# Millimeter Wave Channel Modeling and Cellular Capacity Evaluation

arXiv: https://arxiv.org/abs/1312.4921 · HTML: https://mlsanity.com/paper/1312.4921/

## Key facts

- Cited by 29 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 17 Dec 2013.

## Built on by (most cited first, 29 of 29)

- [Semi-Decentralized Federated Learning with Collaborative Relaying](https://mlsanity.com/paper/2205.10998/) (2022)
- [Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates](https://mlsanity.com/paper/2105.04153/) (2021)
- [Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks](https://mlsanity.com/paper/2102.01751/) (2021)
- [Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying](https://mlsanity.com/paper/2202.11850/) (2022)
- [A Multi-Modal Simulation Framework to Enable Digital Twin-based V2X Communications in Dynamic Environments](https://mlsanity.com/paper/2303.06947/) (2023)
- [Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster](https://mlsanity.com/paper/2112.13194/) (2021)
- [Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach](https://mlsanity.com/paper/2210.01307/) (2022)
- [UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms](https://mlsanity.com/paper/2211.14931/) (2022)
- [Learning and Adaptation for Millimeter-Wave Beam Tracking and Training: a Dual Timescale Variational Framework](https://mlsanity.com/paper/2107.05466/) (2021)
- [Learning-Based Adaptive User Selection in Millimeter Wave Hybrid Beamforming Systems](https://mlsanity.com/paper/2302.08240/) (2023)
- [Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks](https://mlsanity.com/paper/2406.03766/) (2024)
- [Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networks](https://mlsanity.com/paper/2410.05785/) (2024)
- [Learning-Based Multiuser Scheduling in MIMO-OFDM Systems with Hybrid Beamforming](https://mlsanity.com/paper/2506.08263/) (2025)
- [Channel Estimation and Hybrid Architectures for RIS-Assisted Communications](https://mlsanity.com/paper/2104.07115/) (2021)
- [CSI Clustering with Variational Autoencoding](https://mlsanity.com/paper/2111.09758/) (2021)
- [Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systems](https://mlsanity.com/paper/2203.04935/) (2022)
- [Wireless Channel Prediction in Partially Observed Environments](https://mlsanity.com/paper/2207.00934/) (2022)
- [Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems](https://mlsanity.com/paper/2207.14174/) (2022)
- [Learning Optimal Phase-Shifts of Holographic Metasurface Transceivers](https://mlsanity.com/paper/2301.03371/) (2022)
- [UB3: Best Beam Identification in Millimeter Wave Systems via Pure Exploration Unimodal Bandits](https://mlsanity.com/paper/2301.03456/) (2022)
- [Channel Modeling for FR3 Upper Mid-band via Generative Adversarial Networks](https://mlsanity.com/paper/2404.17069/) (2024)
- [On Single-User Interactive Beam Alignment in Next Generation Systems: A Deep Learning Viewpoint](https://mlsanity.com/paper/2102.10229/) (2021)
- [Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications](https://mlsanity.com/paper/2108.13831/) (2021)
- [ProSky: NEAT Meets NOMA-mmWave in the Sky of 6G](https://mlsanity.com/paper/2210.11406/) (2022)
- [Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach](https://mlsanity.com/paper/2308.13616/) (2023)
- [HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceivers](https://mlsanity.com/paper/2401.05420/) (2023)
- [Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems](https://mlsanity.com/paper/2401.12801/) (2024)
- [DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for Mobile Users](https://mlsanity.com/paper/2502.01278/) (2025)
- [GCNO: Gramian Chebyshev Neural Operator for Physics-Based Compression of Wireless Channels](https://mlsanity.com/paper/2608.18522/) (2026)

## Abstract (arXiv)

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 City 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 m to 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.