Application of NOMA for cellular-connected UAVs: opportunities and challenges

Leow, Chee Yen; Navaie, Keivan; Sun, Yanshi; Ding, Zhiguo

doi:10.1007/s11432-020-2986-8

Cited by 15 publications

(8 citation statements)

References 84 publications

(89 reference statements)

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“…The capability of NOMA in UAV-interference cancellation on its co-channel GUEs is evaluated by Pang et aland Mei and Zhang in [12], [13], considering a static location for the UAV. Most prior works control the UAV-interference by considering the UAV staying in a fix location, while interference management dynamically through power or resource allocation optimization is still an open research area [14]. The main aim of this paper is to address this problem using NOMA and MAB methods.…”

Section: Related Workmentioning

confidence: 99%

Uplink Interference Management in Cellular-Connected UAV Networks Using Multi-Armed Bandit and NOMA

Banaeizadeh

Barbeau

García-Alfaro

et al. 2022

2022 IEEE Latin-American Conference on Communications (LATINCOM)

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Ground users suffer from severe uplink interference originating from high altitude Unmanned Aerial Vehicle (UAV) line-of-sight channels. Using multi-armed bandit, we propose a method aiming to find the best resource block and transmit power level for a UAV dynamically paired with a ground user using Non-Orthogonal Multiple Access (NOMA). It is done according to the UAV's location. It results in mitigating the UAV-uplink interference on its co-channel ground user and maximizing the sum of their data rate in the shared resource block. Performance is evaluated via simulating three explorationexploitation strategies, namely, epsilon-greedy, upper confidence bound and Thompson sampling.

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Section: Related Workmentioning

confidence: 99%

Uplink Interference Management in Cellular-Connected UAV Networks Using Multi-Armed Bandit and NOMA

Banaeizadeh

Barbeau

García-Alfaro

et al. 2022

2022 IEEE Latin-American Conference on Communications (LATINCOM)

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“…The impact of ICI and various network parameters have not been studied and the performance of NOMA for AUs and TUs remains unclear. Nevertheless, as it was shown in [24], NOMA might be a perfect fit to serve AUs and TUs for C&C and data links, respectively, due to their downlink asymmetricity.…”

Section: Introductionmentioning

confidence: 97%

Interference-Aware NOMA for Cellular-Connected UAVs: Stochastic Geometry Analysis

Leow

Navaie

Sun

et al. 2021

IEEE J. Select. Areas Commun.

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Efficiency of cellular-connected UAVs is challenged by spectrum inefficiency, limited number of concurrent connectivity, and strong interference. To overcome these issues, in this paper, we study the performance of downlink non-orthogonal multiple access for cellular-connected UAVs. We develop a novel framework based on stochastic geometry for the co-existence of aerial users (AUs) and terrestrial users (TUs), where the spatial distribution of the base stations (BSs) follows a Poisson Point Process. In our analysis, two user association policies and two types of receive antennas are considered while an intercell interference coordination (ICIC) technique is also in place. As the main performance measures, we then analytically derive the coverage probability and average rate of AUs and TUs. These derivations are then used to provide quantitative insights on the impact of different system parameters and settings including AU's altitude, TU's distance from the BS, power allocation, successive interference cancellation (SIC) constraints, user association policy, antenna beamwidth, and the number of coordinated BSs. Based on our analysis we then propose an interference-aware scheme based on maximum-SINR user association, directional antenna, and ICIC. A benchmark scheme based on minimum-distance user association, omni-directional antenna, and without ICIC is considered. Compared to the benchmark scheme, our proposed scheme improves the AU's coverage probability by threefold and TU's average rate by sixfold. Compared to the orthogonal multiple access, our proposed scheme trades off a slight reduction in the AU's coverage probability (~1%) with a significant increase in the achieved rate of the TUs (603Kbps/resource block).

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“…A ket to enhance connection density is to provide device access over a large range. The cellular technique is one of the main access techniques for IoT [5,6]. In narrow-band IoT, single-carrier frequency division multiple access (SC-FDMA) and orthogonal frequency division multiple access (OFDMA) have been adopted in uplink and downlink transmissions, respectively, which are based on the conventional granted orthogonal multiple access (O-MA) scheme [7], and orthogonal time/frequency resources are allocated to different devices.…”

Section: Introductionmentioning

confidence: 99%

Active device detection and performance analysis of massive non-orthogonal transmissions in cellular Internet of Things

Cai

Fan

Zou

et al. 2022

Sci. China Inf. Sci.

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This paper investigates multiple access schemes for uplink and downlink transmissions in cellular networks with massive Internet of Things (IoT) devices. Recall that single-carrier frequency division multiple access and orthogonal frequency division multiple access, which are orthogonal multiple access (OMA) schemes, have been conventionally adopted for uplink and downlink transmissions in narrow-band IoT, respectively. Unlike these OMA schemes, we propose two non-orthogonal multiple access (NOMA) schemes for cellular IoT with short-packet transmissions. Especially, a generalized expectation consistent signal recovery based algorithm is proposed to estimate active devices, channel state information and data in uplink transmission, where all of active devices are allowed to transmit their pilots and data through the same resource block without authorization. On the other hand, the active devices estimated during uplink transmission are grouped for downlink transmission with a trade-off between performance and detection complexity. Additionally, the data error rates are analysed for both uplink and downlink transmissions with low-resolution analog-to-digital converters (ADCs), where the effects of critical parameters such as the estimation error, ADC bits, packet length and message bits are revealed. Both simulation and analytical results are provided to demonstrate the excellent performance of the proposed NOMA schemes and algorithms, especially for active device, channel and data estimations. More importantly, the obtained results show that the data error rate performance of downlink NOMA is superior to that of OMA when the message bits of devices in one group are selected following the proposed strategy.

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Application of NOMA for cellular-connected UAVs: opportunities and challenges

Cited by 15 publications

References 84 publications

Uplink Interference Management in Cellular-Connected UAV Networks Using Multi-Armed Bandit and NOMA

Uplink Interference Management in Cellular-Connected UAV Networks Using Multi-Armed Bandit and NOMA

Interference-Aware NOMA for Cellular-Connected UAVs: Stochastic Geometry Analysis

Active device detection and performance analysis of massive non-orthogonal transmissions in cellular Internet of Things

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