<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>5G NR sidelink time domain based resource allocation in C-V2X</title>
      <link>https://trid.trb.org/View/2517044</link>
      <description><![CDATA[This study explores the need for efficient resource allocation in fifth generation (5G) New Radio (NR) sidelink communication for cellular vehicle-to-everything (C-V2X) applications. With the advent of 5G networks, C-V2X can enable direct connection between neighboring vehicles and infrastructure without relying on the cellular network. However, direct communication between devices in 5G NR sidelink makes resource allocation more challenging than in a cellular network. Efficient resource allocation is essential to maintain dependable communication, especially in crowded and interference-prone contexts. There are different type of resource allocation methods such as time-domain, frequency-domain, and power-domain resource allocation, which can be used separately or in combination to achieve efficient resource allocation. In this study, the authors discuss time domain based resource allocation method based on packet generation time and packet allocation time. The implications of efficient resource allocation in 5G NR sidelink in C-V2X include increased signal-to-noise ratio, reduced interference, lower latency, and increased network capacity. The proposed approach is demonstrated on a Network Simulator (NS3.34) along with the traffic scenarios generated using Simulated Urban Mobility (SUMO). The authors' results demonstrate that time allocation is a promising approach to achieve efficient resource allocation, enabling safer and more effective transportation systems for C-V2X applications.]]></description>
      <pubDate>Wed, 26 Mar 2025 09:06:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2517044</guid>
    </item>
    <item>
      <title>Intelligent Reflecting Surfaces Assisted Cellular V2X Based Open RAN Communications</title>
      <link>https://trid.trb.org/View/2423260</link>
      <description><![CDATA[Open radio access networks (RAN) enhance the capabilities of traditional RAN by introducing features such as interoperability, open interfaces, software/hardware separation, and intelligence. Open RAN has several use cases in cellular vehicle-to-everything communications such as low-latency information exchange between vehicles and RAN intelligence controller (RIC). However, efficient data sharing between vehicles and RIC suffers from the challenges of signal loss due to mobility and dynamic channel conditions. In this regard, intelligent reflecting surfaces (IRS) have emerged as an intriguing concept of reconfigurable and smart environments to improve the performance of wireless communication systems. In the proposed research, the authors' main focus is on a multi-IRS aided single input single output system, where open RAN base stations (BS) convey information to a remote vehicular user. The transmitted signal is reflected by IRSs via multi-hop passive beamforming over pairwise line-of-sight links. To maximize the overall network sum-rate, the authors propose an IRS assignment method that allocates either a single IRS or multiple IRSs to each BS-user pair. In particular, the proposed algorithm consists of two stages, where in the first stage the authors perform k-means clustering to group the IRSs according to their location. In the next stage, for each group, the authors select the best IRS-assisted path (based on received signal strength) by transforming the original network into a trellis graph and using a trellis-search method. Simulation results show that the proposed technique outperforms existing IRS selection techniques in multiple IRS-enabled multi-hop communication systems.]]></description>
      <pubDate>Tue, 24 Sep 2024 14:26:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2423260</guid>
    </item>
    <item>
      <title>TRASH: Traffic Aware Hybrid-CRAN Scheme for V2I Connectivity Enhancement</title>
      <link>https://trid.trb.org/View/2314000</link>
      <description><![CDATA[Cloud Radio Access Network (CRAN) is the most preferred cellular architecture to support millimeter wave (mm-Wave) transmission. It fulfills the requisites of mm-Wave communication by enabling Coordinated Multi Point (CoMP) framework. However, the potential of CRAN is not exploited to its maximum, especially from the perspective of high mobility scenarios, e.g., vehicular transmission. This is because; a) CoMP-enabled framework requires exceptional pretransmission processing, which may soon outdate for high mobility scenarios, b) the network is usually designed to support peak hour traffic and hence mostly remains under-utilized during sparse traffic conditions. To mitigate such issues, this work proposes a modified RAN with Traffic Aware Hybrid CRAN Scheme (TRASH) aiming two-fold advantages; a) micro-Wave and mm-Wave are jointly utilized to enhance reliability of fast moving scenario, b) a sub-RAN architecture is introduced to support traffic fluctuations. Moreover, through simulation results, it is shown that TRASH provides significant performance enhancement against traditional CRAN based transmission.]]></description>
      <pubDate>Wed, 27 Dec 2023 10:28:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2314000</guid>
    </item>
    <item>
      <title>Coverage Analysis of mmWave and THz-Enabled Aerial and Terrestrial Heterogeneous Networks</title>
      <link>https://trid.trb.org/View/2059452</link>
      <description><![CDATA[Heterogeneous networks (HetNets) are becoming a promising solution for future wireless systems to satisfy the high data rate requirements. This paper introduces a stochastic geometry framework for the analysis of the downlink coverage probability in a multi-tier HetNet consisting of a macro-base station (MBS) operating at sub-6 GHz, millimeter wave (mmWave)-enabled unmanned aerial vehicles (UAVs) operating at 28 GHz, and small BSs operating both at mmWave and THz frequencies. The analytical expressions for the coverage probability for each tier have been derived in the paper. Monte Carlo simulations are then performed to validate the analytical expressions. The effectiveness of the HetNet is analyzed on various performance metrics including association and coverage probabilities for different network parameters. The authors show that the mmWave and THz-enabled cells provide significant improvement in the achievable data rates because of their high available bandwidths, however, they have a degrading effect on the coverage probability due to their high propagation losses.]]></description>
      <pubDate>Tue, 21 Mar 2023 09:27:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2059452</guid>
    </item>
    <item>
      <title>Age of Processing-Based Data Offloading for Autonomous Vehicles in MultiRATs Open RAN</title>
      <link>https://trid.trb.org/View/2059367</link>
      <description><![CDATA[Today, vehicles use smart sensors to collect data from the road environment. This data is often processed onboard of the vehicles, using expensive hardware. Such onboard processing increases the vehicle’s cost, quickly drains its battery, and exhausts its computing resources. Therefore, offloading tasks onto the cloud is required. Still, data offloading is challenging due to low latency requirements for safe and reliable vehicle driving decisions. Moreover, age of processing was not considered in prior research dealing with low-latency offloading for autonomous vehicles. This paper proposes an age of processing-based offloading approach for autonomous vehicles using unsupervised machine learning, Multi-Radio Access Technologies (multi-RATs), and Edge Computing in Open Radio Access Network (O-RAN). The authors design a collaboration space of edge clouds to process data in proximity to autonomous vehicles. To reduce the variation in offloading delay, they propose a new communication planning approach that enables the vehicle to optimally preselect the available RATs such as Wi-Fi, LTE, or 5G to offload tasks to edge clouds when its local resources are insufficient. They formulate an optimization problem for age-based offloading that minimizes elapsed time from generating tasks and receiving computation output. To handle this non-convex problem, they develop a surrogate problem. Then, they use the Lagrangian method to transform the surrogate problem to unconstrained optimization problem and apply the dual decomposition method. The simulation results show that their approach significantly minimizes the age of processing in data offloading with 90.34% improvement over similar method.]]></description>
      <pubDate>Mon, 27 Feb 2023 08:51:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2059367</guid>
    </item>
    <item>
      <title>Modeling and simulation of dynamic lane reversal using a cell transmission model</title>
      <link>https://trid.trb.org/View/2047376</link>
      <description><![CDATA[In recent years, large-scale testing has begun for connected and autonomous driving, making it possible to implement the concept of Dynamic Lane Reversal (DLR), which can quickly shift lane directions to reflect instantaneous flow dynamics. DLR is to make full use of road space and avoid waste of road capacity, and potentially alleviate congestion. However, the impact and feasibility of DLR remain unclear. In order to investigate the effectiveness, the feasibility, and the applicability of DLR, the authors utilize a direction-changeable, lane-based cell transmission model to find an optimal DLR scheme for a roadway segment with stochastic traffic flow in both directions. A proxy model is also designed to realize DLR in VISSIM. A regression analysis was carried out to find the impacts of directional flow rate and the number of lanes on delay reductions due to DLR. Results indicate that implementing DLR can reduce the total queuing delay considerably compared to traditional reversible lane strategies. They found that DLR achieved superior performance on segments with more lanes and when the flows from each direction were close to one another. A zigzag frontier of the delay reduction was discovered. Findings from this research shed light on the feasibility and effectiveness of DLR on various types of road segments.]]></description>
      <pubDate>Fri, 30 Dec 2022 16:58:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2047376</guid>
    </item>
    <item>
      <title>Perception synergy optimization with deep reinforcement learning for cooperative perception in C-V2V scenarios</title>
      <link>https://trid.trb.org/View/2047523</link>
      <description><![CDATA[Autonomous vehicles rely on sensors such as cameras and Lidar to collect data to make safe driving decisions. However, the sensors of a single-vehicle may be blocked or interfered with, and insufficient perception limits the safety of autonomous driving. Cooperative perception is to expand the perception domain of a single vehicle by sharing the perception data. Cooperative perception based on V2V (Vehicle to Vehicle) broadcast communication mode still might transmit a large amount of redundant data. Based on C-V2V (Cellular Vehicle to Vehicle) mmWave communication, fortunately, the edge server interconnected with the 5G small cell base station provides the possibility of centralized processing to leverage the perception interactive features among vehicles to carry on effective cooperative perception scheming. In this paper, a method based on deep reinforcement learning is designed to make centralized decisions to optimize the synergy of cooperative perception. In a highway scene, the road is firstly partitioned into several regions. As for each region, the interactive perception features of local vehicles and the regional perception features are obtained by an embedding method. Subsequently, according to the embedded features, the deep Q-learning network generates a perception combination of multiple vehicles to improve the perception synergy. Compared with the baselines, the authors' proposed method improves the perception synergy. The experiment results show that their trained model has generalization ability and the end-to-end delay is under the constraint of safety critical applications.]]></description>
      <pubDate>Fri, 30 Dec 2022 16:58:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2047523</guid>
    </item>
    <item>
      <title>Success Probability Analysis of Cooperative C-V2X Communications</title>
      <link>https://trid.trb.org/View/1993933</link>
      <description><![CDATA[In this paper, the authors present the success probability analysis of cooperative cellular vehicle-to-everything (C-V2X) communications, i.e., cellular-relay V2X communications. They model the spatial layout of macro base stations (MBSs) as a 2D Poisson point process (PPP) and roads as a Poisson line process (PLP), with road wireless nodes (including vehicles and roadside units) modeled as a 1D PPP on each road. For a typical source node, they calculate the signal-to-interference ratio (SIR)-based success probability of transmitting a packet to its closest destination node with the assist of its nearest MBS. They take into account three cooperative transmission schemes and derive their expressions of joint success probability in two consecutive phases considering the correlation of road topology and nodes’ locations, respectively. They verify the accuracy of their analytical results through Monte-Carlo simulations. In addition, they explore the impacts of several key parameters on the success probability and discuss the selection of transmission schemes.]]></description>
      <pubDate>Fri, 30 Sep 2022 14:27:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1993933</guid>
    </item>
    <item>
      <title>Performance Analysis Using Full Duplex Discovery Mechanism in 5G-V2X Communication Networks</title>
      <link>https://trid.trb.org/View/2005866</link>
      <description><![CDATA[In the past few years, the industry and academia have been working hard to establish and standardise vehicle-to-everything (V2X) communications, which is one of the vital emerging services for next generation wireless networks. Therewith, to control radio resource properly with balanced implementation complexity, a full-duplex V2V discovery mechanism in a 5G-V2X network based on a random backoff procedure is proposed to better utilize radio resources on which nearby vehicles establish their links. The main aims are to reduce complexity, latency, improve throughput and guarantee stability for V2V communication. The unemployed cellular network channel operates in full-duplex mode and leaves the channel as soon as they are informed that someone is in place through latency and throughput analysis technique. The channel sensing and identification are done in a cooperative manner before transmission. Furthermore, two effective resource distribution algorithms are proposed that grant the optimum resource distribution for V2V and V2I-Users. The detection and the throughput of full-duplex V2V communication in the proposed scheme have been formulated and the performance of the proposed scheme and validity of corresponding analysis are verified through simulation experiments.]]></description>
      <pubDate>Fri, 30 Sep 2022 14:27:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2005866</guid>
    </item>
    <item>
      <title>Cooperative Ramp Merging Design and Field Implementation: A Digital Twin Approach Based on Vehicle-to-Cloud Communication</title>
      <link>https://trid.trb.org/View/1948138</link>
      <description><![CDATA[Ramp merging is considered as one of the most difficult driving scenarios due to the chaotic nature in both longitudinal and lateral driver behaviors (namely lack of effective coordination) in the merging area. In this study, the authors have designed a cooperative ramp merging system for connected vehicles, allowing merging vehicles to cooperate with others prior to arriving at the merging zone. Different from most of the existing studies that utilize dedicated short-range communication, they adopt a Digital Twin approach based on vehicle-to-cloud communication. On-board devices upload the data to the cloud server through the 4G/LTE cellular network. The server creates Digital Twins of vehicles and drivers whose parameters are synchronized in real time with their counterparts in the physical world, processes the data with the proposed models in the digital world, and sends advisory information back to the vehicles and drivers in the physical world. A real-world field implementation has been conducted in Riverside, California, with three passenger vehicles. The results show the proposed system addresses the issues of safety and environmental sustainability with an acceptable communication delay, compared to the baseline scenario where no advisory information is provided during the merging process.]]></description>
      <pubDate>Mon, 27 Jun 2022 17:19:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1948138</guid>
    </item>
    <item>
      <title>Handover Count Based MAP Estimation of Velocity With Prior Distribution Approximated via NGSIM Data-Set</title>
      <link>https://trid.trb.org/View/1948128</link>
      <description><![CDATA[In this paper, the authors propose a maximum-a-posteriori probability (MAP) based velocity estimation technique in which the prior distribution is defined by current location of the user. Motivation of this work is to improve accuracy of the existing velocity estimation techniques which are either solely based on cellular network measurements or location specific information. Their objective is to exploit both cellular measurements and location information in Bayesian sense; thus, to jointly address the critical applications of mobility management in Heterogeneous-Networks (HetNets), and intelligent transportation system. Here they assume that the Next Generation Simulation (NGSIM) data set for velocity is available at the current location and can be utilized to approximate the prior distribution. Additional information in form of prior distribution function is then exploited to improve the minimum variance unbiased (MVU) estimate of velocity which is based on handover count measurements. Since MVU estimate is a random variable, they first formulate its density function parameterized over the actual velocity. Next, they follow Bayesian approach to accommodate both prior distribution and parametric density function in deriving posterior density function of velocity. Finally, the authors derive expression of the MAP estimator considering various standard distribution functions which best fit to the density function obtained from NGSIM data set. In order to quantify the quality of estimate, they derive its variance and the corresponding Cramer-Rao-bound (CRB) on the minimum error variance. Numerical results demonstrate that the proposed estimator which incorporates NGSIM data set is asymptotically efficient and outperforms other classical handover count based estimation techniques.]]></description>
      <pubDate>Mon, 27 Jun 2022 17:19:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1948128</guid>
    </item>
    <item>
      <title>Analysis of LTE-M Adjacent Channel Interference in Rail Transit</title>
      <link>https://trid.trb.org/View/1971053</link>
      <description><![CDATA[Long Term Evolution-Metro (LTE-M), as a special communication system for train control, has strict requirements on adjacent channel interference (ACI). According to the 3rd Generation Partnership Project (3GPP) protocol of the European Telecommunications Standards Institute (ETSI) standards, this paper presents the required isolation degree for LTE-M systems to resist ACI. Aiming at the scenario of leaky cable transmission and antenna transmission adopted by the underground LTE-M system of the subway, the isolation degree required for LTE-M system deployment is deduced by combining the channel description with the principle of ACI. For the coexistence of a LTE-M system and an adjacent cellular system in a subway ground scenario, the Monte-Carlo (MC) method is used to simulate several conceivable scenarios of the LTE-M system and the adjacent frequency cellular system. In addition, the throughput loss of the LTE-M system is estimated by considering signal to interference plus noise ratio (SINR). Simulation results demonstrate that adjacent frequency user equipment (UE) has negligible small interference with the LTE-M underground system when using the leaky cable radiation pattern, whereas for the LTE-M ground system, the main interference comes from the adjacent frequency UE to the LTE-M base station (BS). Finally, interference avoidance solutions are presented, which can be utilized as a reference in the design and deployment of LTE-M systems in the rail transit environment.]]></description>
      <pubDate>Fri, 24 Jun 2022 17:06:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1971053</guid>
    </item>
    <item>
      <title>Improvement of Throughput in Vehicular Ad-hoc Networks using RODEO, a Method for Radio Resource Reallocation over LTE</title>
      <link>https://trid.trb.org/View/1869734</link>
      <description><![CDATA[This paper treats the sharing of the radio spectrum by cellular and vehicular users, focusing on maximizing the throughput of these users while respecting the required quality of service. The proposed method is called RODEO, and has been designed for the development of applications in a cooperative vehicular scenario, where vehicles in a certain geographical area have to share information related to traffic situations or events. In a high density scenario, nodes, such as vehicles, move at high speed and technologies like IEEE 802.11p have some drawbacks, which can be overcome by cellular technologies like LTE. RODEO has been tested, validated, and compared with other techniques. The throughput provided by it to cellular and vehicular nodes that co-exist in different scenarios, minimizing the use of resources while maintaining the quality of service demanded by the applications of the global set of users, has been measured.]]></description>
      <pubDate>Sun, 31 Oct 2021 19:08:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1869734</guid>
    </item>
    <item>
      <title>Perceptive Mobile Networks: Cellular Networks With Radio Vision via Joint Communication and Radar Sensing</title>
      <link>https://trid.trb.org/View/1855117</link>
      <description><![CDATA[Joint communication and radar/radio sensing (JCAS), also known as "dual-function radar communications", enables the integration of communication and radio sensing into one system, sharing a single transmitted signal. The perceptive mobile network (PMN) is a natural evolution of JCAS from simple point-to-point links to a mobile/cellular network with integrated radio-sensing capability. In this article, the authors present a system architecture that unifies three types of sensing, investigate the required modifications to existing mobile networks, and exemplify the signals applicable to sensing. The authors also provide a review to stimulate research problems and potential solutions, including mutual information, joint design and optimization for waveform and antenna grouping, clutter suppression, sensing parameter estimation and pattern recognition, and networked sensing under the cellular topology.]]></description>
      <pubDate>Tue, 24 Aug 2021 10:35:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1855117</guid>
    </item>
    <item>
      <title>Decentralized Configuration Protocols for Low-Cost Offloading From Multiple Edges to Multiple Vehicular Fogs</title>
      <link>https://trid.trb.org/View/1770786</link>
      <description><![CDATA[A vehicular-fog (VF) system as an emerging platform consists of electric vehicles with computing resources that are mostly under-utilized. This paper considers a two-tier federated Edge and Vehicular-Fog (EVF) system, where edge systems may partially offload user traffic to nearby VFs for potential cost reduction. Offloading configuration is to determine the ratios and targets of offloading traffic for maximal cost reduction, which is formulated as a mixed integer programming problem in this paper. The authors first present a decentralized offloading configuration protocol (DOCP) for an individual edge system to set up its own offloading configuration. The authors then propose a matching protocol among multiple edge systems to resolve resource contention when they simultaneously request resources from the same VF. Simulation results show that the proposed approach can leverage the heterogeneity of cost and capacity between edge systems and VFs. The proposed protocol outperforms greedy approaches by at most 40% and is comparable to a centralized off-line approach that is based on Particle Swarm Optimization.]]></description>
      <pubDate>Thu, 22 Apr 2021 17:48:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/1770786</guid>
    </item>
  </channel>
</rss>