Power Allocation-Based QoS Guarantees in Millimeter-Wave-Enabled Vehicular Communications

Swain, Satyabrata; Sahoo, Jyoti Prakash; Tripathy, Asis Kumar

doi:10.1007/978-981-15-4218-3_4

Cited by 3 publications

(2 citation statements)

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“…The beam misalignment between users in movement may deteriorate the QoS in mmWave 5G communications. In this context, authors in [79] consider a switching/resetting beam serving method based on power allocation, taking into account the contextual information of the vehicles. Simulation results prove the performance of this power-based switching method in terms of user throughput, connection stability, and QoS.…”

Section: ) Beam Alignment and Qosmentioning

confidence: 99%

Initial Access & Beam Alignment for mmWave and Terahertz Communications

2022

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The initial access in Millimeter wave (mmWave) 5G communications is very challenging and time consuming. In general, mmWave and terahertz communications require the use of directional antennas to seek narrow beams. However, due to this directionality, many issues can impact the beam alignment between transmitter and receiver. In this paper, we present a comprehensive survey of beam alignment and initial access in mmWave and terahertz 5G/6G systems. First, we present a detailed overview of initial access methods, techniques, and beam management procedures. Then, we classify recent works related to beam alignment based on their objective functions (i.e., latency, power allocation, QoS, energy consumption, cost). We also highlight the beam alignment in terahertz 6G, where we find that deep learning and reconfigurable intelligent surface are the two protagonists that help to achieve fast beam alignment.INDEX TERMS 5G/6G, mmWave, terahertz, beamforming, initial access, beam alignment, beam steering, reconfigurable intelligent surface.

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Section: ) Beam Alignment and Qosmentioning

confidence: 99%

Initial Access & Beam Alignment for mmWave and Terahertz Communications

2022

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“…To increase the clustering effect, the K-mean++ algorithm is used along with the intersection-over-union (IoU). A convolutional block attention module (CBAM) is used in the network to improve the network capability [29] , [30] . The detection capability of a YOLOv3 network for small-object defects is greatly improved after the addition of fourth-scale prediction.…”

Section: Introductionmentioning

confidence: 99%