Channel Quality Research Articles

Evaluating Latency in Fifth-Generation Vehicle-to-Everything Communications using Adaptive Neuro-Fuzzy Inference System Model

The fifth-generation new radio technology (5G NR) introduces improved functions to the air interface. In addition, the 5G NR non-standalone (NSA) will operate with long-term evolution, enabling vehicle-to-everything communications (V2X) for improved infotainment services. V2X includes four main classes of communications: vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-pedestrian devices, and vehicle-to-network. However, the stringent transmission frequency, latency, and throughput requirements of infotainment applications constrain the transmitting packets of 5G-V2X-based NSA in highway scenarios. In this paper, the latency is improved by preventing the physical layer of gNodeB and the user equipment (UE) from sending redundant packets for service in a highway scenario. The proposed approach adopts an adaptive neuro-fuzzy inference system (ANFIS), a powerful modeling technique based on artificial neural networks ,and a fuzzy inference system. The performance of ANFIS is compared with that of the traditional 5G V2X NSA architecture in a simulation study using Voice over Internet Protocol (VoIP) traffic. The delays, throughputs, and packet losses of both architectures are determined in radio link control (RLC) and VoIP applications. The switch-modes, signal-to-interference-noise ratios (SINRs), hybrid automatic repeat request (HARQ) error rate, channel quality indicators (CQIs), served blocks, and transmission-state of gNodeB are computed for the two architectures for device-to-device (D2D), uplink (UL) and downlink (DL) traffic directions. The simulation results show comparable SINRs, CQIs, served blocks ,andswitchmodes in both scenarios, but the presented ANFIS model significantly outperforms the traditional architecture in delay by 66% in D2D, 29% in UL, 25% in DL, and packet loss by 21% in UL in RLC, the HARQ error rate by 9% in D2D, 30% in UL, 95% in DL, transmission-state in gNodeB by 29%, and the delay by 4% for UEs, and frame loss by 90%for UE in VoIP.

Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices

As the number of Internet of things (IoT) devices increases exponentially, scheduling and managing the radio resources for IoT devices has become more important. To efficiently allocate radio resources, the base station (BS) needs the channel state information (CSI) of devices every time. Hence, each device needs to periodically (or aperiodically) report its channel quality indicator (CQI) to the BS. The BS determines the modulation and coding scheme (MCS) based on the CQI reported by the IoT device. However, the more a device reports its CQI, the more the feedback overhead increases. In this paper, we propose a long short-term memory (LSTM)-based CQI feedback scheme, where the IoT device aperiodically reports its CQI relying on an LSTM-based channel prediction. Additionally, because the memory capacity of IoT devices is generally small, the complexity of the machine learning model must be reduced. Hence, we propose a lightweight LSTM model to reduce the complexity. The simulation results show that the proposed lightweight LSTM-based CSI scheme dramatically reduces the feedback overhead compared with that of the existing periodic feedback scheme. Moreover, the proposed lightweight LSTM model significantly reduces the complexity without sacrificing performance.

Detection and Reconstruction of Poor-Quality Channels in High-Density EMG Array Measurements

High-density electromyography (HD-EMG) arrays allow for the study of muscle activity in both time and space by recording electrical potentials produced by muscle contractions. HD-EMG array measurements are susceptible to noise and artifacts and frequently contain some poor-quality channels. This paper proposes an interpolation-based method for the detection and reconstruction of poor-quality channels in HD-EMG arrays. The proposed detection method identified artificially contaminated channels of HD-EMG for signal-to-noise ratio (SNR) levels 0 dB and lower with ≥99.9% precision and ≥97.6% recall. The interpolation-based detection method had the best overall performance compared with two other rule-based methods that used the root mean square (RMS) and normalized mutual information (NMI) to detect poor-quality channels in HD-EMG data. Unlike other detection methods, the interpolation-based method evaluated channel quality in a localized context in the HD-EMG array. For a single poor-quality channel with an SNR of 0 dB, the F1 scores for the interpolation-based, RMS, and NMI methods were 99.1%, 39.7%, and 75.9%, respectively. The interpolation-based method was also the most effective detection method for identifying poor channels in samples of real HD-EMG data. F1 scores for the detection of poor-quality channels in real data for the interpolation-based, RMS, and NMI methods were 96.4%, 64.5%, and 50.0%, respectively. Following the detection of poor-quality channels, 2D spline interpolation was used to successfully reconstruct these channels. Reconstruction of known target channels had a percent residual difference (PRD) of 15.5 ± 12.1%. The proposed interpolation-based method is an effective approach for the detection and reconstruction of poor-quality channels in HD-EMG.

Securing UAV Flying Base Station for Mobile Networking: A Review

A flying base station based on an unmanned aerial vehicle (UAV) uses its mobility to extend its connectivity coverage and improve its communication channel quality to achieve a greater communication rate and latency performances. While UAV flying base stations have been used in emergency events in 5G networking (sporadic and temporary), their use will significantly increase in 6G networking, as 6G expects reliable connectivity even in rural regions and requires high-performance communication channels and line-of-sight channels for millimeter wave (mmWave) communications. Securing the integrity and availability of the base station operations is critical because of the users’ increasing reliance on the connectivity provided by the base stations, e.g., the mobile user loses connectivity if the base station operation gets disrupted. This paper identifies the security issues and research gaps of flying base stations, focusing on their unique properties, while building on the existing research in wireless communications for stationary ground base stations and embedded control for UAV drones. More specifically, the flying base station’s user-dependent positioning, its battery-constrained power, and the dynamic and distributed operations cause vulnerabilities that are distinct from those in 5G and previous-generation mobile networking with stationary ground base stations. This paper reviews the relevant security research from the perspectives of communications (mobile computing, 5G networking, and distributed computing) and embedded/control systems (UAV vehicular positioning and battery control) and then identifies the security gaps and new issues emerging for flying base stations. Through this review paper, we inform readers of flying base station research, development, and standardization for future mobile and 6G networking.

Flexible Rate-Splitting Multiple Access With Finite Blocklength

Rate-splitting multiple access (RSMA) is a promising multiple access (MA) technique. It employs rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receiver. Most of the existing works on RSMA assume that all users use SIC to decode the common stream and the blocklength is infinite. The first assumption causes the data rate of the common stream to be limited by the user with the worst channel quality. The second assumption may lead to suboptimal performance in practical systems with finite blocklength. In this paper, we propose a flexible RSMA scheme, which allows the system to decide whether a user should use SIC to decode the common stream or not. We consider the effective throughput as the performance metric, which incorporates the data rate as well as the error performance of RSMA with finite blocklength. We first derive the effective throughput expression and then formulate an effective throughput maximization problem by jointly optimizing the beamforming vectors, transmission data rates, and RS-user selection. We develop an optimal algorithm as well as a low-complexity algorithm for beamforming design. We derive a semi-closed-form solution of the optimal data rates and propose an efficient algorithm for the RS-user selection. Numerical results demonstrate that the proposed algorithm obtains a higher effective throughput than space division multiple access (SDMA), non-orthogonal multiple access (NOMA), and two other RSMA schemes.

Intelligent Reflecting Surface-Aided Full-Duplex Covert Communications: Information Freshness Optimization

This work investigates the covert information freshness in intelligent reflecting surface (IRS)-aided communications, where a public full-duplex user (Alice) and a private full-duplex user (Bob) exchange information in the presence of a watchful warden (Willie). In particular, with the help of Alice’s undisguised signal transmission, Bob can establish covert communications such that his transmission can be shielded from Willie. Considering both the non-retransmission protocol and the automatic repeat-request (ARQ) protocol for Bob’s transmission, we study the resource allocation design. By exploiting the channel statistics, the joint design of active beamforming at Alice and Bob, the passive beamforming at the IRS, and the packet length of the confidential data packet is formulated as a nonconvex optimization problem which minimizes the age of information (AoI) at Alice for the two considered protocols taking into account the quality of service in terms of the maximum tolerable AoI at Bob and communication covertness. To circumvent the non-convexity of the design problem, we propose alternating optimization algorithms to find effective solutions. Numerical results demonstrate the superiority of our proposed optimization algorithms over various benchmarks and unveil the decrease of the optimized packet length with the improved covert channel quality.

Artificial Intelligence Empowered Traffic Control for Internet of Things with Mobile Edge Computing

Mobile edge computing (MEC) is one of the efficient technologies to provide satisfying quality of experience (QoE) for emerging computation-intensive applications in internet of things (IoT). However, some new challenges will be encountered when MEC is applied in a large-scale IoT with massive access devices or heavy traffic loads such as load balancing and traffic offloading. Aiming at the solution of these problems, this paper proposes a learning-based traffic control architecture for IoT with MEC. Moreover, a deep-learning-based load balancing framework is developed to control user association in IoT. The user association is determined at each IoT access points (IAP) by the deep neural network (DNN), which is the duplication of the well-trained DNN with the global network information. In addition, we propose a reinforcement-learning-based partial traffic offloading scheme to reduce the traffic origination. The IoT devices are able to independently decide its offloading radio according to the channel quality information, service requirement, and workload of the IAP. Simulation results indicate that the proposed deep-learning-based load balancing scheme is able to achieve uniform traffic distribution, and meanwhile our partial offloading policy can significantly reduce the network traffic.

Secrecy Capacity Enhancement in Active IRS-Assisted UAV Communication System.

As a new technology for reconstructing communication environments, intelligent reflecting surfaces (IRSs) can be applied to UAV communication systems. However, some challenges exist in IRS-assisted UAV communication system design, such as physical layer security issues, IRS design, and power consumption issues owing to the limitation of the hardware. Therefore, a secrecy capacity optimization scheme for an active IRS-assisted unmanned aerial vehicle (UAV) communication system is proposed to solve multi-user security issues. In particular, controllable power amplifiers are integrated into reflecting units to solve the problem of blocked links, and the UAV can dynamically select the served user according to the channel quality. In order to maximize the system average achievable secrecy capacity and ensure the power constraints of the UAV and active IRS, user scheduling, UAV trajectory, beamforming vector, and reflection matrix are jointly optimized, and the block coordinate descent (BCD) algorithm is applied to solve this non-convex problem. Simulation results show that the active IRS-assisted UAV communication scheme can significantly weaken the "multiplicative fading" effect and enhance the system secrecy capacity by 55.4% and 11.9% compared with the schemes with passive IRS and without optimal trajectory, respectively.

Resource allocation scheme for eMBB and uRLLC coexistence in 6G networks

5G technology is intended to support three promising services with heterogeneous requirements: Ultra-Reliable and Low Latency Communication (uRLLC), enhanced Mobile Broadband (eMBB), and massive Machine Type Communication (mMTC). 6G is required to support even more challenging scenarios, including the presence of a large number of uRLLC devices, under the massive uRLLC (mURLLC) use case scenario. The presence of these services on the same network creates a challenging task of resource allocation to meet their diverse requirements. Given the critical nature of uRLLC applications, uRLLC traffic will always have the highest priority which causes a negative impact on the performance of other services. In this paper, the problem of uRLLC/eMBB resource allocation is investigated. An optimal resource allocation scheme is proposed with two scenarios including a guaranteed fairness level and minimum data rate among eMBB users. In addition, a knapsack-inspired punctured resource allocation algorithm is proposed where the users’ channel qualities of both services are considered at each time slot leading to the most suitable Resource Block (RB) selection for puncturing in a way that minimizes the negative impact on eMBB performance. The proposed solution was compared with three puncturing baseline reference algorithms and the performance was evaluated in terms of eMBB Sum throughput and Fairness level. The simulation results show that the proposed algorithm outperforms the above-mentioned reference algorithms in all evaluation metrics and is proved to be comparable to the optimal solution given its low complexity.

Fractional mayfly optimization algorithm‐based Infrastructure‐to‐Vehicle and Vehicle‐to‐Vehicle scheduling for service message transmission in IoV‐fog

SummaryFor the rising number of vehicles, and the advancement of communication and computation technologies, the perception of Internet of Vehicles (IoV) is introduced. It is utilized to capture vehicle‐based information that includes road description, road congestion, vehicle speed, and location. However, this information is important, and it showed more benefits in different ways, like route selection and message dissemination. IoV is the self‐structured network composed with vehicles lies in the road and the road side units (RSUs). It offers Infrastructure‐to‐Vehicle (I2V), as well as Vehicle‐to‐Vehicle (V2V) data transmission mechanism for transmitting service messages. To reliably broadcast the service information to the intended recipient in the IoV network still faced issues. Hence, an efficient service message transmission protocol is developed using the proposed fractional mayfly optimization algorithm (FMA) for selecting the relay vehicle and cooperative vehicle for transmitting service messages to the destination vehicle from RSU through the process of I2V and V2V scheduling. The RSU selects the relay vehicle for every service message using the proposed algorithm and allocates the cooperative vehicle by RSU for scheduling V2V transmission. The simulation results showed that the proposed scheduling method obtains the best channel quality indicator (CQI), delay, distance, packet delivery ratio (PDR), and throughput value of 0.92 for 150 vehicles and 0.005891, 6.060731, 83.45%, and 171.50 Mbps for 100 vehicles.

Distributed Design of Wireless Powered Fog Computing Networks With Binary Computation Offloading

This paper investigates a multi-user wireless powered fog computing (FC) network, where multiple energy-limited wireless sensor devices (WSDs) first harvest energy from a nearby hybrid access point (HAP), and then compute their tasks locally (i.e., the local computing (LC) mode) or offload the tasks to the HAP (i.e., the FC mode) via a binary offloading policy. In order to pursue the green computing network design, an optimization problem is formulated to minimize the transmit power at the HAP by jointly optimizing the time allocation ratio and the computing mode selection vector, under the energy causality constraints and the WSDs’ computing rate requirements constraints. To efficiently solve the formulated non-convex problem in a distributed manner, it is first transformed into an approximate form, and then an alternating direction method of multipliers (ADMM)-based algorithm is designed to solve the transformed problem, based on which the successive convex approximation (SCA) is adopted to improve the approximating precision in an iterative way. With the proposed ADMM-based distributed algorithm, each WSD is able to optimize its computing mode and offloading time with local channel state information (CSI), which thus is more suitable for large-scale networks. For comparison, a channel-sorting-based (CSB) centralized algorithm with global CSI is also presented, and the computational complexities of the proposed ADMM-based algorithm and the CSB algorithm are analyzed. Simulation results show that the proposed distributed algorithm achieves a comparable performance with the CSB centralized algorithm and the exhaustive search method. It is also observed that to minimize the transmit power at the HAP, the WSDs with the better channel quality are inclined to select the LC mode, which is much different from traditional sum-computation-rate maximization design.

Scheduling and Aggregation Design for Asynchronous Federated Learning Over Wireless Networks

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines on-device training and server-based aggregation to train a common ML model among distributed agents. In this work, we propose an asynchronous FL design with periodic aggregation to tackle the straggler issue in FL systems. Considering limited wireless communication resources, we investigate the effect of different scheduling policies and aggregation designs on the convergence performance. Driven by the importance of reducing the bias and variance of the aggregated model updates, we propose a scheduling policy that jointly considers the channel quality and training data representation of user devices. The effectiveness of our channel-aware data-importance-based scheduling policy, compared with state-of-the-art methods proposed for synchronous FL, is validated through simulations. Moreover, we show that an ``age-aware'' aggregation weighting design can significantly improve the learning performance in an asynchronous FL setting.

Joint Convexity of Error Probability in Blocklength and Transmit Power in the Finite Blocklength Regime

To support ultra-reliable and low-latency services for mission-critical applications, transmissions are usually carried via short blocklength codes, i.e., in the so-called finite blocklength (FBL) regime. Different from the infinite blocklength regime where transmissions are assumed to be arbitrarily reliable at the Shannon’s capacity, the reliability and capacity performances of an FBL transmission are impacted by the coding blocklength. The relationship among reliability, coding rate, blocklength and channel quality has recently been characterized in the literature, considering the FBL performance model. In this paper, we follow this model, and prove the joint convexity of the FBL error probability with respect to blocklength and transmit power within a region of interest, as a key enabler for designing systems to achieve globally optimal performance levels. Moreover, we apply the joint convexity to general use cases and efficiently solve the joint optimization problem in the setting with multiple users. We also extend the applicability of the proposed approach by proving that the joint convexity still holds in fading channels, as well as in relaying networks. Via simulations, we validate our analytical results and demonstrate the advantage of leveraging the joint convexity compared to other commonly-applied approaches.

Wireless Distributed Learning: A New Hybrid Split and Federated Learning Approach

Cellular-connected unmanned aerial vehicle (UAV) with flexible deployment is foreseen to be a major part of the sixth generation (6G) networks. The UAVs connected to the base station (BS), as aerial users (UEs), could exploit machine learning (ML) algorithms to provide a wide range of advanced applications, like object detection and video tracking. Conventionally, the ML model training is performed at the BS, known as centralized learning (CL), which causes high communication overhead due to the transmission of large datasets, and potential concerns about UE privacy. To address this, distributed learning algorithms, including federated learning (FL) and split learning (SL), were proposed to train the ML models in a distributed manner via only sharing model parameters. FL requires higher computational resource on the UE side than SL, while SL has larger communication overhead when the local dataset is large. To effectively train an ML model considering the diversity of UEs with different computational capabilities and channel conditions, we first propose a novel distributed learning architecture, a hybrid split and federated learning (HSFL) algorithm by reaping the parallel model training mechanism of FL and the model splitting structure of SL. We then provide its convergence analysis under non-independent and identically distributed (non-IID) data with random UE selection scheme. By conducting experiments on training two ML models, Net and AlexNet, in wireless UAV networks, our results demonstrate that the HSFL algorithm achieves higher learning accuracy than FL and less communication overhead than SL under IID and non-IID data, and the learning accuracy of HSFL algorithm increases with the increasing number of the split training UEs. We further propose a Multi-Arm Bandit (MAB) based best channel (BC) and best 2-norm (BN2) (MAB-BC-BN2) UE selection scheme to select the UEs with better wireless channel quality and larger local model updates for model training in each round. Numerical results demonstrate it achieves higher learning accuracy than BC, MAB-BC and MAB-BN2 UE selection scheme under non-IID, Dirichlet-nonIID and Dirichlet-Imbalanced data.

How to Define the Propagation Environment Semantics and Its Application in Scatterer-Based Beam Prediction

In view of the propagation environment directly determining the channel properties, the applications can also be solved with the aid of environmental information. Inspired by task-oriented semantic communication and machine learning (ML) powered environment-channel mapping methods, we aim to provide a new view of the environment from the semantic level. This letter defines propagation environment semantics (PES) as a limited set of propagation environment semantic symbols (PESS). The PESS is extracted, which is oriented to the application tasks with concerned channel properties as a foundation. For method validation, the PES-aided beam prediction (PESaBP) is investigated and implemented for non-line-of-sight (NLOS) scenarios. Environment features and graph representations are constructed for actions of channel quality evaluation and target scatterer detection with maximum power. It can obtain 0.92 and 0.9 precision, respectively, and save over 87% of the time cost.

Examining the mediating effect of multi-channel integration quality in the relationship with service quality, customer satisfaction and customer loyalty in the Saudi banking sector

PurposeSince banks do not sell tangible products, they rely heavily on customer interactions and retention, which requires service quality, customer satisfaction and customer loyalty. Banks must innovate and develop new services and expand customer engagement efforts beyond stores, kiosks, direct mail and websites to include social media, mobile applications and location-based services in order to meet their customers’ growing demands. A multi-channel strategy that integrates the offline and online presences of banks can increase quality, customer satisfaction and loyalty. This paper aimed to use a service quality scale to: (1) examine the association between service quality and customer satisfaction; (2) examine the association between customer satisfaction and customer loyalty; (3) examine the indirect association between service quality and customer loyalty through customer satisfaction; and (4) examine the mediation effect of multi-channel integration quality in the relationships between service quality, customer satisfaction and customer loyalty.Design/methodology/approachThe data was obtained from banks in Saudi Arabia. The analysis was based on an online survey of 265 Saudi bank customers. The multi-channel integration quality model and Statistical Package for the Social Sciences (SPSS) were used to test the proposed hypothesis and conduct the analysis.FindingsThe results found that there was a statistically significant link between service quality and customer satisfaction in the Saudi banking industry. Service quality did not directly affect customer loyalty. When multi-channel integration quality was moderate to high, service quality affected customer loyalty through customer satisfaction. For service quality and customer loyalty in the Saudi banking sector to be achieved, customers must be satisfied, but also the bank’s brand must manage the quality of integration channels provided to them with care, and thus branding plays a key role in achieving customer loyalty in the Saudi banking sector.Originality/valueThe academic community has provided little evidence to support how the relationships between constructs such as service quality, customer satisfaction, customer loyalty and multi-channel integration quality apply to the Saudi banking sector. A conceptual framework was proposed to show how these constructs affect the Saudi banking sector. An empirical study was conducted to see how the framework held up in banking settings. The conceptual framework serves to advance the fields of business and management and banking and their respected literature, as well as advance the understanding of multi-channel integration in boosting customer satisfaction and loyalty through high service quality in the Saudi banking sector.

A Qualitative Study on Bancassurance Products and Its Implementation for Efficient Customer Experience in Pakistan

Distribution of life insurance product in Pakistan is challenging for insurers to facilitate customers. Bancassurance products develop a bridge of life insurance product distribution via bank to facilitate customers. The implementation of Bancassurance and its players, including banks, the distribution channel, insurance services, financial services, and service quality, are discussed in this research. Because of their relationship and trust with the bank, particularly the local bank branch, customers prefer Bancassurance. Digitalization is another crucial factor that can significantly improve this system. Adopting modern technology and digitalization can facilitate consumer accessibility and increase this product's sales. A qualitative methodology has adopted to conduct this research. Thematic analysis has applied as a tool of research in order to transcribe interviews conducted from 10 participants. Purposive sampling was used to collect data in order to obtain detailed information from interviewees. Bancassurance is a tactical arrangement that offers consumers insurance services in addition to numerous other financial services. The themes are generated from the factors explored during interview. Since it has a competitive edge over others, bancassurance has a wider scope than other channels. In a partnership, banks and insurance companies market each other's insurance products to their respective customers.

A Qualitative Study on Bancassurance Products and Its Implementation for Efficient Customer Experience in Pakistan

Distribution of life insurance product in Pakistan is challenging for insurers to facilitate customers. Bancassurance products develop a bridge of life insurance product distribution via bank to facilitate customers. The implementation of Bancassurance and its players, including banks, the distribution channel, insurance services, financial services, and service quality, are discussed in this research. Because of their relationship and trust with the bank, particularly the local bank branch, customers prefer Bancassurance. Digitalization is another crucial factor that can significantly improve this system. Adopting modern technology and digitalization can facilitate consumer accessibility and increase this product's sales. A qualitative methodology has adopted to conduct this research. Thematic analysis has applied as a tool of research in order to transcribe interviews conducted from 10 participants. Purposive sampling was used to collect data in order to obtain detailed information from interviewees. Bancassurance is a tactical arrangement that offers consumers insurance services in addition to numerous other financial services. The themes are generated from the factors explored during interview. Since it has a competitive edge over others, bancassurance has a wider scope than other channels. In a partnership, banks and insurance companies market each other's insurance products to their respective customers.

Reliable and energy-efficient UAV-assisted air-to-ground transmission: Design, modeling and analysis

In this paper, a novel reliable and energy-efficient transmission scheme is proposed for unmanned aerial vehicle (UAV) assisted air-to-ground (A2G) communication. The scheme is tailored to handle burst errors in transmission caused by network collision, switching or interference. Application Layer Forward Error Correction (AL-FEC) mechanism is proposed to enhance the reliability of the A2G communication. By avoiding retransmissions, energy efficiency can be guaranteed. Based on Gilbert-Elliott channel model, we perform a detailed mathematical analysis on the performance of the proposed AL-FEC scheme. The packet delivery ratio and energy efficiency are studied and validated by comprehensive simulations. An application layer Automatic Repeat reQuest (AL-ARQ) protocol based on selective retransmission is used as a baseline scheme and compared with the proposed scheme. It is shown that the proposed AL-FEC scheme can significantly reduce the energy consumption and ensure the same or even higher application packet delivery ratio compared with the baseline scheme. In addition, the proposed scheme is especially preferable when the channel quality is poor. These specific findings can provide guidance for practical applications relying on UAV-assisted A2G communication, which require reliable and energy-efficient UAV-to-ground transmission in harsh wireless conditions.

Quality of Stormwater Infrastructure Systems in Vulnerable Communities: Three Case Studies from Texas.

A properly functioning local stormwater drainage system is essential for mitigating flood risks. This study evaluates the quality of roadside drainage channels in three underserved communities in Texas: the Sunnyside neighborhood in Houston (Harris County), a neighborhood in the City of Rockport (Aransas County), and the Hoehn colonia (Hidalgo County). These communities have a history of flooding, are highly socially vulnerable, and rely on roadside ditches as their principal stormwater drainage system for runoff control. Mobile lidar (Light Detection and Ranging) measurements were collected for 6.09 miles of roadside channels in these communities. The raw lidar measurements were processed to evaluate drainage conditions based on the channel's geometric properties, hydraulic capacity, and level of service. The assessment results are linked to a Geographic Information System (GIS) tool for enhanced visualization. Finally, the paper provides insights regarding the quality of stormwater infrastructure in the study communities and discusses their practical implications.