Multi-Slot Over-the-Air Computation in Fading Channels

Tang, Suhua; Popovski, Petar; Zhang, Chao; Obana, Sadao

doi:10.1109/twc.2023.3245304

Cited by 9 publications

(7 citation statements)

References 31 publications

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“…In [17, p. 70], Tse and Viswanath point out that, "• • • the main reason why detection in fading channel has poor performance is not because of the lack of knowledge of the channel at the receiver. It is due to the fact that the channel gain is random and there is a significant probability that the channel is in a deep fade", and this effect of deep fading is a major, well-documented challenge in wireless communication [17], [37], [38], [39]. In this section, we see that as far as the deep fading is concerned, operating in the mediumband regime may be extremely beneficial.…”

Section: Effect Of Deep Fading Avoidancementioning

confidence: 99%

Introduction to Mediumband Wireless Communication

Basnayaka

2023

IEEE Open J. Commun. Soc.

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The multipath is unavoidable in radio frequency (RF) wireless communication, and affects almost every element of the communication systems. The impact of multipath on the received signal depends on whether the delay spread (i.e., spread of time delays associated with different multipath components) is large or small relative to the symbol period of the wireless communication system. In narrowband channels, the symbol period is set such that the delay spread is about one tenth (or less) of it. In broadband channels, it is set such that the delay spread is many times greater than the symbol period. In between these two extremes, there appears to exist an important, yet overlooked, class of channels whose delay spread is neither small nor large enough for them to fall into these two basic channel classes. In this paper, we study the effect of multipath on channels that fall in the transitional region between narrowband and broadband referred henceforth as "mediumband". This paper shows that mediumband channels possess a distinct channel model, and pose both challenges and opportunities for reliable wireless communication. For instance, mediumband channels enable signalling at a significantly higher rate than that of narrowband channels, but on the flip side, as the degree of mediumband-ness increases, the quality of the channel deteriorates rapidly due to the excessive inter-symbol-interference (ISI). However, mediumband channels have an inherent ability to avoid deep fading, and if designed properly, mediumband wireless communication, which refers to wireless communication through mediumband channels, could be made to be significantly more reliable too.INDEX TERMS Communication theory, multipath, delay spread, wireless channel models.

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Section: Effect Of Deep Fading Avoidancementioning

confidence: 99%

Introduction to Mediumband Wireless Communication

Basnayaka

2023

IEEE Open J. Commun. Soc.

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“…An effective method to channel fading is diversity. Time diversity is exploited in [21], by extending AirComp to multiple slots. Each node can delay its transmission until its channel gain gets above a pre-defined threshold.…”

Section: B Time/frequency/path Diversitymentioning

confidence: 99%

“…In a fast fading environment, however, the CSI detection itself becomes a challenging problem. Using the statistics of CSI instead of their instantaneous values and letting each node decide its transmission based on its own CSI, the distributed scheduling in [21] avoids the feedback of instantaneous CSI from each node to the sink.…”

Section: Csi Detectionmentioning

confidence: 99%

Miso: Misalignment Allowed Optimization for Multiantenna Over-the-Air Computation

Tang

Zhang

et al. 2024

IEEE Internet Things J.

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Over-the-air computation (AirComp), as an effective method to wireless data aggregation, has attracted much attention recently. It helps to improve network efficiency and scalability by integrating communication and computation in the air. In AirComp, both signal magnitude misalignment and noise lead to computation error. In the single antenna case, by allowing misalignment in signal magnitude, a good tradeoff can be achieved between signal distortion and noise power, which leads to a minimal error. In the multi-antenna case, usually the zeroforcing policy is used to enforce signal magnitude alignment (no distortion), which, however, increases noise and affects the overall computation error. To better exploit multiple antennas at the sink, in this paper, we propose a misalignment allowed optimization (Miso) method for AirComp. Specifically, a group of nodes whose signals may be misaligned are dynamically selected, and other signals are aligned to a higher level with higher quality. On this basis, the optimization of multi-antenna AirComp is converted to a difference of convex problem and is solved iteratively. Simulations confirm that the proposed method greatly reduces computation error and scales better with the number of nodes, compared with previous methods.

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“…Last, to evaluate the performance of AirComp, we adopt two benchmarking schemes, namely analog orthogonal access in (10) and the adaptive (dual-mode) access in (33), to support local feature uploading.…”

Section: A Experimental Settingsmentioning

confidence: 99%

“…There exist few results on quantifying the scaling law. On the other hand, in the aspect of air interface, the AirComp error diminishes with the increase of the number of links due to aggregation and exploitation of (channel) spatial diversity [33]. The consideration of E2E performance for ISEA naturally couples the two aspects and gives rise to the following open research questions we attempt to answer sequentially in this work.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Method for River Water Level Recognition from Surveillance Images Using Convolution Neural Networks

Chen

et al. 2022

Remote Sensing

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Water conservancy personnel usually need to know the water level by water gauge images in real-time and with an expected accuracy. However, accurately recognizing the water level from water gauge images is still a complex problem. This article proposes a composite method applied in the Wuyuan City, Jiangxi Province, in China. This method can detect water gauge areas and number areas from complex and changeable scenes, accurately detect the water level line from various water gauges, and finally, obtain the accurate water level value. Firstly, FCOS is improved by fusing a contextual adjustment module to meet the requirements of edge computing and ensure considerable detection accuracy. Secondly, to deal with scenes with indistinct water level features, we also apply the contextual adjustment module for Deeplabv3+ to segment the water gauge area above the water surface. Then, the area can be used to obtain the position of the water level line. Finally, the results of the previous two steps are combined to calculate the water level value. Detailed experiments prove that this method solves the problem of water level recognition in complex hydrological scenes. Furthermore, the recognition error of the water level by this method is less than 1 cm, proving it is capable of being applied in real river scenes.

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Multi-Slot Over-the-Air Computation in Fading Channels

Cited by 9 publications

References 31 publications

Introduction to Mediumband Wireless Communication

Introduction to Mediumband Wireless Communication

Miso: Misalignment Allowed Optimization for Multiantenna Over-the-Air Computation

An Integrated Method for River Water Level Recognition from Surveillance Images Using Convolution Neural Networks

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