conversely, when “0” output (decision H0) is displayed, the channel is unoccupied. There is a gradual decrease in the probability of detection (Pd), when the probability of false alarm (Pfa) is increased from 1% to 5%. In the presence of higher Pfa, the Pd of the receiver maintains a high stability. Hence, the analysis finds the spectrum hole and identifies how it can be reused

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“…For (resource/power) constrained IoT devices, simple signal processing techniques are needed to save energy, and reduce system cost. Hence, the simplest non-coherent energy detection (as sensing technique) is preferred as in the previous research [10,11]. In our proposed system, we also consider energy based sensing.…”

Section: Introductionmentioning

confidence: 99%

Spatial–Temporal Sensing and Utilization in Full Duplex Spectrum-Heterogeneous Cognitive Radio Networks for the Internet of Things

Khalid

2019

Sensors

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The continuous growth of interconnected devices in the Internet of Things (IoT) presents a challenge in terms of network resources. Cognitive radio (CR) is a promising technology thatcan address the IoT spectral demands by enabling an opportunistic spectrum access (OSA) scheme. The application of full duplex (FD) radios in spectrum sensing enables secondary users (SUs) to perform sensing and transmission simultaneously, and improves the utilization of the spectrum. However, random and dense distributions of FD-enabled SU transmitters (FD-SU TXs) with sensing capabilities in small-cell CR-IoT environments poses new challenges, and creates heterogeneous environments with different spectral opportunities. In this paper, we propose a spatial and temporal spectral-hole sensing framework for FD-SU TXs deployed in CR-IoT spectrum-heterogeneous environment. Incorporating the proposed sensing model, we present the analytical formulation and an evaluation of a utilization of spectrum (UoS) scheme for FD-SU TXs present at different spatialpositions. The numerical results are evaluated under different network and sensing parameters to examine the sensitivities of different parameters. It is demonstrated that self-interference, primary user activity level, and the sensing outcomes in spatial and temporal domains have a significant influence on the utilization performance of spectrum.

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Network Throughput Optimization for Random Access Narrowband Cognitive Radio Internet of Things (NB-CR-IoT)

Cited by 89 publications

References 28 publications

Availability-Reliability-Stability Trade-Offs in Ultra-Reliable Energy-Harvesting Cognitive Radio IoT Networks

Availability-Reliability-Stability Trade-Offs in Ultra-Reliable Energy-Harvesting Cognitive Radio IoT Networks

Analysis of radio frequency spectrum usage using cognitive radio

Spatial–Temporal Sensing and Utilization in Full Duplex Spectrum-Heterogeneous Cognitive Radio Networks for the Internet of Things

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