Study on 3GPP Rural Macrocell Path Loss Models for Millimeter Wave Wireless Communications

Little research has been done to reliably model millimeter wave (mmWave) path loss in rural macrocell settings, yet, models have been hastily adopted without substantial empirical evidence. This paper studies past rural macrocell (RMa) path loss models and exposes concerns with the current 3rd Generation Partnership Project (3GPP) TR 38.900 (Release 14) RMa path loss models adopted from the International Telecommunications Union - Radiocommunications (ITU-R) Sector. This paper shows how the 3GPP RMa large-scale path loss models were derived for frequencies below 6 GHz, yet they are being asserted for use up to 30 GHz, even though there has not been sufficient work or published data to support their validity at frequencies above 6 GHz or in the mmWave bands. We present the background of the 3GPP RMa path loss models and their use of odd correction factors not suitable for rural scenarios, and show that the multi-frequency close-in free space reference distance (CI) path loss model is more accurate and reliable than current 3GPP and ITU-R RMa models. Using field data and simulations, we introduce a new close-in free space reference distance with height dependent path loss exponent model (CIH), that predicts rural macrocell path loss using an effective path loss exponent that is a function of base station antenna height. This work shows the CI and CIH models can be used from 500 MHz to 100 GHz for rural mmWave coverage and interference analysis, without any discontinuity at 6 GHz as exists in today's 3GPP and ITU-R RMa models.Comment: To be published in 2017 IEEE International Conference on Communications (ICC), Paris, France, May 201

A Novel Millimeter-Wave Channel Simulator and Applications for 5G Wireless Communications

This paper presents details and applications of a novel channel simulation software named NYUSIM, which can be used to generate realistic temporal and spatial channel responses to support realistic physical- and link-layer simulations and design for fifth-generation (5G) cellular communications. NYUSIM is built upon the statistical spatial channel model for broadband millimeter-wave (mmWave) wireless communication systems developed by researchers at New York University (NYU). The simulator is applicable for a wide range of carrier frequencies (500 MHz to 100 GHz), radio frequency (RF) bandwidths (0 to 800 MHz), antenna beamwidths (7 to 360 degrees for azimuth and 7 to 45 degrees for elevation), and operating scenarios (urban microcell, urban macrocell, and rural macrocell), and also incorporates multiple-input multiple-output (MIMO) antenna arrays at the transmitter and receiver. This paper also provides examples to demonstrate how to use NYUSIM for analyzing MIMO channel conditions and spectral efficiencies, which show that NYUSIM is an alternative and more realistic channel model compared to the 3rd Generation Partnership Project (3GPP) and other channel models for mmWave bands.Comment: 7 pages, 8 figures, in 2017 IEEE International Conference on Communications (ICC), Paris, May 201

Measurement-based Close-in Path Loss Modeling with Diffraction for Rural Long-distance Communications

In this letter, we investigate rural large-scale path loss models based on the measurements in a central area of South Korea (rural area) in spring. In particular, we develop new close-in (CI) path loss models incorporating a diffraction component. The transmitter used in the measurement system is located on a hill and utilizes omnidirectional antennas operating at 1400 and 2250 MHz frequencies. The receiver is also equipped with omnidirectional antennas and measures at positions totaling 3,858 (1,262 positions for LOS and 2,596 positions for NLOS) and 4,957 (1,427 positions for LOS and 3,530 positions for NLOS) for 1400 and 2250 MHz, respectively. This research demonstrates that the newly developed CI path loss models incorporating a diffraction component significantly reduce standard deviations (STD) and are independent of frequency, especially for LOS beyond the first meter of propagation, making them suitable for use with frequencies up to a millimeter-wave.Comment: 5 pages, 5 figure

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201

Receiver diversity with selection combining for drone communication around buildings at frequency 10 GHz

The communication network for cellular network keep development. This research analyzed about cellular network was used drone network. The mobile drone used frequency at 10 GHz for communication. The mobile drone moved around buildings. Buildings were used high variation. Base Station placed around building. This research was using macro diversity Base Station, variation building, variation modulation, and variation height of drone trajectory. Macro diversity mechanism used for that two Base Station. Selection Combining (SC) method was used for that macro diversity mechanism. The modulation communication based from Adaptive Modulation and Coding (AMC). Adaptive Modulation and Coding (AMC) was used Modulation and coding scheme (MCS). Modulation was used QPSK, 16 QAM, and 64 QAM. As the result described signal to noise ratio (SNR) at every node communication, probability MCS, and percentage coverage of drone trajectory. MCS probability for 64 QAM become increased with selection combining method. The percentages coverage of drone trajectory was obtained 77.2% of the first BS, 66.8% of the second BS, and 87.2% with SC method.The communication network for cellular network keep development. This research analyzed about cellular network was used drone network. The mobile drone used frequency at 10 GHz for communication. The mobile drone moved around buildings. Buildings were used high variation. Base Station placed around building. This research was using macro diversity Base Station, variation building, variation modulation, and variation height of drone trajectory. Macro diversity mechanism used for that two Base Station. Selection Combining (SC) method was used for that macro diversity mechanism. The modulation communication based from Adaptive Modulation and Coding (AMC). Adaptive Modulation and Coding (AMC) was used Modulation and coding scheme (MCS). Modulation was used QPSK, 16 QAM, and 64 QAM. As the result described signal to noise ratio (SNR) at every node communication, probability MCS, and percentage coverage of drone trajectory. MCS probability for 64 QAM become increased with selection combining method. The percentages coverage of drone trajectory was obtained 77.2% of the first BS, 66.8% of the second BS, and 87.2% with SC method

Impact of the propagation model on the capacity in small‐cell networks: comparison between the UHF/SHF and the millimetre wavebands

This work shows how both frequency and the election of path loss model affect estimated spectral efficiency. Six different frequency bands are considered, ranging from 2.6 GHz in the Ultra High Frequency (UHF) band to 73 GHz in the millimetre wave bands (mmWaves), using both single-slope and two-slope path-loss models. We start by comparing four ur ban path loss models for UHF: the urban/vehicular and pedestrian test environment from the ITU-R M. 1255 Report, which includes the two-slope urban micro line-of-sight (LoS) and NLoS, from the ITU-R 2135 Report. Then, we consider mmWaves taking into con26 sideration the modified Friis propagation model, followed by an analysis of the through put for the 2.6, 3.5, 28, 38, 60 and 73 GHz frequency bands. We have found that the signal to-interference-plus-noise ratio, as estimated with the more realistic two-slope model, is lower for devices that are within the break-point of the transmitter, which is a small dis tance in the UHF/SHF band. As a result, spectral efficiency is higher with mmWaves than with UHF/SHF spectrum when cell radius is under 40 meters but not when cells are larger. Consequently, mmWaves spectrum will be more valuable as cells get small. We also find that capacity as estimated with the two-slope model is considerably smaller than one would obtain with the one-slope model when cells are small but there is little difference in the models when cells are larger. Thus, as cells get smaller, the use of one slope models may underestimate the number of cells that must be deployed.info:eu-repo/semantics/acceptedVersio