Performance Modeling of LTP-HARQ Schemes Over OSTBC-MIMO Channels for Hybrid Satellite Terrestrial Networks

Jiao, Jian; Hu, Youjun; Zhang, Qinyu; Wu, Shaohua

doi:10.1109/access.2018.2792400

Cited by 19 publications

(4 citation statements)

References 24 publications

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“…, and p i (t) denotes the transmit power allocated to UE i at time slot t. Consistent with most existing works [26], [27], we assume that the links between S and UEs experience independent and identically distributed (i.i.d.) block shadowed-Rician fading distribution and additive white Gaussian noise (AWGN).…”

Section: System Modelmentioning

confidence: 99%

Deep Learning-Based Long-Term Power Allocation Scheme for NOMA Downlink System in S-IoT

et al. 2019

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In this paper, we formulate a long-term resource allocation problem of non-orthogonal multiple access (NOMA) downlink system for the satellite-based Internet of Things (S-IoT) to achieve the optimal decoding order and power allocation. This long-term resource allocation problem of the satellite NOMA downlink system can be decomposed into two subproblems, i.e., a rate control subproblem and a power allocation subproblem. The latter is a non-convex problem and the solution of which relies on both queue state and channel state. However, the queue state and the channel state continually change from one time slot to another, which makes it extremely strenuous to characterize the optimal decoding order of successive interference cancellation (SIC). Therefore, we explore the weight relationship between the queue state and the channel state to derive an optimal decoding order by leveraging deep learning. The proposed deep learning-based long-term power allocation (DL-PA) scheme can efficiently derive a more accurate decoding order than the conventional solution. The simulation results show that the DL-PA scheme can improve the performance of the S-IoT NOMA downlink system, in terms of long-term network utility, average arriving rate, and queuing delay. INDEX TERMS Satellite-based Internet of Things, deep learning, non-orthogonal multiple access, successive interference cancellation.

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Section: System Modelmentioning

confidence: 99%

Deep Learning-Based Long-Term Power Allocation Scheme for NOMA Downlink System in S-IoT

et al. 2019

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“…The authors of [27] studied the performance of transmission control protocol enhancements for a hybrid terrestrial-satellite network, and the expressions for the end-to-end throughput with link-layer suitable for reliable-ARQ and transmission control protocol splitting were obtained. The closed-form expressions for the mean number of transmission rounds were derived by using Laplace transforms for reliable data delivery in ARQ and HARQ schemes in multipleinput multiple-output STT networks [28].…”

Section: Introductionmentioning

confidence: 99%

“…Differing from the existing works on the HARQ schemes for STT [24]- [28], analysis models are set up to study and understand the performance of three classic HARQ schemes, namely, GSA, RTD, and general IR schemes in satellite-to-terrestrial fading channels while considering the randomness of the positions of terrestrial receivers. The best of our knowledge, this work is the first to build up such performance analysis models for the HARQ schemes for STT, which can also serve as a useful reference for investigating the performance of other HARQ scheme for STT.…”

Section: Introductionmentioning

confidence: 99%

On HARQ Schemes in Satellite-Terrestrial Transmissions

Pan

Tian

et al. 2020

IEEE Trans. Wireless Commun.

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As an efficient supplement for terrestrial communications, e.g., fifth-generation communication networks, satellite communication has been proved as a promising choice to realize seamless coverage, due to its inherent superiority over common wireless communication systems, like large-scale coverage area and flexibility of deployment. In this paper, a satellite-terrestrial transmission (STT) system including a satellite transmitter (S) and a group of terrestrial receivers (D) is focused on. Considering the randomness of D and employing stochastic geometry, the outage performance and diversity gain of three hybrid automatic repeat request (HARQ) schemes, generalized slotted ALOHA (GSA), repetition time diversity (RTD) and general incremental redundancy (IR), are respectively studied. In detail, the exact closedform analytical expression for the outage probability (OP) is derived for the GSA scheme, while the approximated expression for the OP of RTD scheme is presented and a useful expression for the OP of IR scheme is derived. Furthermore, the diversity gains of the three considered HARQ schemes are respectively investigated in STT scenarios. Finally, some numerical results are provided to validate the proposed analysis models.

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“…Fortunately, the size of antenna is decreasing as well as the frequency is increasing, which means that the short wavelength of mmWave band signal allows to integrate of large number of antennas in a limited space on HTS and UEs. It has proved that the efficient method to compensate for poor radio propagations of mmWave band signals is using massive multiple input and multiple output (MIMO) antennas with the aid of beamforming [5]. Moreover, the mmWave band signal can restrain interference by concentrating energy to desired direction only, thereby ensuring higher throughput and better energy efficiency [6], [7].…”

Section: Introductionmentioning

confidence: 99%

ARM: Adaptive Random-Selected Multi-Beamforming Estimation Scheme for Satellite-Based Internet of Things

Wang

Jiao

et al. 2019

IEEE Access

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In this paper, we focus on the millimeter-wave (mmWave) band multiple-input and multipleoutput (MIMO) channel estimation in the satellite-based Internet of Things (S-IoT). At first, we establish a sparse geometric-based mmWave MIMO channel model between a high throughput satellite (HTS) and multiple terrestrial user equipments (UEs) for the S-IoT downlink system. By exploiting the sparsity inherent to the mmWave band channel, we propose an efficient adaptive random-selected multi-beamforming (ARM) estimation scheme, which can simultaneously estimate the mmWave MIMO channel state information (CSI) for multiple UEs in angle domain. The ARM estimation scheme measures of the CSI between the HTS and multiple UEs by utilizing a series of random combinations of transmitting beamforming vectors and receiving UEs' antennas and the required number of measurements can adaptively reduce as well as the signal-to-noise ratios (SNR) increases. To further improve the performance of ARM estimation scheme, the number of each beamforming been selected for the channel measurement is buffered, and modify the probability of the random selection of beams and UEs. Two improved ARM estimation schemes are proposed, one is ARM forcing adaptation beam selection (ARM-FABS) scheme, and the other is ARM partially estimated beam selection (ARM-PEBS) scheme. The simulation results show that the proposed ARM estimation schemes can reduce the required number of measurements and achieve a better tracking performance over a wide range of SNRs.INDEX TERMS Satellite-based Internet of Things, millimeter-wave band, multiple-input and multipleoutput, channel estimation.

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Performance Modeling of LTP-HARQ Schemes Over OSTBC-MIMO Channels for Hybrid Satellite Terrestrial Networks

Cited by 19 publications

References 24 publications

Deep Learning-Based Long-Term Power Allocation Scheme for NOMA Downlink System in S-IoT

Deep Learning-Based Long-Term Power Allocation Scheme for NOMA Downlink System in S-IoT

On HARQ Schemes in Satellite-Terrestrial Transmissions

ARM: Adaptive Random-Selected Multi-Beamforming Estimation Scheme for Satellite-Based Internet of Things

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