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Recent years have seen the introduction of large- scale platforms for experimental wireless research. These platforms, which include testbeds like those of the PAWR program and emulators like Colosseum, allow researchers to prototype and test their solutions in a sound yet realistic wireless environment before actual deployment. Emulators, in particular, enable wire- less experiments that are not site-specific as those on real testbeds. Researchers can choose among different radio frequency (RF) scenarios for real-time emulation of a vast variety of different situations, with different numbers of users, RF bandwidth, antenna counts, hardware requirements, etc. Although very powerful, in that they can emulate virtually any real-world deployment, emulated scenarios are only as useful as how accurately they can capture the targeted wireless channel and environment. Achieving emulation accuracy is particularly challenging, especially for experiments at scale for which emulators require considerable amounts of computational resources. In this paper we propose a framework to create RF scenarios for emulators like Colosseum from rich forms of inputs, like those obtained by measurements through radio equipment or via software (e.g., ray-tracers and electromagnetic field solvers). Our framework optimally scales down the large set of RF data in input to the fewer parameters allowed by the emulator by using efficient clustering techniques and channel impulse response re-sampling. We showcase our method by generating wireless scenarios for Colosseum by using Remcom’s Wireless InSite, a commercial-grade ray-tracer that produces key characteristics of the wireless channel. Examples are provided for line-of-sight and non-line-of-sight scenarios on portions of the Northeastern University main campus. 

The extremely high data rates provided by communications in the millimeter-length (mmWave) frequency bands can help address the unprecedented demands of next-generation wireless communications. However, atmospheric attenuation and high propagation loss severely limit the coverage of mmWave networks. To overcome these challenges, multi-input-multi-output (MIMO) provides beamforming capabilities and high-gain steer- able antennas to expand communication coverage at mmWave frequencies. The main contribution of this paper is the per- formance evaluation of mmWave communications on top of the recently released NR standard for 5G cellular networks. Furthermore, we compare the performance of NR with the 4G long-term evolution (LTE) standard on a highly realistic campus environment. We consider physical layer constraints such as transmit power, ambient noise, receiver noise figure, and practical antenna gain in both cases, and examine bitrate and area coverage as the criteria to benchmark the performance. We also show the impact of MIMO technology to improve the performance of the 5G NR cellular network. Our evaluation demonstrates that 5G NR provides on average 6.7 times bitrate improvement without remarkable coverage degradation. 

This paper concerns the task of generating simpler yet accurate mmWave channel models based on clustering all multipath components arriving at the receiver. Our work focuses on 28 GHz communications in urban outdoor scenarios simulated with a ray-tracer tool. We investigate the effectiveness of k- means and k-power-means clustering algorithms in predicting the optimal number of clusters by using cluster validity indices (CVIs) and score fusion techniques. Our results show how the joint use of these techniques generate accurate approximation of the mmWave large-scale and small-scale channel models, greatly simplifying the complexity of analyzing large amount of rays at any receiver location. 

Radio channel propagation models for the millimeter wave (mmWave) spectrum are extremely important for planning future 5G wireless communication systems. Transmitted radio signals are received as clusters of multipath rays. Identifying these clusters provides better spatial and temporal characteristics of the mmWave channel. This paper deals with the clustering process and its validation across a wide range of frequencies in the mmWave spectrum below 100 GHz. By way of simulations, we show that in outdoor communication scenarios clustering of received rays is influenced by the frequency of the transmitted signal. This demonstrates the sparse characteristic of the mmWave spectrum (i.e., we obtain a lower number of rays at the receiver for the same urban scenario). We use the well-known k-means clustering algorithm to group arriving rays at the receiver. The accuracy of this partitioning is studied with both cluster validity indices (CVIs) and score fusion techniques. Finally, we analyze how the clustering solution changes with narrower-beam antennas, and we provide a comparison of the cluster characteristics for different types of antennas.