Machine Learning Assisted Phase-less Millimeter-Wave Beam Alignment in Multipath Channels

Domae, Benjamin W.; Li, Ruifu; Čabrić, Danijela

doi:10.1109/globecom46510.2021.9685678

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…Several open questions remain with Seq2Seq phase-less BT and BP algorithms. Multipath channels would likely impact prediction performance, as with mmRAPID [17], and would correspond to the algorithm's ability to generalize to new environments. BP with more complex UE trajectories, alternative sounding codebooks, and joint BS-UE BA have also not yet been explored.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Prediction for Phase-less Millimeter-Wave Beam Tracking

Domae

Boljanovic

et al. 2022

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)

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Future wireless networks may operate at millimeterwave (mmW) and sub-terahertz (sub-THz) frequencies to enable high data rate requirements. While large antenna arrays are critical for reliable communications at mmW and sub-THz bands, these antenna arrays would also mandate efficient and scalable initial beam alignment and link maintenance algorithms for mobile devices. Low-power phased-array architectures and phaseless power measurements due to high frequency oscillator phase noise pose additional challenges for practical beam tracking algorithms. Traditional beam tracking protocols require exhaustive sweeps of all possible beam directions and scale poorly with high mobility and large arrays. Compressive sensing and machine learning designs have been proposed to improve measurement scaling with array size but commonly degrade under hardware impairments or require raw samples respectively. In this work, we introduce a novel long short-term memory (LSTM) network assisted beam tracking and prediction algorithm utilizing only phase-less measurements from fixed compressive codebooks. We demonstrate comparable beam alignment accuracy to state-ofthe-art phase-less beam alignment algorithms, while reducing the average number of required measurements over time.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Prediction for Phase-less Millimeter-Wave Beam Tracking

Domae

Boljanovic

et al. 2022

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)

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Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges

et al. 2022

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The Internet of Things (IoT) has rapidly expanded for a wide range of applications towards a smart future world by connecting everything. As a result, new challenges emerge in meeting the requirements of IoT applications while retaining optimal performance. These challenges may include power consumption, quality of service, localization, security, and accurate modeling and characterization of wireless channel propagation. Among these challenges, the latter is critical to establishing point-to-point wireless communication between the sensors. Channel modeling also varies depending on the features of the surrounding area, which have a direct impact on the propagation of wireless signals. This presents a difficult task for network planners to efficiently design and deploy IoT applications without understanding the appropriate channel model to analyze coverage and predict optimal deployment configurations. As a result, this challenge has attracted considerable interest in academic and industrial communities in recent years. Therefore, this review presents an overview of current breakthroughs in wireless IoT technologies. The challenges in such applications are then briefly reviewed, focusing on wireless channel propagation modeling and characterization. Finally, the study gives a generalized form of commonly used channel models and a summary of recent channel modeling developments for wireless IoT technology. The outcome of this review is expected to provide a new understanding of the propagation behavior of present and future wireless IoT technologies, allowing network engineers to undertake correct planning and deployment in any environment. Additionally, the study may serve as a guideline for future channel modeling and characterization studies.

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Graph Attention Network-Based Single-Pixel Compressive Direction of Arrival Estimation

Tekbıyık¹,

Yurduseven

Kurt

2022

IEEE Commun. Lett.

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In this paper, we present a single-pixel compressive direction of arrival (DoA) estimation technique leveraging a graph attention network (GAT)-based deep-learning framework. The physical layer compression is achieved using a codedaperture technique, probing the spectrum of far-field sources that are incident on the aperture using a set of spatio-temporally incoherent modes. This information is then encoded and compressed into the channel of the coded-aperture. The coded-aperture is based on a metasurface antenna design and it works as a receiver, exhibiting a single-channel and replacing the conventional multichannel raster scan-based solutions for DoA estimation. The GAT network enables the compressive DoA estimation framework to learn the DoA information directly from the measurements acquired using the coded-aperture. This step eliminates the need for an additional reconstruction step and significantly simplifies the processing layer to achieve DoA estimation. We show that the presented GAT integrated single-pixel radar framework can retrieve high fidelity DoA information even under relatively low signal-to-noise ratio (SNR) levels.

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Machine Learning Assisted Phase-less Millimeter-Wave Beam Alignment in Multipath Channels

Cited by 6 publications

References 13 publications

Machine Learning Prediction for Phase-less Millimeter-Wave Beam Tracking

Machine Learning Prediction for Phase-less Millimeter-Wave Beam Tracking

Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges

Graph Attention Network-Based Single-Pixel Compressive Direction of Arrival Estimation

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