Mobile Scenarios Research Articles

Regulation under administrative guidance: The case of China's forcing interoperability on digital platforms

Whether policy-makers should mandate that various digital platforms be made interoperable with others and how to achieve it has sparked much controversy, while there is less discussion on the implementation and effectiveness of interoperability on digital platforms in specific contexts and given settings. This article examines the practice and outcome of Chinese policy-makers and regulators in promoting interoperability on digital platforms in the field of social media and mobile payment scenarios. China's practice shows that mandatory interoperability of platforms brings very limited convenience to consumers, and it hardly facilitates entry of small operators but rather facilitates expansion of other competitive platforms. The interoperability promoted by administrative forces unfairly provides a competitive advantage to state-owned enterprises and reduces the willingness of existing platforms to innovate and expand. These results corroborate international criticisms and concerns about mandatory interoperability, reflecting the very limited effect of mandatory interoperability on cracking the network blocking effect of internet platforms.

On the Analysis and Optimization of Fast Conditional Handover With Hand Blockage for Mobility

Although frequency range 2 (FR2) systems are an essential part of 5G-Advanced and future 3GPP releases, the mobility performance of multi-panel user equipment (MPUE) with hand blockage is still an area open for research and standardization. In this article, a comprehensive study on the mobility performance of MPUE with hand blockage is performed for conditional handover (CHO) and its potential enhancement denoted by fast conditional handover (FCHO). In contrast to CHO, in FCHO the MPUE can reuse earlier target cell preparations after each handover to autonomously execute subsequent handovers. This saves both the signaling overhead associated with the reconfiguration and re-preparation of target cells after each handover and reduces mobility failures. Results have shown that FCHO offers considerable mobility performance gains as compared to CHO for different hand blockage cases that are dependent on the hand position around the MPUE. For the worst-case hand blockage scenario, it is seen that mobility failures reduce by 10.5% and 19.3% for the 60 km/h and 120 km/h mobility scenarios, respectively. This gain comes at the expense of reserving the handover resources of an MPUE for a longer time given that the target cell configurations are not necessarily released after each handover. In this article, the longer resource reservation problem in FCHO is analysed and three different resource reservation optimization techniques are introduced. Results have shown that these optimization techniques not only reduce the resource reservation time but also significantly reduce the signaling overhead at the possible expense of a tolerable degradation in mobility performance.

Assessing the impacts of shared autonomous mobility with M3ICA: A multi-impact, multi-actor, multi-criteria approach

The impacts of connected cooperative autonomous vehicles on society can be significant. We propose a holistic framework that brings together stakeholder assessment with quantitative evaluation of impacts. In this paper, we apply the first part of the framework, and present our findings from a stakeholder workshop within the context of the H2020 SHOW project. 6 groups of stakeholders in the Linköping pilot site participated in a MAMCA workshop and evaluate scenarios of autonomous mobility. Performance scores of each scenario were calculated using the scores and weights assigned to each criteria by their level of importance for each actor group. The findings show a general positive perception of autonomous mobility, with public actors preferring autonomous shared modes, while users and mobility service providers found modes that supported flexible transport to achieve their objectives better.

Identification of a Printed Anti-Counterfeiting Code Based on Feature Guidance Double Pool Attention Networks

The authenticity identification of anti-counterfeiting codes based on mobile phone platforms is affected by lighting environment, photographing habits, camera resolution and other factors, resulting in poor collection quality of anti-counterfeiting codes and weak differentiation of anti-counterfeiting codes for high-quality counterfeits. Developing an anti-counterfeiting code authentication algorithm based on mobile phones is of great commercial value. Although the existing algorithms developed based on special equipment can effectively identify forged anti-counterfeiting codes, the anti-counterfeiting code identification scheme based on mobile phones is still in its infancy. To address the small differences in texture features, low response speed and excessively large deep learning models used in mobile phone anti-counterfeiting and identification scenarios, we propose a feature-guided double pool attention network (FG-DPANet) to solve the reprinting forgery problem of printing anti-counterfeiting codes. To address the slight differences in texture features in high-quality reprinted anti-counterfeiting codes, we propose a feature guidance algorithm that creatively combines the texture features and the inherent noise feature of the scanner and printer introduced in the reprinting process to identify anti-counterfeiting code authenticity. The introduction of noise features effectively makes up for the small texture difference of high-quality anti-counterfeiting codes. The double pool attention network (DPANet) is a lightweight double pool attention residual network. Under the condition of ensuring detection accuracy, DPANet can simplify the network structure as much as possible, improve the network reasoning speed, and run better on mobile devices with low computing power. We conducted a series of experiments to evaluate the FG-DPANet proposed in this paper. Experimental results show that the proposed FG-DPANet can resist high-quality and small-size anti-counterfeiting code reprint forgery. By comparing with the existing algorithm based on texture, it is shown that the proposed method has a higher authentication accuracy. Last but not least, the proposed scheme has been evaluated in the anti-counterfeiting code blurring scene, and the results show that our proposed method can well resist slight blurring of anti-counterfeiting images.

Design, Implementation, and Practical Evaluation of a Voice Recognition Based IoT Home Automation System for Low-Resource Languages and Resource-Constrained Edge IoT Devices: A System for Galician and Mobile Opportunistic Scenarios

Systems with voice control are an attractive option for increasing technological integration, not only for people with little knowledge on technology or constrained Internet access, but also for people with certain disabilities. In addition, devices based on Alexa or Google Home provide an interesting alternative for interacting with Internet of Things (IoT) devices, but they usually rely on an Internet connection to a cloud server for their full operation. Furthermore, many voice-recognition systems are only available in a limited number of languages, which tend to be those with the highest number of speakers, thus excluding minority-language speakers. To address the previously mentioned issues, this article presents a solution based on Edge Computing and voice commands that carries out offline voice processing and that is able to interact with IoT-based systems. The proposed system performs local speech inference, providing a communication interface with IoT devices in a Bluetooth mesh, all in a fast way and without the need for an Internet connection. In addition, the proposed solution can be adapted easily for voice recognition of languages with few resources. Such a feature is demonstrated with the Galician language, which is spoken by less than 3 million people worldwide. In particular, different Automatic Speech Recognition (ASR) models based on three of the most popular ASR development frameworks (wav2vec2, DistilHubert, Whisper) were developed to transcribe short speech and to translate it into IoT commands that perform specific home-automation actions. Such models were fine-tuned for Galician with a corpus of approximately 20 hours and were evaluated in static and mobile opportunistic scenarios in terms of accuracy, energy consumption and latency on an embedded platform (that acts as an edge device) and on a cloud server. The obtained results show that inference is performed in less than 2 seconds on a Raspberry Pi 4 for the two smallest models and in less than 500 ms on a high-end Android smartphone when processing all data locally with CPU-only inference (i.e., without hardware acceleration or external processing). The results of the transcriptions are accurate enough to be able to use simple text distance algorithms to detect keywords in the speech and perform commands on IoT devices. In particular, a maximum success rate of 92% was achieved for detecting the indicated commands when using models optimized for being executed on embedded devices. For selected home scenarios, command actions were sent via Bluetooth with average response times of up to 113 ms.

DQN-Based Adaptive MCS and SDM for 5G Massive MIMO-OFDM Downlink

In this letter, we propose a deep Q-network (DQN)-based joint adaptive scheduling algorithm of modulation and coding scheme (MCS) and space division multiplexing (SDM) in 5G massive MIMO-OFDM, that enables the next-generation NodeB (gNB) to choose optimal MCS and SDM for downlink scheduling that maximizes the spectral efficiency (SE) and throughput while in a low block error rate (BLER). The simulation results show that in the typical high-speed mobile and uplink interference scenarios, the proposed DQN-based algorithm has a 10% to 40% improvement in SE and a 10% to 20% improvement in throughput compared to representative algorithms, and can effectively compensate for the channel state information (CSI) feedback delay.

OTFS-based efficient handover authentication scheme with privacy-preserving for high mobility scenarios

Handover authentication in high mobility scenarios is characterized by frequent and short-term parallel execution. Moreover, the penetration loss and Doppler frequency shift caused by high speed also lead to the deterioration of network link quality. Therefore, high mobility scenarios require handover schemes with less handover overhead. However, some existing schemes that meet this requirement cannot provide strong security guarantees, while some schemes that can provide strong security guarantees have large handover overheads. To solve this dilemma, we propose a privacy-preserving handover authentication scheme that can provide strong security guarantees with less computational cost. Based on Orthogonal Time Frequency Space (OTFS) link and Key Encapsulation Mechanism (KEM), we establish the shared key between protocol entities in the initial authentication phase, thereby reducing the overhead in the handover phase. Our proposed scheme can achieve mutual authentication and key agreement among the user equipment, relay node, and authentication server. We demonstrate that our proposed scheme can achieve user anonymity, unlinkability, perfect forward secrecy, and resistance to various attacks through security analysis including the Tamarin. The performance evaluation results show that our scheme has a small computational cost compared with other schemes and can also provide a strong guarantee of security properties.

OTFS modulation and PAPR reduction for IoT-railways

The internet of things (IoT) has been widely considered to be integrated with high-speed railways to improve safety and service. It is important to achieve reliable communication in IoT for railways (IoT-R) under high mobility scenarios and strict energy constraints. Orthogonal time frequency space (OTFS) modulation is a two-dimensional modulation technique that has the potential to overcome the challenges in high Doppler environments. In addition, OTFS can have lower peak-to-average power ratio (PAPR) compared to orthogonal frequency division multiplexing, which is especially important for the application of IoT-R. Therefore, OTFS modulation for IoT-R is investigated in this paper. In order to decrease PAPR of OTFS and promote the application of OTFS modulation in IoT-R, the peak windowing technique is used in this paper. This technique can reduce the PAPR of OTFS by reducing the peak power and does not require multiple iterations. The impacts of different window functions, window sizes and clipping levels on PAPR and bit error rate of OTFS are simulated and discussed. The simulation results show that the peak windowing technique can efficiently reduce the PAPR of OTFS for IoT-R.

Iterative receiver for orthogonal time frequency space with index modulation via structured prior-based hybrid belief and expectation propagation

Orthogonal Time Frequency Space (OTFS) signaling with index modulation (IM) is a promising transmission scheme characterized by high transmission efficiency for high mobility scenarios. In this paper, we study the receiver for coded OTFS-IM system. First, we construct the corresponding factor graph, on which the structured prior incorporating activation pattern constraint and channel coding is devised. Then we develop a iterative receiver via structured prior-based hybrid belief propagation (BP) and expectation propagation (EP) algorithm, named as Str-BP-EP, for the coded OTFS-IM system. To reduce the computational complexity of discrete distribution introduced by structured prior, Gaussian approximation conducted by EP is adopted. To further reduce the complexity, we derive two variations of the proposed algorithm by using some approximations. Simulation results validate the superior performance of the proposed algorithm.

SPROUT case studies: Assessing future mobility scenarios by following a city-led consequence analysis framework - the case of Budapest and Tel Aviv city

The rapid changes in the urban mobility environment together with the on-going and future technological trends constitute a great challenge for policy makers to assess the expected impacts. This study aims to confront these issues through the development and application of a framework for anticipating the effect of the transition taking place in a city's urban mobility environment. The framework is used for collecting the required data from the cities, followed by the analysis of the results and the identification of the impacts of any city-specific future mobility scenario. In this paper, the cases of Budapest and Tel Aviv are chosen to be presented as two cities with similar size, population and level of smartness. The cross-comparison of the two cases shows the consequences foreseen, as for example an increased level of congested streets, while also of the Air Quality index.

Machine Learning-Based Integrated Wireless Sensing and Positioning for Cellular Network

Integrated sensing and communication (ISAC) is one of the most promising technical directions in B5G/6G era. Nevertheless, it is challenging for network operators to provide proper human-centric services (HCSs), appropriately with the explosive growth of mobile terminals in constantly changing scenarios. Scenario sensing is the foundation to provide users with intelligent context-aware services, while the combination of machine learning (ML) and automatic data collection enables network operators to become more flexible in decision-making and planning. In this article, an ML-based integrated indoor/outdoor (IO) sensing and positioning framework is proposed to improve the potential of ML in the context of cellular network service management. First, large amounts of measurement reports (MRs) are collected over Layer 3 at user equipment (UE) and evolved NodeBs (eNBs) in urban areas, including both indoor and outdoor samples to simulate the minimization of drive test (MDT). Then, a random-forest-based IO classifier is implemented to sense the mobile scenarios and filter the positioning fingerprints. Subsequently, the weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -nearest neighbor (WKNN)-based Enhanced Cell ID (ECID) merging with the MR method is used for cellular positioning. The performance evaluation has shown that the positioning error of the MRs after denoising is effectively reduced compared with the conventional fingerprint-based positioning. Specifically, under the 67% standard defined by Federal Communications Commission (FCC), the resulting positioning error is about 4% lower than the case without preprocessing. In addition, comparative experiments are conducted to discuss the impact of different <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> values at different data sizes of fingerprints. Moreover, the further data analysis shows that MRs collected in the mild indoor (MI) scenarios have a positive effect on the overall positioning error, which indicates that refined contextualizations contribute to the cellular positioning for HCS upgrade.

360-Degree Holistic Systems Modelling for ‘Innovative, Integrated and Sustainable’ Mobility Scenario Analysis and Policy Formulation

360-Degree Holistic Systems Modelling for ‘Innovative, Integrated and Sustainable’ Mobility Scenario Analysis and Policy Formulation

Data-driven deep learning for OTFS detection

Recently, orthogonal time frequency space (OTFS) was presented to alleviate severe Doppler effects in high mobility scenarios. Most of the current OTFS detection schemes rely on perfect channel state information (CSI). However, in real-life systems, the parameters of channels will constantly change, which are often difficult to capture and describe. In this paper, we summarize the existing research on OTFS detection based on data-driven deep learning (DL) and propose three new network structures. The presented three networks include a residual network (ResNet), a dense network (DenseNet), and a residual dense network (RDN) for OTFS detection. The detection schemes based on data-driven paradigms do not require a model that is easy to handle mathematically. Meanwhile, compared with the existing fully connected-deep neural network (FC-DNN) and standard convolutional neural network (CNN), these three new networks can alleviate the problems of gradient explosion and gradient disappearance. Through simulation, it is proved that RDN has the best performance among the three proposed schemes due to the combination of shallow and deep features. RDN can solve the issue of performance loss caused by the traditional network not fully utilizing all the hierarchical information.

Pandemic impacts on public transport safety and stress perceptions in Nordic cities.

COVID-19 has brought severe disruption and demand suppression to mobility, especially to public transport (PT). A key challenge now is to restore trust that PT is safe again. This paper investigates pandemic impacts on PT safety and stress perceptions in three Nordic cities, drawing on 2018 and 2020 survey data analysed in structural equation models. While finding modest pandemic effects on safety and stress perceptions overall, strong heterogeneities exist across gender, age and geographic categories. Women perceive less PT safety and more stress, especially during the pandemic. Older adults reduced PT more during the pandemicandperceived no stress reduction like younger adults. Stockholm travellers feel less safe and more stressed than in Oslo and Bergen, whilstpandemic PTuseandperceived safety reductions are least inBergen. The paper discusses thelong-term implicationsfor theory and policy across multiple mobility scenarios accounting for modal change and travel demand uncertainties.

3D Gaze Estimation Using RGB-IR Cameras.

In this paper, we present a framework for 3D gaze estimation intended to identify the user's focus of attention in a corneal imaging system. The framework uses a headset that consists of three cameras, a scene camera and two eye cameras: an IR camera and an RGB camera. The IR camera is used to continuously and reliably track the pupil and the RGB camera is used to acquire corneal images of the same eye. Deep learning algorithms are trained to detect the pupil in IR and RGB images and to compute a per user 3D model of the eye in real time. Once the 3D model is built, the 3D gaze direction is computed starting from the eyeball center and passing through the pupil center to the outside world. This model can also be used to transform the pupil position detected in the IR image into its corresponding position in the RGB image and to detect the gaze direction in the corneal image. This technique circumvents the problem of pupil detection in RGB images, which is especially difficult and unreliable when the scene is reflected in the corneal images. In our approach, the auto-calibration process is transparent and unobtrusive. Users do not have to be instructed to look at specific objects to calibrate the eye tracker. They need only to act and gaze normally. The framework was evaluated in a user study in realistic settings and the results are promising. It achieved a very low 3D gaze error (2.12°) and very high accuracy in acquiring corneal images (intersection over union-IoU = 0.71). The framework may be used in a variety of real-world mobile scenarios (indoors, indoors near windows and outdoors) with high accuracy.

SDFnT-Based Parameter Estimation for OFDM Radar Systems with Intercarrier Interference

The orthogonal frequency division multiplexing (OFDM) radar suffers from severe performance degradation in range-velocity estimation in high mobility scenarios. In this paper, a novel intercarrier interference (ICI)-free parameter estimation method for OFDM radar is proposed. By employing a scale discrete Fresnel transform (SDFnT), the OFDM radar signals are converted to the scale Fresnel domain, and the orthogonality of subcarriers can be recovered with the optimal scale factor. Furthermore, due to the compatibility of the SDFnT and the discrete Fourier Transform (DFT), the proposed method has low computational complexity and high feasibility for OFDM radar implementation. Simulation results show that the proposed SDFnT-based scheme effectively eliminates the ICI effect for single and multiple targets and achieves high accuracy delay-Doppler estimation for OFDM radar systems in circumstances of high velocity and low SNR with consistency and robustness.

QFD and SDE Methods Applied to Autonomous Minibus Redesign and an Innovative Mobile Charging System (MBS)

Urban mobility scenarios are constantly evolving, and today’s solutions may not be adequate in the future. Through innovative analysis and design methods encapsulated by the IDeS methodology, it is possible to plausibly hypothesize a number of key scenarios to be analyzed, for which vehicles can be designed in order to solve the main problems. Scenarios such as the steady growth in public mobility, based on the sharing of electric mini-buses at the expense of the privatization of the means of transport, lead to the gradual rethinking of citizens’ needs and the supporting infrastructure. Problems such as the lack of privacy of public vehicles, the efficiency of the infrastructure and recharging modes of e-buses, and autonomous driving are addressed here through methods such as QFD (quality function deployment) and SDE (stylistic design engineering), with the aim of outlining a proposal that, to date, is futuristic but is designed to be concrete and feasible within the next decade. These methodologies were applied to the design of a sustainable urban transport system consisting of an electric mini-bus, effected by rethinking the layout of the interior spaces in favor of areas enabling greater privacy and a mobile recharging system (MBS) capable of offering a new management strategy for the non-stop recharging phase. Through the use of an MBS, which functions as a mobile ‘energy bank’ module that is capable of autonomously reaching a mini-bus in need of recharging and extending its autonomy by connecting and recharging it, the proposed system can potentially be enabled to perform its required service during the day without any need to spend time making intermediate stops for the purpose of recharging.

Hybrid precoding design forMmWavemulti‐user massiveMIMOsystems under high mobility

AbstractStudying hybrid precoding in a high mobility scenario is challenging task. Because the channel changes quickly with time and becomes double selective fading in the spatial domain. As a result, there is a difficulty to achieve perfect channel state information or estimate the channel under high mobility. In this paper, an applicable hybrid precoding based on amplitude and phase excitation beams for millimeter wave (mmWave) massive multiple input multiple output (MIMO) systems is proposed under high speed railway. The proposed hybrid precoding uses the data from the locations of the mobile stations to build the amplitude and phase beam matrix. The proposed precoders and decoders are designed to maximize the mobile service. An optimization problem of mobile service maximization is formulated for designing hybrid precoders and decoders. To solve the optimization problem, a heuristic technique under power constraint is provided. The proposed hybrid precoding algorithm is compared with analog only beam steering algorithm, partially connected hybrid precoding algorithm and fully connected hybrid precoding algorithm. Results illustrated that the proposed precoding scheme outperforms the other precoding algorithms and handover success probability is enhanced under high mobility.

Use of agent-based modeling to model intermediate force capabilities in (counter)mobility crowd scenarios

In this paper, we use an agent-based model (ABM) to run (counter)mobility scenarios to explore which characteristics of intermediate force capabilities (IFC) are relevant to these, and how they can affect outcomes in gray zone conflicts. Using an ABM called Map-Aware Non-Uniform Automata (MANA), developed by the New Zealand Defense Technology Agency, we implemented two scenarios where the friendly forces’ mobility was limited by large groups of civilians. Then, we employed data farming and analytics methods to analyze the data and identify key parameters influencing the outcomes. The main parameters appeared to be the IFC Range, Power (a measure of the duration of the effect), and Crowd Density. Future research could include a wide range of mobility scenarios and possibly a more detailed IFC representation.

Spatio-Temporal Neural Network for Channel Prediction in Massive MIMO-OFDM Systems

In massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, a challenging problem is how to predict channel state information (CSI) (i.e., channel prediction) accurately in mobility scenarios. However, a practical obstacle is caused by CSI non-stationary and nonlinear dynamics in temporal domain. In this paper, we propose a spatio-temporal neural network (STNN) to achieve better performance by carefully taking into account the spatio-temporal characteristics of CSI. Specifically, STNN uses its encoder and decoder modules to capture the spatial correlation and temporal dependence of CSI. Further, the differencing-attention module is designed to deal with the non-stationary and nonlinear temporal dynamics and realize adaptive feature refinement for more accurate multi-step prediction. Additionally, an advanced training scheme is adopted to reduce the discrepancy between STNN training and testing. Evaluated on a realistic channel model with enhanced mobility and spherical waves, experimental results show that STNN can effectively improve the accuracy of prediction and perform well with respect to different signal to noise ratios (SNRs). Visualization and testing for unit root illustrate STNN is able to learn CSI time-varying patterns by alleviating series non-stationarity.