Potential Design for Modulation and Coding Scheme in mmWave Multicarrier HetNets

Kemmochi, Fumiya; Fujisawa, Kento; Otsuka, Hiroyuki

doi:10.1109/vtcfall.2019.8891460

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…The path-loss model and other assumptions refer to [8]. We use 22 types of modulation and coding scheme (MCS) for a downlink adaptation, which is different from that used in [5]. Figure 2(a) shows the average and 5-percentile user throughput in the downlink as a function of CSO.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Throughput performance of mmWave HetNets using three-sector picocell

Kemmochi

Otsuka

2021

IEICE ComEX

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Section: Simulation Resultsmentioning

confidence: 99%

“…In [5], we have proposed mmWave HetNets using three-sector picocell with a wider bandwidth to overcome high path loss. However, the comparison with non-sector picocell, i.e., omni picocell, has not been evaluated.…”

Section: Introductionmentioning

confidence: 99%

Throughput performance of mmWave HetNets using three-sector picocell

Kemmochi

Otsuka

2021

IEICE ComEX

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“…In ref. [29], it is shown that average user throughput increases as the CSO increases up to a certain value. In this paper, it is assumed that the signal bandwidth of picocell is the same as that of microcell, and the number of UE is relatively high, For the conventional method, five kinds of fixed CSO are set: CSO = 0, 3, 6, 9, and 12 dB.…”

Section: Comparison With Conventional Methodsmentioning

confidence: 99%

Personal Picocell Scheme Using Adaptive Control CRE in Heterogeneous Mobile Networks

Fujisawa

Kemmochi

Otsuka

2020

JSAN

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Heterogeneous networks (HetNets), which are combined with a macrocell and picocell in the same coverage, are expected to further increase the system capacity in fifth-generation mobile systems and beyond. In HetNets, the cell range expansion (CRE) technique plays an important role and can allow more user equipment (UE) to access the picocell, i.e., virtually expand the picocell coverage. However, conventional CRE techniques that provide a fixed cell selection offset (CSO) for all UE may worsen user throughput if UE is forced to connect to the picocell because the received signal-to-interference plus noise ratio of the UE becomes lower. Therefore, we propose a personal picocell scheme using an adaptive control CRE technique to improve user throughput in which different CSOs are assigned to UE to form each optimal picocell for each UE. In this paper, we first describe the aspects and algorithm of the proposed scheme. Then, we show the user throughput for adaptive control CRE in comparison with conventional CRE by using system-level computer simulations for the two types of HetNets, i.e., single-band and multi-band HetNets. In the simulations, we first clarify the optimal parameters of the adaptive control CRE. We then show the average and 5-percentile user throughput of the optimized adaptive control CRE in comparison with that of conventional CRE. From these results, we confirmed that the personal picocell scheme using the adaptive control CRE can improve the 5-percentile user throughput while maintaining the average user throughput compared with that of conventional CRE.

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“…High order modulation, supports 1024-QAM for downlink and 256-QAM for sub-7 GHz uplink [75][76][77] low order modulation [57], supports Time Spread On-Off Keying (TS-OOK), Rate Division (RD-TS-OOK), Direct Sequence (DS-OOK) [78] Antenna Omni, directional, MIMO supported with high gain [79] Omni and directional with phased array [75,80] Bandwidth 0.03-0. [106], 5G NR networks [107] TeraMIMO [108], NYUSIM [109], CloudRT [110], Nano-Sim [111], THz propagation [112], TeraSim [113], TeraSim-6G [114], and TeraSim-MAC [115] The THz band has transmission windows that vary with distance, reaching up to THz bandwidth, while the mmWave band has a 10 GHz bandwidth and cannot achieve Tbps link speeds.…”

Section: Modulationmentioning

confidence: 99%

Terahertz Meets AI: The State of the Art

Farhad

Pyun

2023

Sensors

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Terahertz (THz) is a promising technology for future wireless communication networks, particularly for 6G and beyond. The ultra-wide THz band, ranging from 0.1 to 10 THz, can potentially address the limited capacity and scarcity of spectrum in current wireless systems such as 4G-LTE and 5G. Furthermore, it is expected to support advanced wireless applications requiring high data transmission and quality services, i.e., terabit-per-second backhaul systems, ultra-high-definition streaming, virtual/augmented reality, and high-bandwidth wireless communications. In recent years, artificial intelligence (AI) has been used mainly for resource management, spectrum allocation, modulation and bandwidth classification, interference mitigation, beamforming, and medium access control layer protocols to improve THz performance. This survey paper examines the use of AI in state-of-the-art THz communications, discussing the challenges, potentials, and shortcomings. Additionally, this survey discusses the available platforms, including commercial, testbeds, and publicly available simulators for THz communications. Finally, this survey provides future strategies for improving the existing THz simulators and using AI methods, including deep learning, federated learning, and reinforcement learning, to improve THz communications.

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Potential Design for Modulation and Coding Scheme in mmWave Multicarrier HetNets

Cited by 9 publications

References 16 publications

Throughput performance of mmWave HetNets using three-sector picocell

Throughput performance of mmWave HetNets using three-sector picocell

Personal Picocell Scheme Using Adaptive Control CRE in Heterogeneous Mobile Networks

Terahertz Meets AI: The State of the Art

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