WINNER II Channel Models

Meinilä, Juha; Kyösti, Pekka; Jämsä, Tommi; Hentilä, Lassi

doi:10.1002/9780470748077.ch3

Cited by 568 publications

(151 citation statements)

References 24 publications

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“…Also, the birthdeath process of clusters appearing and disappearing is discussed in [39] for the sake of characterizing its nonstationary nature. The results in [39] show that the phases of the linear AA responses are no longer linear, and the AoAs impinging on the AA gradually shift, in agreement with the measurement results in [27]; and • 3D channel model: The model methodology and parameters of some existing models, such as the 3D channel model of the WINNER+ project [41], depend primarily on literatures, rather than on realistic measurements [42]. Thus, it needs further verification by realistic channel measurements, including the elevation characteristics of the AA as well as the cross-correlation matrix of the large-scale fading parameters.…”

Section: ) Gbsmsupporting

confidence: 72%

Survey of Large-Scale MIMO Systems

Zheng

Zhao

Mei

et al. 2015

IEEE Commun. Surv. Tutorials

286

172

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Abstract-The escalating teletraffic growth imposed by the proliferation of smartphones and tablet computers outstrips the capacity increase of wireless communications networks. Furthermore, it results in substantially increased carbon dioxide emissions. As a powerful countermeasure, in the case of full-rank channel matrices, MIMO techniques are potentially capable of linearly increasing the capacity or decreasing the transmit power upon commensurately increasing the number of antennas. Hence, the recent concept of large-scale MIMO (LS-MIMO) systems has attracted substantial research attention and been regarded as a promising technique for next-generation wireless communications networks. Therefore, this paper surveys the state of the art of LS-MIMO systems. First, we discuss the measurement and modeling of LS-MIMO channels. Then, some typical application scenarios are classified and analyzed. Key techniques of both the physical and network layers are also detailed. Finally, we conclude with a range of challenges and future research topics.Index Terms-Large-scale MIMO, 3-D MIMO, channel modeling, physical layer, networking.

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Section: ) Gbsmsupporting

confidence: 72%

Survey of Large-Scale MIMO Systems

Zheng

Zhao

Mei

et al. 2015

IEEE Commun. Surv. Tutorials

286

172

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“…A rectangular area of 2×2 km is considered. A WINNER+ B1 path loss model with α = 4.37, d 0 = 1 m, and L 0 = 0.068 is set with a height of 1.5 m and a frequency of 700 MHz, which is the most widely used bandwidth [9,39]. For intra-group links, SNR threshold γ 1 is set to 9 dB with 16 QAM, a 616/1024 code rate, and 1 % bit error rate based on the performance evaluation in an LTE system [48].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…We consider path loss channel model defined in 3GPP specification for direct communication between UEs [9,39]. The received signal power at UE j from UE i is given by…”

Section: Channel Model and Network Structurementioning

confidence: 99%

Reliable low-energy group formation for infrastructure-less public safety networks

Seong

Sohn

Choi

et al. 2016

J Wireless Com Network

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In this paper, we study an infrastructure-less public safety network (IPSN) where energy efficiency and reliability are critical requirements in the absence of cellular infrastructure, i.e., base stations and wired backbone lines. We formulate the IPSN group formation as a clustering problem. A subset of user equipments (UEs), called group owners (GOs), are chosen to serve as virtual base stations, and each non-GO UE, referred to as group member, is associated with a GO as its member. We propose a novel clustering algorithm in the framework of affinity propagation, which is a state-of-the-art message-passing technique with a graphical model approach developed in the machine learning field. Unlike conventional clustering approaches, the proposed clustering algorithm minimizes the total energy consumption while guaranteeing link reliability by adjusting the number of GOs. Simulation results verify that the IPSN optimized by the proposed clustering algorithm reduces the total energy consumption of the network by up to 31 % compared to the conventional clustering approaches.

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“…The proposed 3D 3GPP channel model introduced the zenith AoD and zenith AoA that are modeled by inverse Laplacian functions [117]. The 3D extensions of SCM and the WINNERII/WINNER+ channel models were proposed in [118] and [119], respectively, and an extension of the IMT-A channel model to the elevation plane was proposed in [120], [121]. However, none of the aforementioned channel models has considered any of the HST scenarios.…”

Section: D Hst Channel Modelsmentioning

confidence: 99%