NOMA-Enhanced Computation Over Multi-Access Channels

Wu, Fangzhou; Chen, Li; Zhao, Nan; Chen, Yunfei; Yu, F. Richard; Wei, Guo

doi:10.1109/twc.2019.2963654

Cited by 13 publications

(11 citation statements)

References 36 publications

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“…Among them, we believe non-orthogonal multiple access (NOMA), which is emerging as a multiple access technique thanks to its high spectral efficiency, is one of the techniques leveraging the AirComp framework [ 25 , 26 ]. Actually, as aforementioned in Section 1 , AirComp with NOMA has been studied in wireless networks [ 18 ]. We leave NOMA-based AirComp, which combines the proposed MIMO AirComp with NOMA, to be investigated in further studies of massive IoT networks.…”

Section: Discussionmentioning

confidence: 99%

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Beamforming Techniques for Over-the-Air Computation in MIMO IoT Networks

Lee

Jung

2020

Sensors

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In this paper, a novel beamforming technique is proposed as the over-the-air computation (AirComp) framework in a multiple-input multiple-output (MIMO) Internet-of-things (IoT) network consisting of multiple IoT sensors (STAs) and a single access point (AP). We assume that each IoT device has the channel state information (CSI) from itself to the AP and the AP has the global CSI of all IoT devices. We consider the mean squared error (MSE), which represents the reliability of function computation, as a performance metric. In short, each IoT device exploits maximum-ratio transmission (MRT) as a transmit beamforming technique to improve MSE performance by taking full advantage of multiple transmit antennae. Moreover, for the receive beamforming, we first consider a receive antenna selection (RAS) technique as the simplest beamforming method at the AP. Then, a semi-definite relaxation (SDR) method and a successive convex approximation (SCA) algorithm are considered and compared with each other in terms of MSE. Finally, we propose a novel two-step beamforming algorithm to further improve the MSE performance of the aforementioned techniques. We have numerically verified through computer simulations that the proposed framework has an improved MSE performance of about 6dB compared to the conventional single-input multiple-output (SIMO) AirComp, even with only two transmit antennae, and the modified MRT outperforms the other transmit beamforming techniques. Furthermore, the proposed receive beamforming technique, a two-step algorithm, shows the best performance in terms of MSE compared to prior studies.

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Section: Discussionmentioning

confidence: 99%

“…They designed the beamformer through optimization, focusing on multi-function computation, not single-function computation. In [ 18 ], the method of computing multiple functions was proposed by applying non-orthogonal multiple access (NOMA) to AirComp. Moreover, various application fields to which AirComp is applied are being researched.…”

Section: Introductionmentioning

confidence: 99%

Beamforming Techniques for Over-the-Air Computation in MIMO IoT Networks

Lee

Jung

2020

Sensors

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“…These studies rely on the NOMA and OFDM models to use wideband frequencies. A NOMA assisted function computation network is proposed in [132] and [133]. As given in the previous sections, NOMA is a multiple access method that is based on the superposition of signals.…”

Section: Computation Ratementioning

confidence: 99%

The Magic of Superposition: A Survey on Simultaneous Transmission Based Wireless Systems

Altun,

Kurt,

Ozdemir

2021

Preprint

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In conventional communication systems, any interference between two communicating points is regarded as unwanted noise since it distorts the received signals. On the other hand, allowing simultaneous transmission and intentionally accepting the interference of signals and even benefiting from it have been considered for a range of wireless applications. As prominent examples, non-orthogonal multiple access (NOMA), joint source-channel coding, and the computation codes are designed to exploit this scenario. They also inspired many other fundamental works from network coding to consensus algorithms. Especially, federated learning is an emerging technology that can be applied to distributed machine learning networks by allowing simultaneous transmission. Although various simultaneous transmission applications exist independently in the literature, their main contributions are all based on the same principle; the superposition property. In this survey, we aim to emphasize the connections between these studies and provide a guide for the readers on the wireless communication techniques that benefit from the superposition of signals. We classify the existing literature depending on their purpose and application area and present their contributions. The survey shows that simultaneous transmission can bring scalability, security, low-latency, lowcomplexity and energy efficiency for certain distributed wireless scenarios which are inevitable with the emerging Internet of things (IoT) applications.

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“…In the literature, there are two lines of research, which focus on digital coded AirComp (see, e.g., [10]- [18]) and analog uncoded AirComp (see, e.g., [4]- [8], [19]- [24]). Simple uncoded transmission was proven to achieve a sufficiently low computation distortion for independent and identically Gaussian distributed data sources [19].…”

Section: Introductionmentioning

confidence: 99%

“…Based on information theory, the achievable computation rate of AirComp was investigated in [16] and [17]. To improve the computation rate of multi-function AirComp, non-orthogonal multiple access (NOMA) was studied in [18], where various functions arriving from different WDs were superimposed in each resource block.…”

Section: Introductionmentioning

confidence: 99%