Wireless InSite References
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.
1. Comparative Characterization of Indoor VLC and MMW Communications via Ray Tracing Simulations
The demand for ultra-high-speed indoor wireless connectivity is ever-increasing, which poses unique challenges for the next generation wireless communication system design. This has prompted the exploration of higher frequency bands including millimeter wave (MMW) and visible light bands in addition to the conventional sub-6 GHz band. This paper provides a comprehensive comparison of the propagation channels of these frequency bands under the same indoor environment and scenarios. We adopt ray tracing techniques for site-specific channel modeling, which enables the consideration of the three-dimensional models of the indoor environment and objects inside. It allows us to take into account different frequencies, i.e., 2.4 GHz, 6 GHz, 28 GHz, 60 GHz, 100 GHz, and visible light band as well as different transmitter types, i.e., omnidirectional/directional antennas for radio frequency systems and indoor luminaries for visible light communications (VLC). For different frequencies under consideration, we obtain channel impulse responses (CIRs) and present the channel path losses for various user trajectories in indoor environments. Furthermore, we propose closed-form expressions for the cumulative distribution functions (CDFs) of received power levels for all frequency bands under consideration. Our results demonstrate that VLC channels exhibit lower path loss than that in MMW bands but higher than that of 2.4 GHz band. In addition, it is observed that VLC systems exhibit more sensitivity to shadowing and blockage effects. Our findings further indicate that the characteristics of the propagation channel are greatly influenced by the antenna type. For instance, using omnidirectional and rectangular patch antennas results in lower path loss compared to horn antennas, and this difference becomes more significant as the transmission distance decreases.
F. Aghaei, H. B. Eldeeb, L. Bariah, S. Muhaidat and M. Uysal, "Comparative Characterization of Indoor VLC and MMW Communications via Ray Tracing Simulations," in IEEE Access, doi: 10.1109/ACCESS.2023.3307186.
2. Supervised Learning Technique for First Order Multipaths Identification of V2V Scenario
In geometrical localization techniques, the propagated signal’s first-order multipath (FOMP) characteristics are used to calculate the location based on geometrical relationships. Utilizing the characteristics of higher order multipath (HOMP) results in a significant localization error. Therefore, distinguishing between FOMPs and HOMPs is an important task. The previous works used traditional methods based on a deterministic threshold to accomplish this task. Unfortunately, these methods are complicated and insufficiently accurate. The authors of this paper propose an efficient method based on supervised learning to distinguish more accurately between the propagated FOMP and HOMP of millimeter-Wave Vehicle-to-Vehicle communication in an urban scenario. Ray tracing technique based on Shoot and Bounce Ray (SBR) is used to generate the dataset’s features including received power, propagation time, the azimuth angle of arrival (AAOA), and elevation angle of arrival (EAOA). A statistical analysis based on the probability distribution function (PDF) is presented first to study the selected features’ impact on the classification process. Then, six supervised classifiers, namely Decision Tree, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Random Forest, and artificial neural network, are trained and tested, and their performance is compared in terms of HOMP misclassification. The effect of the considered features on the classifiers’ performance is further investigated. Our results showed that all the proposed classifiers provided an acceptable classification performance. The proposed ANN showed the best performance, whereas the NB was the worst. In fact, the HOMP misclassification error varied between 2.3% and 16.7%. The EAOA exhibited the most significant influence on classification performance, while the AAOA was the least.
Bakhuraisa YA, Abd Aziz AB, Geok TK, Abu Bakar NB, Jamian SB, Mustakim FB. Supervised Learning Technique for First Order Multipaths Identification of V2V Scenario. World Electric Vehicle Journal. 2023; 14(4):109. https://doi.org/10.3390/wevj14040109
In recent years, there have been increased demands for aggregating sensor information from several sensors owing to the spread of the Internet of Things (IoT). However, packet communication, which is a conventional multiple-access technology, is hindered by packet collisions owing to simultaneous access by sensors and waiting time to avoid packet collisions; this increases the aggregation time. The physical wireless parameter conversion sensor network (PhyC-SN) method, which transmits sensor information corresponding to the carrier wave frequency, facilitates the bulk collection of sensor information, thereby reducing the communication time and achieving a high aggregation success rate. However, when more than one sensor transmits the same frequency simultaneously, the estimation accuracy of the number of accessed sensors deteriorates significantly because of multipath fading. Thus, this study focuses on the phase fluctuation of the received signal caused by the frequency offset inherent to the sensor terminals. Consequently, a new feature for detecting collisions is proposed, which is a case in which two or more sensors transmit simultaneously. Furthermore, a method to identify the existence of 0, 1, 2, or more sensors is established. In addition, the authors demonstrate the effectiveness of PhyC-SNs in estimating the location of radio transmission sources by utilizing three patterns of 0, 1, and 2 or more transmitting sensors.
Ito T, Oda M, Takyu O, Ohta M, Fujii T, Adachi K. Three Level Recognition Based on the Average of the Phase Differences in Physical Wireless Parameter Conversion Sensor Networks and Its Effect to Localization with RSSI. Sensors. 2023; 23(6):3308. https://doi.org/10.3390/s23063308
4. Millimeter-Wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation
In this article, the authors address the radio-based sensing and environment mapping prospects with specific emphasis on the user equipment (UE) side. We first describe an efficient ℓ1 -regularized least-squares (LS) approach to obtain sparse range–angle charts at individual measurement or sensing locations. For the subsequent environment mapping, we then introduce a novel state model for mapping diffuse and specular scattering, which allows efficient tracking of individual scatterers over time using interacting multiple model (IMM) extended Kalman filter and smoother. Also the related measurement selection and data association problems are addressed. We provide extensive numerical indoor mapping results at the 28 GHz band deploying OFDM-based 5G NR uplink waveform with 400 MHz channel bandwidth, covering both accurate ray-tracing based as well as actual RF measurement results. The results illustrate the superiority of the dynamic tracking-based solutions, compared to static reference methods, while overall demonstrate the excellent prospects of radio-based mobile environment sensing and mapping in future mm-wave networks.
C. Baquero Barneto et al., "Millimeter-Wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation," in IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 3900-3916, April 2022, doi: 10.1109/TVT.2022.3146003.
5. Deep Reinforcement Learning-Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks
In this paper, the authors propose a novel deep reinforcement learning (DRL) based coordinated beamforming scheme where multiple base stations serve one mobile station (MS) jointly. The constructed solution then uses a proposed DRL model and predicts the suboptimal beamforming vectors at the base stations (BSs) out of possible beamforming codebook candidates. This solution enables a complete system that facilitates highly mobile mmWave applications with dependable coverage, minimal training overhead, and low latency. Numerical results demonstrate that our proposed algorithm remarkably increases the achievable sum rate capacity for the highly mobile mmWave massive MIMO scenario while ensuring low training and latency overhead.
Tarafder P, Choi W. Deep Reinforcement Learning-Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks. Sensors. 2023; 23(5):2772. https://doi.org/10.3390/s23052772
This article presents a method of preparing reference RSSI distributions using computer simulations. The simulations were conducted using the ray tracing propagation model and the computationally less demanding multiwall model. The simulated RSSI distributions were tested using a location algorithm. Their performance was compared with that of measured RSSI maps. The results show that it is possible to replace the complex creation of reference maps with simulations using the ray tracing model or the multiwall model, both of which provide sufficiently accurate measurements (from the point of view of the user) of 1–2.5 m.
Kawecki R, Hausman S, Korbel P. Performance of Fingerprinting-Based Indoor Positioning with Measured and Simulated RSSI Reference Maps. Remote Sensing. 2022; 14(9):1992. https://doi.org/10.3390/rs14091992
7. Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning.
Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. This paper proposes efficient solutions to LIS challenges by leveraging tools from compressive sensing and deep learning. First, a novel LIS architecture based on sparse channel sensors is proposed. In this architecture, all the LIS elements are passive except for a few elements that are active (connected to the baseband). Two solutions are then developed that design the LIS reflection matrices with negligible training overhead. In the first approach, the authors leverage compressive sensing tools to construct the channels at all the LIS elements from the channels seen only at the active elements. In the second approach, they develop a deep-learning-based solution where the LIS learns how to interact with the incident signal given the channels at the active elements, which represent the state of the environment and transmitter/receiver locations. They show that the achievable rates of the proposed solutions approach the upper bound, which assumes perfect channel knowledge, with negligible training overhead and with only a few active elements, making them promising for future LIS systems.
A. Taha, M. Alrabeiah and A. Alkhateeb, "Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning," in IEEE Access, vol. 9, pp. 44304-44321, 2021, doi: 10.1109/ACCESS.2021.3064073.
8. Enhanced TOA Estimation Using OFDM over Wide-Band Transmission Based on a Simulated Model
This paper presents the advantages of using a wideband spectrum adopting multi-carrier to improve targets localization within a simulated indoor environment using the Time of Arrival (TOA) technique. The study investigates the effect of using various spectrum bandwidths and a different number of carriers on localization accuracy. Also, the paper considers the influence of the transmitters’ positions in line-of-sight (LOS) and non-LOS propagation scenarios. It was found that the accuracy of the proposed method depends on the number of sub-carriers, the allocated bandwidth (BW), and the number of access points (AP). In the case of using large BW with a large number of subcarriers, the algorithm was effective to reduce localization errors compared to the conventional TOA technique. The performance degrades and becomes similar to the conventional TOA technique while using a small BW and a low number of subcarriers.
Obeidatat, H.A., Ahmad, I., Rawashdeh, M.R. et al. Enhanced TOA Estimation Using OFDM over Wide-Band Transmission Based on a Simulated Model. Wireless Pers Commun (2021). https://doi.org/10.1007/s11277-021-09297-z
9. Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems
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.
10. Characterization of mmWave Channel Properties at 28 and 60 GHz in Factory Automation Deployments
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.
11. Addressing Deep Indoor Coverage in Narrowband-5G
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.
12. Effects of Crowd Density on Radio Propagation at 24 GHz in a Pedestrian Tunnel for 5G Communications
In this paper, the authors report the results of radio propagation characterization in a pedestrian tunnel with different crowd densities at 24 GHz using commercial ray-tracing software called Wireless InSite. The 3D empty tunnel and human body models we created using computer-aided design software and imported into Wireless InSite. The tunnel model is based on a pedestrian tunnel connecting Suria and KLCC, which is located in the heart of Kuala Lumpur. Five three-dimensional (3D) human body models with different levels of detail were developed and tested. The crowd densities investigated were 0, 0.05, 0.1, 0.15 and 0.2 people/ m2 which correspond to 0, 25, 50, 75, and 100 people, respectively, in the study area. The results show that the path loss exponent, log-normal shadowing’s standard deviation, and fluctuation in received power increase as the number of people increases. When the crowd density is above 0.1 people/ m2 , the path loss exponent of the large-scale path loss model is higher than that of the empty tunnel. The results of this study are also useful for understanding the effects of human crowds on millimetre wave propagation in indoor tunnel-like environments such as hallways, enclosed corridors, mines, and transportation tunnels. The findings contribute to increasing the effectiveness of network planning and deployment for 5G communication, especially in pedestrian tunnels.
I. H. P. Tai, H. S. Lim, K. S. Diong and K. A. Alaghbari, "Effects of Crowd Density on Radio Propagation at 24 GHz in a Pedestrian Tunnel for 5G Communications," in IEEE Access, vol. 11, pp. 40240-40248, 2023, doi: 10.1109/ACCESS.2023.3269813.
13. Machine Learning for Reliable mmWave Systems: Blockage Prediction and Proactive Handoff
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.
14. Joint Optimization of Hybrid Beamforming for Multi-User Massive MIMO Downlink
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.
15. 60 GHz channel measurements and ray tracing modeling in an indoor environment
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).
17. Beamformed Fingerprint Learning for Accurate Millimeter Wave Positioning
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.
18. Angular and Temporal Correlation of V2X Channels Across Sub-6 GHz and mmWave Bands
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.
19. MmWave Beam Prediction with Situational Awareness: A Machine Learning Approach
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.