Interference Characterization in Random Waypoint Mobile Networks

Irio, Luis; Furtado, António; Oliveira, Rodolfo; Bernardo, Luís; Dinis, Rui

doi:10.1109/twc.2018.2866426

Cited by 20 publications

(23 citation statements)

References 34 publications

(50 reference statements)

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“…The characterization of the interference in wireless networks has been a topic of extensive research in the last years [5]- [8]. The aggregate interference is usually modeled through advanced stochastic geometry techniques [9] that have into account the spatial position of each interferer and its individual radio channel to determine the amount of interference caused to a specific node [5], [6], [10].…”

Section: A Related Workmentioning

confidence: 99%

Aggregate Interference Power Characterization for Directional Beamforming Wireless Networks

Oliveira

2021

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)

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In this paper, we characterize the aggregate interference power in directional millimeter-wave communications, capturing the effect of beamforming. The analysis considers a general distance-based path loss with Rayleigh and Rician fading channels and a sectored antenna model. Moreover, the nodes are uniformly distributed over a circular or annular area centered at the receiver. The main contribution of the paper is the derivation of the moment generating function (MGF) of the aggregate interference power. The MGF is adopted in a moment-based approximation to a Gamma distribution and used as a model of the aggregate interference power. Several simulations confirm the effectiveness of the proposed approximation for different scenarios, highlighting the effect of directional communications on the aggregate interference power.

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Section: A Related Workmentioning

confidence: 99%

Aggregate Interference Power Characterization for Directional Beamforming Wireless Networks

Oliveira

2021

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)

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“…where E[I η,l ] and Var[I η,l ] are the expectation and variance of the power received from the transmitters of the annulus l of the network η, which are respectively given by [9]…”

Section: Harvested Energymentioning

confidence: 99%

Impact of Wireless-Powered Communications in Coexisting Mobile Networks

Irio¹,

Oliveira²,

Costa³

et al. 2020

Preprint

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In this letter, we investigate the impact of wireless-powered communications when energy is harvested from multiple static and/or mobile wireless coexisting networks. In a first step, we characterize the aggregate power received by a harvester node when it harnesses the energy generated by the coexisting wireless networks. Considering that the harvester node acts as a transmitter after the harvesting duration, we derive the outage probability for such coexisting scenario. In addition, the throughput achieved by the harvester node is also characterized, and the optimal harvesting duration is identified taking into account the mobility of the coexisting networks, the features of the static networks, the energy harvesting process, as well as the communication performance between the harvester node and the receiver. Our work shows that the distribution of the power received by the harvester from the coexisting networks can be accurately approximated by an α − µ distribution. Moreover, the mobility also impacts on the optimal throughput of the wireless-powered communications, which is accurately confirmed by the proposed analysis and extensive simulations.

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“…In [21] the authors have derived the integral in (8). Using the MGF derived in the integral in ( [21], Equation (20)), (8) is written as…”

Section: Amplitude and Power Of The Aggregate Interferencementioning

confidence: 99%

“…In [21] the aggregate power of nodes that are distributed according to a 2D Poisson point process and located within an annulus is approximated through a Gamma distribution. Likewise, the interference caused by multiple PU nodes located within an area A l can be approximated by a Gamma distribution with PDF and CF given by,…”

Section: Power and Envelope Signals Of Primary Networkmentioning

confidence: 99%

“…where the parameters k PU and θ PU of the Gamma distribution are obtained by employing a moment matching method [20]. As in [21], the shape and the scale parameters of the Gamma distribution that characterizes the aggregate interference caused by the transmitters located within an area A l are respectively given by…”

Section: Power and Envelope Signals Of Primary Networkmentioning

confidence: 99%

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Performance Analysis of Interference-Aware Multi-Packet Reception Networks

et al. 2020

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Decentralized wireless networks are gaining increasing popularity as they do not need a fixed infrastructure. Simultaneously, multiple research initiatives have led to different findings at the PHY layer of the wireless communication systems, which include Multi-Packet Reception (MPR) techniques that enable a receiver to decode multiple packets that are transmitted simultaneously. However, the distributed nature of decentralized wireless networks demands different network control policies that should take into account the MPR capabilities to increase the network performance. This work studies the performance of a wireless network composed of multiple transmitters that are willing to transmit to a single receiver. This receiver has MPR capability and adopts an Energy-based Sensing (EBS) technique to enable uplink users’ transmissions without interfering with the ongoing transmissions from other transmitters. The first remark to be made is that the MPR technique performance depends on the channel propagation conditions and on the amount of time the receiver needs to detect the spectrum’s occupancy state. However, it is shown that by increasing the number of samples needed to increase the sensing accuracy, the receiver may degrade its throughput, namely if the receiver is equipped with a single radio, that is sequentially used for sensing and transmitting (split-phase operation). The results presented in the paper show the impact of the channel propagation condition and EBS parameterization on wireless network throughput and the cases where the receiver MPR capture performance is greatly improved by the use of a spectrum sensing technique.

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Interference Characterization in Random Waypoint Mobile Networks

Cited by 20 publications

References 34 publications

Aggregate Interference Power Characterization for Directional Beamforming Wireless Networks

Aggregate Interference Power Characterization for Directional Beamforming Wireless Networks

Impact of Wireless-Powered Communications in Coexisting Mobile Networks

Performance Analysis of Interference-Aware Multi-Packet Reception Networks

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