The following is a list of scientific and technical articles in which Remcom's software was used in the authors' research. We've included excerpts from the publication abstracts and offsite links to the original published content.
Supporting high mobility in millimeter wave (mmWave) systems enables a wide range of important applications such as vehicular communications and wireless virtual/augmented reality. Realizing this in practice, though, requires overcoming several challenges. In this paper, a novel integrated machine learning and coordinated beamforming solution is developed to overcome these challenges and enable highly-mobile mmWave applications. Simulation results show that the proposed deep-learning coordinated beamforming strategy approaches the achievable rate of the genie-aided solution that knows the optimal beamforming vectors with no training overhead.
Future cellular systems are expected to revolutionize today's industrial ecosystem by satisfying the stringent requirements of ultra-high reliability and extremely low latency. Along these lines, the core technology to support the next-generation factory automation deployments is the use of millimeter-wave (mmWave) communication that operates at extremely high frequencies (i.e., from 10 to 100 GHz). However, characterizing the radio propagation behavior in realistic factory environments is challenging due to shorter mmWave wavelengths, which make channel properties be sensitive to the actual topology and size of the surrounding objects. For these reasons, this paper studies the important mmWave channel properties for two distinct types of factories, namely, light industry and heavy industry. These represent the extreme cases of factory classification based on the level of technology, the density and the size of the equipment, and the goods produced. Accordingly, we assess the candidate mmWave frequencies of 28 and 60 GHz for licensed-and unlicensed-band communication, respectively.
Ubiquitous connectivity is a common requirement of many services considered in Fifth Generation (5G) communication systems. However providing network coverage or wireless connectivity becomes very challenging in deep-indoor scenarios such as underground parking lots where the total channel loss can easily exceed the maximum coupling loss (MCL) of the communication technology. We motivate the importance of deep coverage by conducting a representative site-specific realistic coverage analysis using ray tracing. The results show that existing cellular-based coverage-optimized technologies cannot achieve ubiquitous coverage in deep indoor/underground areas and highlight the need for dynamic multi-hop relaying in 5G MTC.
The sensitivity of millimeter wave (mmWave) signals to blockages is a fundamental challenge for mobile mmWave communication systems. In this paper, we leverage machine learning tools and propose a novel solution for these reliability and latency challenges in mmWave MIMO systems. In the developed solution, the base stations learn how to predict that a certain link will experience blockage in the next few time frames using their past observations of adopted beamforming vectors. This allows the serving base station to proactively hand-over the user to another base station with a highly probable LOS link. Simulation results show that the developed deep learning based strategy successfully predicts blockage/hand-off in close to 95% of the times. This reduces the probability of communication session disconnection, which ensures high reliability and low latency in mobile mmWave systems.
Considering the design of two-stage beamformers for the downlink of multi-user massive multiple-input multiple-output systems in frequency division duplexing mode, this paper investigates the case where both the link ends are equipped with hybrid digital/analog beamforming structures. A virtual sectorization is realized by channel-statistics-based user grouping and analog beamforming, where the user equipment only needs to feedback its intra-group effective channel, and the overall cost of channel state information (CSI) acquisition is significantly reduced. Simulations over the propagation channels obtained from geometric-based stochastic models, ray tracing results, and measured outdoor channels, demonstrate that our proposed beamforming strategy outperforms the state-of-the-art methods.
Millimeter wave (mmWave) communication has become a promising key technology of the fifth generation (5G) communication systems, and gained extensive interests. In this paper, we examine 60 GHz mmWave channels in an indoor office environment by means of ray tracing method. Based on geometrical optic (GO) and uniform theory of diffraction (UTD), ray tracing method uses computer simulation to approximate the radio wave propagation. The accuracy of ray tracing based simulation is guaranteed by a very detailed three-dimensional (3-D) environment model and proper material electromagnetic parameters. The simulation results including power delay profile (PDP) and normalized power angular spectrum (PAS) are compared with the channel measurement data which is processed by the space-alternating generalized expectation-maximization (SAGE) estimation algorithm. The comparison results indicate that ray tracing can be a useful and reliable method for characterizing 60 GHz channel properties.
Millimeter-wave indoor propagation characteristics including path loss models and multipath delay spread values for systems using directional and omnidirectional antennas are presented. The performance of the four 5G candidate frequencies, 28 GHz, 39 GHz, 60 GHz and 73 GHz, are investigated in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios using published real time frequency measurements conducted in indoor environments. Comparisons are made against simulation data obtained from the 3D Ray Tracing Wireless InSite software over Tx-Rx separations of 1.5 m to 62 m. In addition, frequency-dependent electrical properties, such as conductivity-σ and permittivity-ε, of common building materials are incorporated in the simulation. Results show material type influences propagation behavior of mm-waves due to reflections, diffractions and penetrations of walls and objects (obstacles).
With millimeter wave wireless communications, the resulting radiation reflects on most visible objects, creating rich multipath environments, namely in urban scenarios. The radiation captured by a listening device is thus shaped by the obstacles encountered, which carry latent information regarding their relative positions. In this paper, a system to convert the received millimeter wave radiation into the device’s position is proposed, making use of the aforementioned hidden information. Using deep learning techniques and a pre-established codebook of beamforming patterns transmitted by a base station, the simulations show that average estimation errors below 10 meters are achievable in realistic outdoors scenarios that contain mostly non-line-of-sight positions, paving the way for new positioning systems. Index Terms—5G, Beamforming, Deep Learning, mmWaves, Outdoor Positioning.
5G millimeter wave (mmWave) technology is envisioned to be an integral part of next-generation vehicle-toeverything (V2X) networks and autonomous vehicles due to its broad bandwidth, wide field of view sensing, and precise localization capabilities. In this paper, we use ray tracing simulations to characterize the angular and temporal correlation across a wide range of propagation frequencies for V2X channels ranging from 900 MHz up to 73 GHz, for a vehicle maintaining line-of-sight (LOS) and non-LOS (NLOS) beams with a transmitter in an urban environment.
Millimeter-wave communication is a challenge in the highly mobile vehicular context. Traditional beam training is inadequate in satisfying low overheads and latency. In this paper, we propose to combine machine learning tools and situational awareness to learn the beam information (power, optimal beam index, etc) from past observations. We consider forms of situational awareness that are specific to the vehicular setting including the locations of the receiver and the surrounding vehicles. We leverage regression models to predict the received power with different beam power quantizations. The result shows that situational awareness can largely improve the prediction accuracy and the model can achieve throughput with little performance loss with almost zero overhead.