Global Minimax Approximations and Bounds for the Gaussian Q-Function by Sums of Exponentials

Tanash, Islam M.; Riihonen, Taneli

doi:10.1109/tcomm.2020.3006902

“…Therefore, it is suggested to use the weighted sums of exponential approximations for the Q function to have a closed-form solution. The weights are optimized to minimize the error and can be found in [35]. The approximation of the Gaussian function is given as…”

Section: Unconditional Performance Analysismentioning

confidence: 99%

Hybrid Cognitive-Radio NOMA with Blind Transmission Mode Identification and BER Constraints

Yahya¹,

Alsusa²,

Al-Dweik³

2021

Preprint

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<div>The synergy of nonorthogonal multiple access (NOMA) and cognitive radio (CR) can provide efficient spectrum utilization for future wireless networks and enable supporting heterogeneous quality of service (QoS) requirements. In this context, this article aims at evaluating the throughput of a downlink CR-NOMA network where the secondary user (SU) data is opportunistically multiplexed with the primary user (PU) data using power-domain NOMA. The data multiplexing process is constrained by the PU QoS requirements. The multiplexing process can be considered seamless with respect to the PU because its receiver design will generally remain unchanged. Moreover, we consider the case where the SU detects its own data by blindly identifying the adopted transmission mode (TM) at the base station, which can be PU orthogonal multiple access PU-OMA, SU-OMA, PU/SU-NOMA, and no transmission. Consequently, the network can be classified as a hybrid underlay-interweave. The detection process is considered blind because the SU does not receive side information about the adopted TM. The obtained analytical results corroborated by Monte Carlo simulation results show that the proposed CR-NOMA network can provide substantial throughput improvement over conventional NOMA networks, particularly at low signal-to-noise ratios (SNRs) because the unutilized PU spectrum can be used by the SU. Moreover, in good channel conditions the PU can tolerate some interference from the SU, which may improve the channel utilization significantly. </div><div><br></div>

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“…is the hypergeometric function and B (•, •) is the beta function, B (α, β) = Γ(α)Γ(β) Γ(α+β) . For the general case of A 1 = A 2 , the integral can be solved using the Q-function approximation [66]…”

Section: B Two Reflectors L =mentioning

confidence: 99%

“…where δ l and ε l are constants evaluated to minimize the approximation error, their values are given in [66]. Using the Q-function approximation (36) can be simplified tō…”

Section: B Two Reflectors L =mentioning

confidence: 99%

IRS-Assisted UAV Communications with Imperfect Phase Compensation

Al-Dweik¹,

Al‐Jarrah²,

Alsusa³

et al. 2020

Preprint

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<div>This work presents a performance analysis on unmanned aerial vehicles (UAVs) assisted wireless</div><div>communications systems, where one of the UAVs supports intelligent reflecting surfaces (IRS). As the</div><div>estimation and compensation of the end-to-end phase for each propagation path is prone to errors, imperfect</div><div>phase compensation at the IRS is taken into consideration. The performance is derived in terms of symbol</div><div>error rate (SER) and outage probability, where the phase error is modeled using the von Mises distribution.</div><div>The air-to-air (A2A) channel for</div><div>each propagation path is modeled as a single dominant line-of-sight (LoS) component, and the results are</div><div>compared to the Rician channel model. The obtained results reveal that the considered A2A model can be</div><div>used to accurately represent the A2A channel with Rician fading.</div>

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“…where 2 F 2 (•) is the hypergeometric function and B (•, •) is the beta function, B (α, β) = Γ(α)Γ(β) Γ(α+β) . For the general case of A 1 = A 2 , the integral can be solved using the Q-function approximation [66]…”

Section: B Two Reflectors L =mentioning

confidence: 99%

“…where δ l and ε l are constants evaluated to minimize the approximation error, their values are given in [66]. Using the Q-function approximation (36) can be simplified to…”

Section: B Two Reflectors L =mentioning

confidence: 99%

IRS-Assisted UAV Communications with Imperfect Phase Compensation

Al-Dweik¹,

Al‐Jarrah²,

Alsusa³

et al. 2020

Preprint

View full text Add to dashboard Cite

<div>This work presents a performance analysis on unmanned aerial vehicles (UAVs) assisted wireless</div><div>communications systems, where one of the UAVs supports intelligent reflecting surfaces (IRS). As the</div><div>estimation and compensation of the end-to-end phase for each propagation path is prone to errors, imperfect</div><div>phase compensation at the IRS is taken into consideration. The performance is derived in terms of symbol</div><div>error rate (SER) and outage probability, where the phase error is modeled using the von Mises distribution.</div><div>The air-to-air (A2A) channel for</div><div>each propagation path is modeled as a single dominant line-of-sight (LoS) component, and the results are</div><div>compared to the Rician channel model. The obtained results reveal that the considered A2A model can be</div><div>used to accurately represent the A2A channel with Rician fading.</div>

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Global Minimax Approximations and Bounds for the Gaussian Q-Function by Sums of Exponentials

Cited by 27 publications

References 39 publications

Hybrid Cognitive-Radio NOMA with Blind Transmission Mode Identification and BER Constraints

Hybrid Cognitive-Radio NOMA with Blind Transmission Mode Identification and BER Constraints

IRS-Assisted UAV Communications with Imperfect Phase Compensation

IRS-Assisted UAV Communications with Imperfect Phase Compensation

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