High-speed radio-resource scheduling for 5G ultra-high-density distributed antenna systems

Arikawa, Yuki; Uzawa, Hiroyuki; Sakamoto, Takeshi; Shigematsu, Satoshi

doi:10.1109/wcsp.2016.7752480

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…where z represents the additive white Gaussian noise, and (u u u(t)) † represents the Hermitian of u u u(t). Based on the received signal in (1), the signal-to-noise ratio (SNR) for the terminal served by BS at time t for sub-carrier n, with noise power σ 2 z , is given by:…”

Section: A Basic System Modelmentioning

confidence: 99%

“…The task of determining the appropriate resource allocation relies on the information about the propagation environment acquired by the transmitter(s), as well as on the available computational power in the system. Traditionally, the users' channel state information (CSI) available at the transmitters is utilized for resource allocation, where heuristic approaches can be used to optimize the practical implementation [1]. The CSI acquisition contributes to a performance overhead, which varies depending on the system's user density.…”

Section: Introductionmentioning

confidence: 99%

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Coordinates-based Resource Allocation Through Supervised Machine Learning

Imtiaz¹,

Schiessl²,

Koudouridis³

et al. 2020

Preprint

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Appropriate allocation of system resources is essential for meeting the increased user-traffic demands in the next generation wireless technologies. Traditionally, the system relies on channel state information (CSI) of the users for optimizing the resource allocation, which becomes costly for fast-varying channel conditions. Considering that future wireless technologies will be based on dense network deployment, where the mobile terminals are in line-of-sight of the transmitters, the terminals' position information provides an alternative to estimate the channel condition. In this work, we propose a coordinates-based resource allocation scheme using supervised machine learning techniques, and investigate how efficiently this scheme performs in comparison to the traditional approach under various propagation conditions. We consider a simplistic system set up as a first step, where a single transmitter serves a single mobile user. The performance results show that the coordinates-based resource allocation scheme achieves a performance very close to the CSI-based scheme, even when the available user's coordinates are erroneous. The proposed scheme performs consistently well with realisticsystem simulation, requiring only 4 s of training time, and the appropriate resource allocation is predicted in less than 90 µs with a learnt model of size <1 kB.

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Section: A Basic System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coordinates-based Resource Allocation Through Supervised Machine Learning

Imtiaz¹,

Schiessl²,

Koudouridis³

et al. 2020

Preprint

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“…Therefore, spectrum efficiency also depends on the allocation of UEs and the selection of serving antennas [5]. In [6] and [7] scheduling schemes for a DAS based cellular system have been investigated. The research in [6] has demonstrated a UE allocation scheme with a dedicated hardware.…”

Section: Introductionmentioning

confidence: 99%

“…In [6] and [7] scheduling schemes for a DAS based cellular system have been investigated. The research in [6] has demonstrated a UE allocation scheme with a dedicated hardware. In this research, each antenna selects a UE that achieves the highest throughput.…”

Section: Introductionmentioning

confidence: 99%

Phase Selection in Round-Robin Scheduling Sequence for Distributed Antenna System

Otsuru

Sanada

2020

IEICE Trans. Commun.

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Go OTSURU †a) , Student Member and Yukitoshi SANADA †b) , Fellow SUMMARY One of key technologies in the fifth generation mobile communications is a distributed antenna system (DAS). As DAS creates tightly packed antenna arrangements, inter-user interference degrades its spectrum efficiency. Round-robin (RR) scheduling is known as a scheme that achieves a good trade-off between computational complexity and spectrum efficiency. This paper proposes a user equipment (UE) allocation scheme for RR scheduling. The proposed scheme offers low complexity as the phase of UE allocation sequences are predetermined. Four different phase selection criteria are compared in this paper. Numerical results obtained through computer simulation show that maximum selection, which sequentially searches for the phase with the maximum tentative throughput realizes the best spectrum efficiency next to full search. There is an optimum number of UEs which obtains the largest throughput in single-user allocation while the system throughput improves as the number of UEs increases in 2-user RR scheduling.

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