Spectrum sensing and detection of incumbent-UEs in secondary-LTE based aerial-terrestrial networks for disaster recovery

Arunthavanathan, Senthuran; Kandeepan, Sithamparanathan; Evans, R.J.

doi:10.1109/camad.2013.6708117

Cited by 17 publications

(3 citation statements)

References 35 publications

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“…This is done by the methodology of spectrum sensing. There are various spectrum sensing methods that have been proposed in literature such as energy detection, cyclostationary detection, waveform detection, covariance detection, and cooperative distributed sensing, moreover the performance of these techniques are well presented under various assumptions, models and scenarios [6]- [24]. However, current literature does not provide much knowledge on the use of intelligent learning by the application of information obtained through spectral sensing.…”

Section: Introductionmentioning

confidence: 97%

Reinforcement learning based secondary user transmissions in cognitive radio networks

Arunthavanathan

Kandeepan

Evans

2013

2013 IEEE Globecom Workshops (GC Wkshps)

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In this paper, we address the decision making criteria of a secondary user (SU) for deciding whether to transmit or not upon performing spectrum sensing and detecting the presence of any primary user (PU) in the environment in a cognitive radio network (CRN). We propose a reinforcement learning (RL) based approach by a Markov process at the SU node and present novel analytical methods to analyze the performance of such approaches. In particular, we define the probability of interference Pi and the probability of wastage Pw, and compare these metrics with a RL based and a non-RL based approach for SU transmission. The simulations show the presence of a tradeoff in the two probability metrics Pw and Pi, based on the Markov process. The simulation results are compared in the form of the transmitter operating characteristics (ToC) curves. Using our approach, one could control the interference to the PU by trading off with the spectral wastage.

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Section: Introductionmentioning

confidence: 97%

Reinforcement learning based secondary user transmissions in cognitive radio networks

Arunthavanathan

Kandeepan

Evans

2013

2013 IEEE Globecom Workshops (GC Wkshps)

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“…Spectrum sensing (SS) is one of the CR technique that used to detect the spectrum hole of the primary user. There are several other SS approaches proposed in LTE-A system such as Auto-Correlation based Advanced Energy spectrum sensing algorithm [12], Spectrum sensing based on time domain energy detection method [13], and Pilot Induced Cyclostationarity and Gaussian Maximum-Likelihood spectrum sensing algorithms [14]. On the other hand, there are researches have been done to enable LTE and Wi-Fi systems to exist in an unlicensed band.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of video streaming on LTE with coexistence of Wi-Fi signal

Yaqoob¹,

Pang²,

Wong³

et al. 2019

Bulletin EEI

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The continuous growth in mobile data traffic and limited license wireless spectrum have led to dramatically increase the demand of the radio spectrum. It is widespread the concern about the coexistence of long term evolution (LTE) and Wi-Fi in the unlicensed band. There are several techniques have been proposed to enable the coexistence of LTE and Wi-Fi in the unlicensed band, but these works are targeted on the impact of the LTE to the Wi-Fi network performance. An experiment is carried out in this work to evaluate the impact of Wi-Fi signal on the video streaming in the LTE network. The experimental test comprised of the national instrument (NI) universal software radio peripheral (USRP) 2953R that is controlled by the LabVIEW Communication LTE application framework. Extensiveexperiments are carried out under two scenarios, i.e. (1) Coexistence of LTE and Wi-Fi signal, (2) LTE signal only. Performance evaluations are carried out with different Modulation and coding schemes (MCS) values and different mode of operations, i.e. frequency division duplex (FDD) and time division duplex (TDD) mode. The results illustrated that the interference from Wi-Fi signal caused the performance degradation of the LTE network in throughput and the power received by user equipment (UE).

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“…Cognitive radio networks has been widely discussed for intelligently adapting the network, communications parameters and the available resources based on the radio environmental conditions [14,15]. In our work we consider such intelligence is available in the network, enabled by learning and sensing techniques [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], for intelligently making decisions based on the network and the radio environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Relay Nodes for airborne LTE emergency networks

Al‐Hourani

Kandeepan

2013

2013, 7th International Conference on Signal Processing and Communication Systems (ICSPCS)

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This paper is proposing a novel concept of Cognitive Relay Node for intelligently improving the radio coverage of an airborne LTE emergency network, considering the scenarios outlined in the ABSOLUTE research project. The proposed network model was simulated comparing the different cases of deploying relay nodes to complement the coverage of an aerial LTE network. Simulation results of the proposed Cognitive Relay Nodes show significant performance improvement in terms of radio coverage quantified by the regional outage probability enhancement. Also, this paper is presenting the methodology and results of choosing the optimum aerial eNodeB altitude.

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Spectrum sensing and detection of incumbent-UEs in secondary-LTE based aerial-terrestrial networks for disaster recovery

Cited by 17 publications

References 35 publications

Reinforcement learning based secondary user transmissions in cognitive radio networks

Reinforcement learning based secondary user transmissions in cognitive radio networks

Performance evaluation of video streaming on LTE with coexistence of Wi-Fi signal

Cognitive Relay Nodes for airborne LTE emergency networks

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