Millimeter Wave Channel Modeling and Cellular Capacity Evaluation

By Mustafa Riza Akdeniz, Yuanpeng Liu, Shu Sun, Sundeep Rangan, Theodore Ted S. Rappaport, Elza Erkip. IEEE Journal on Selected Areas in Communications, 2013

2,401 citations recorded by the source corpus.

With the severe spectrum shortage in conventional cellular bands, millimeter wave (mmW) frequencies between 30 and 300 GHz have been attracting growing attention as a possible candidate for next-generation micro- and picocellular wireless networks. The mmW bands offer orders of magnitude greater spectrum than current cellular allocations and enable very high-dimensional antenna arrays for further gains via beamforming and spatial multiplexing. This paper uses recent real-world measurements at 28 and 73 GHz in New York, NY, USA, to derive detailed spatial statistical models of the channels and uses these models to provide a realistic assessment of mmW micro- and picocellular networks in a dense urban deployment. Statistical models are derived for key channel parameters, including the path loss, number of spatial clusters, angular dispersion, and outage. It is found that, even in highly non-line-of-sight environments, strong signals can be detected 100-200 m from potential cell sites, potentially with multiple clusters to support spatial multiplexing. Moreover, a system simulation based on the models predicts that mmW systems can offer an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks with no increase in cell density from current urban deployments.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/69a6cab0dd64f91548ce9b7feb24cdf92a0d0fe2

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Citations in the corpus

Stochastic Geometry Modeling and Analysis of Multi-Tier Millimeter Wave Cellular Networks (2014, methodology): “Based on these measurements, the authors of [2] have recently investigated system-level performance of mmWave cellular networks and have compared them against conventional micro wave (μWave) cellular networks.”
Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design (2019, methodology): “7dB, as suggested by real-world NLOS channel measurements [40].”
Compressed Channel Estimation for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems (2020, background): “Hopefully, real-world channel measurements [12], [13] have shown that mmWave channels exhibit sparse scattering characteristics, which can be utilized to substantially reduce the training overhead.”
Wideband Millimeter-Wave Propagation Measurements and Channel Models for Future Wireless Communication System Design (2015, methodology): “73 GHz outage probabilities for the hybrid scenario in New York City for T-R separation distances up to 216 m [39], [82].”
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What Will 5G Be? (2014, background): “New channel models capturing these effects are much needed, and in fact currently being developed [67], [70], [71] and applied to system-level analysis [57], [72]–[74] and simulation studies such as [75] and [76] in this special issue.”
A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies (2018, background): “A complete description of the channel parameters can be found in [84], while the main simulation parameters for this paper are reported in Table VI.”
A Comprehensive Survey on Millimeter Wave Communications for Fifth-Generation Wireless Networks: Feasibility and Challenges (2020, methodology): “Speciﬁcally, if there are n rx receive antennas and n tx transmit antennas, the narrow-band time-varying channel gain between a transmit-receiver antenna pair depicts the mmWave channel matrix as [174] where K and L k denote the number of clusters and the number of rays within the k th cluster, resp”
Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing (2020, methodology): “We employ a throughput model based on the Shannon capacity with a certain loss used in [41], in which = 1.6 and ρ max = 4.8.”
Propagation Path Loss Models for 5G Urban Micro- and Macro-Cellular Scenarios (2015, methodology): “Additionally, the 1 m CI model also offers more accurate prediction on path loss beyond measurement ranges when compared to the AB and ABG models as shown in [11], [19], [22].”
Hybrid Beamforming for Massive MIMO - A Survey (2016, background): “• At mm-wave frequencies, the multipath channel experiences higher (by orders-of-magnitude) propagation loss and becomes sparser because diffraction, penetration and blockage show higher attenuation than at cm-waves [53]–[59].”
Millimeter Wave MIMO With Lens Antenna Array: A New Path Division Multiplexing Paradigm (2015, methodology): “Furthermore, the complex-valued path gains { α l } Ll =1 are modeled as α l = √ βκ l e jη l , l = 1 , · · · , L [9], where β denotes the large-scale attenuation including distance-dependent path loss and shadowing, κ l represents the power fractional ratio for the l th path, with are random…”
Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces (2020, neutral): “The path loss can be high in mmWave frequency, for example with rd = 300 m,PT = 20 dBm, fc = 28 GHz scenario, the equivalent receive SNR can be as low as −10 dB and as high as 20 dB, according to the measurement model in [15].”
Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities (2020, background): “Subsequent intensive measurement campaigns [3] have shown the poten-”
Millimeter-Wave Cellular Wireless Networks: Potentials and Challenges (2014, methodology): “Here, we summarize some of the main findings from [34] to help understand the potential capacity of mmW systems, and to identify the key design issues [33].”
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Toward 6G (2021, background): “made it mostly known as a solution for significantly high data rates of multigigabits [113].”
A survey of millimeter wave communications (mmWave) for 5G: opportunities and challenges (2015, background): “Akdeniz et al. [40] derived detailed spatial statistical models of channels at 28 and 73 GHz in New York, NY, USA, and the channel parameters include the path loss, number of spatial clusters, angular dispersion, and outage.”
Channel Estimation in Broadband Millimeter Wave MIMO Systems With Few-Bit ADCs (2016, methodology): “for mmWave broadband MIMO channels when few-bit ADCs are used at the receiver. In the mmWave channel, there are relatively fewer channel paths and large antenna arrays at the transmitter and receiver [31]. As a result, the channel is approximately sparse in the joint angle-delay domain. We design a ”