Dense Urban Scenario Research Articles

A geometry-based stochastic scattering channel model for V2V communications in dense urban street environments

In this paper, we propose a stochastic channel model in three-dimensional (3D) space for multiple input multiple output (MIMO) vehicle-to-vehicle (V2V) communications in dense urban environments. The movement of the mobile transmitter and mobile receiver results in the V2V channel model behave temporal non-stationarity. Therefore, the time-varying parameters of the propagation paths and angles are derived to characterize such property. Using this channel model, we investigate the propagation characteristics of V2V channels in terms of the road section and moving time/directions/speeds of the transmitter and receiver. Numerical results show that the theoretical results of the propagation characteristics of the V2V channel model are very close to those of the simulation ones, which show that the proposed channel model is suitable for depicting the V2V communications in dense urban scenarios.

A Novel Machine Learning Scheme for mmWave Path Loss Modeling for 5G Communications in Dense Urban Scenarios

Accurate and efficient path loss prediction in mmWave communication plays an important role in large-scale deployment of the mmWave-based 5G mobile communication systems. Existing methods often present limitations in accuracy and efficiency and fail to fulfill the requirements of cell planning, especially in dense urban environments. In this paper, we propose a novel training method called multi-way local attentive learning, which allows for learning from multiple perspectives on the same set of training samples with local attention paid to each subset of the entire dataset. The sample data set can be partitioned in various ways with respect to different attributes, such that a larger amount of knowledge can be extracted from the same data set. The proposed scheme outperforms the existing schemes in terms of prediction accuracy at the average RMSE of 6.01 dBm.

Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications

In the emerging high mobility vehicle-to-everything (V2X) communications using millimeter wave (mmWave) and sub-THz, multiple-input multiple-output (MIMO) channel estimation is an extremely challenging task. At mmWaves/sub-THz frequencies, MIMO channels exhibit few leading paths in the space-time (ST) domain (i.e., directions or arrival/departure and delays). Algebraic low-rank (LR) channel estimation exploits ST channel sparsity through the computation of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">position-dependent</i> MIMO channel eigenmodes leveraging recurrent training vehicle passages in the coverage cell. LR requires vehicles’ geographical positions and tens to hundreds of training vehicles’ passages for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">each</i> position, leading to significant complexity and control signaling overhead. Here, we design a deep-learning (DL)-based LR channel estimation method to infer MIMO channel eigenmodes in V2X urban settings, starting from a single least squares (LS) channel estimate and without needing vehicle’s position information. Numerical results show that the proposed method attains comparable mean squared error (mse) performance as the position-based LR. Moreover, we show that the proposed model can be trained on a reference scenario and be effectively transferred to urban contexts with different ST channel features, providing comparable mse performance without an explicit transfer learning procedure. This result eases the deployment in arbitrary dense urban scenarios.

User-Centric Cell-Free and Co-Located Cellular Large Scale MU-MIMO Systems: A Comparative Performance Study With Spatial Channel Correlation in Dense Urban Scenario

Large Scale Multi-user MIMO (LS-MU-MIMO) is a promising technology for the fifth-generation (5G) and beyond wireless systems. It offers several magnitudes of improvement in data rates and spectral efficiency (SE) due to its ability to suppress the interference and to have the properties of channel hardening and favourable propagation. In its conventional cellular paradigm, a large number of co-located antennas are deployed at the Base Station (BS) to serve a smaller number of user terminals (UTs). In order to deal with the inter-cell interferences more efficiently to achieve higher SE, a Cell-Free paradigm was proposed. Previous studies, which compare the two network deployments, relied on idealized assumptions, such as perfect channel state information, uncorrelated channels, and single-cell processing analysis-based, to name a few. This paper intends to bring further understanding of these two paradigms by examining the potential benefits of each paradigm in more realistic scenarios. Specifically, the influence of channel correlation on the achieved performance and network density in dense urban scenarios is investigated. Here, the performance of a Cell-Free network versus a traditional Co-located Cellular network structure has been compared in a more realistic setting. The comparison is carried out in different settings, taking into consideration the dense urban scenario, which supports low-to-moderate mobility and channel dispersion. First, we study the system performance gain in terms of Per-Terminal SE for different ratios of Antenna-UT and pilot scalers. Next, the Area-SE, defined as the sum SE of all UTs per unit area, is considered for different values of network density. Then, the channel estimation accuracy for both network deployments is compared, and its impact on the system performance as the Antenna-UT ratio increases is presented. Further, the impact of the spatially correlated channels is investigated in both network configurations. Finally, fronthaul requirements and distributed implementation in Cell-Free system deployment are discussed. Numerical simulations have been performed to investigate the performance gap between the two network deployments. Considering a cell-free system with scalable linear detectors and a large number of APs, the results show that the impact of noise and small-scale fading vanishes; moreover, a reduction in the non-coherent interference is observed in the same way as in the Co-Cellular LS-MU-MIMO systems. The findings indicate that employing linear detectors results in non-increasing Per-Terminal SE as the network density increases. It is also found that Area-SE grows exponentially with the network density in both system deployments. Moreover, the increase in the Antenna-UT ratio improves the Per-Terminal SE and channel estimation accuracy. However, increasing the pilot scalers affects the systems’ behavior in both deployments differently. Furthermore, local detection schemes are investigated, demonstrating the advantages of distributed implementation in the Cell-Free system in terms of reducing fronthaul signaling.

I2MB: Intelligent Immersive Multimedia Broadcast in Next-Generation Cellular Networks

The popularity of immersive multimedia content is prevalent and the consumption of 360° videos is increasing rapidly in varied domains. The broadcast of such content in cellular networks will be challenging in terms of dynamic content adaptation and efficient resource allocation to serve heterogeneous consumers. In this work, we propose an intelligent immersive new radio multimedia broadcast multicast system (NR-MBMS), I2MB, for next-generation cellular networks. I2MB intelligently forecasts the users’ viewing angle and the 360° video tiles to be broadcast beforehand using long short-term memory network. We define broadcast areas by using modified K-means clustering. The complex multivariable optimization problem that integrates efficient adaptive 360-degree video encoding and tiled broadcast using optimized transmission parameters is defined as as a Markov Decision Process (MDP) . In a dense urban scenario with a large MBSFN synchronization area, the state and action space dimensionality is very high, in which the solution is obtained by using Deep Deterministic Policy Gradient (DDPG) algorithm. I2MB incorporates Deep Reinforcement Learning based radio resource allocation (modulation-coding scheme and frequency-time resource blocks) and tiled video encoding to maximize the viewport video quality experienced by the broadcast mobile users. I2MB provides improved immersive video broadcast streaming quality while serving a higher number of mobile users. Adaptive encoding of 360° video tiles and radio resource allocation are performed based on users’ forecasted viewing angle, spatial distribution, channel conditions, and service request. The performance evaluation of our proposed scheme, I2MB, shows considerable gains in viewport quality ( 46.83%) and number of users served ( 30.52%), over a recent state-of-the-art method VRCAST.

Interference Mitigation for Non-Overlapping Sub-Band Full Duplex for 5G-Advanced Wireless Networks

With new services emerging in fifth generation (5G)-advanced, the evolution in duplex modes plays an important role to meet more stringent requirements for both downlink (DL) and uplink (UL) transmissions. In this paper, sub-band full duplex (SBFD) at base station (BS) is studied as an attainable evolution of the traditional time division duplex (TDD). Both user equipment (UE) transparent SBFD and UE perceptive SBFD are proposed and studied to serve different types of UEs. To tackle the interference introduced by SBFD, a model including both BS self-interference and cross-link interference (CLI) is presented as a first step, and new interference management schemes are proposed. Three approaches to mitigate BS self-interference, namely the passive suppression, analog interference cancellation and digital interference cancellation are analyzed. A new framework for CLI management is illustrated along with enhancements for interference identification, spatial domain interference coordination and power domain adjustment. To validate the feasibility and performance of the proposed SBFD methods under indoor and dense urban scenarios, system-level simulations (SLSs) are carried out and a proof-of-concept (PoC) is developed for the purpose of obtaining experimental results.

Re-Identification in Urban Scenarios: A Review of Tools and Methods

With the widespread use of surveillance image cameras and enhanced awareness of public security, objects, and persons Re-Identification (ReID), the task of recognizing objects in non-overlapping camera networks has attracted particular attention in computer vision and pattern recognition communities. Given an image or video of an object-of-interest (query), object identification aims to identify the object from images or video feed taken from different cameras. After many years of great effort, object ReID remains a notably challenging task. The main reason is that an object’s appearance may dramatically change across camera views due to significant variations in illumination, poses or viewpoints, or even cluttered backgrounds. With the advent of Deep Neural Networks (DNN), there have been many proposals for different network architectures achieving high-performance levels. With the aim of identifying the most promising methods for ReID for future robust implementations, a review study is presented, mainly focusing on the person and multi-object ReID and auxiliary methods for image enhancement. Such methods are crucial for robust object ReID, while highlighting limitations of the identified methods. This is a very active field, evidenced by the dates of the publications found. However, most works use data from very different datasets and genres, which presents an obstacle to wide generalized DNN model training and usage. Although the model’s performance has achieved satisfactory results on particular datasets, a particular trend was observed in the use of 3D Convolutional Neural Networks (CNN), attention mechanisms to capture object-relevant features, and generative adversarial training to overcome data limitations. However, there is still room for improvement, namely in using images from urban scenarios among anonymized images to comply with public privacy legislation. The main challenges that remain in the ReID field, and prospects for future research directions towards ReID in dense urban scenarios, are also discussed.

Location-Aware Node Management Solution for Multi-Radio Dual Connectivity Scenarios.

The location of user equipments (UEs) allows application developers to customize the services for users to perceive an enhanced experience. In addition, this UE location enables network operators to develop location-aware solutions to optimize network resource management. Moreover, the combination of location-aware approaches and new network features introduced by 5G enables to further improve the network performance. In this sense, dual connectivity (DC) allows users to simultaneously communicate with two nodes. The basic strategy proposed by 3GPP to select these nodes is based only on the power received by the users. However, the network performance could be enhanced if an alternative methodology is proposed to make this decision. This paper proposes, instead of power-based selection, to choose the nodes that provide the highest quality of experience (QoE) to the user. With this purpose, the proposed system uses the UE location as well as multiple network metrics as inputs. A dense urban scenario is assumed to test the solution in a system-level simulation tool. The results show that the optimal selection varies depending on the UE location, as well as the increase in the QoE perceived by users of different services.

Spatial channel correlation for local scattering with linear MMSE‐based estimator and detector in multi‐cell large scale MU‐MIMO networks

AbstractScalable Multi‐antenna systems are designed to gain improvement in energy and spectral efficiency under different propagation conditions by equipping the BSs with hundreds or even more antennas. A better understanding of such systems leads to overcome the essential challenges, which is important for the beneficial deployment in future networks. In wireless communication systems, channel models describe the main characteristics of the propagation environment and are essential for systems performance evaluation. This article considers a multi‐cell large scale multiuser MIMO (LS‐MU‐MIMO) system with a physical correlated channel model and linear‐based MMSE estimator and detector. A physical local scattering geometric‐based stochastic channel model for dense urban and suburban scenarios is presented in this article to illustrate the impact of propagation environment on signal transmission in a general framework of antenna spatial correlation. Also, a pilot signaling‐based channel estimation in the uplink scenario, the effect of pilot contamination (PC), and spatial correlation on the channel estimation are studied. The effect of PC and channel estimation on the performance of LS‐MU‐MIMO system is investigated by applying MMSE‐based channel estimator and detector over uniform linear arrays (ULA) of BS antennas in different scattering environments. Moreover, different pilot reuse patterns are considered, and their impact on the average sum SE has been investigated. The simulation results show that LS‐MU‐MIMO system can provide higher performance for dense urban scenarios where the user terminals (UTs) have limited mobility and higher coherence time than suburban scenarios. Furthermore, while higher spatial correlation will result in improvement in the channel estimation quality, it contributes imperfectly to the channel hardening effect.

Quantifying the Efficiency of Ride Sharing

In unit-capacity mobility-on-demand systems, the vehicles transport only one travel party at a time, whereas in ride-sharing mobility-on-demand systems, a vehicle may transport different travel parties at the same time, e.g., if paths are partially overlapping. One potential benefit of ride sharing is increased system efficiency. However, it is not clear what the trade-offs are between the efficiency gains and the reduction in quality of service. To quantify those trade-offs, an open-source simulation environment is introduced, which is capable of evaluating a large class of operational policies for ride-sharing mobility-on-demand systems. The impact of ride sharing on efficiency and service level is assessed for several benchmark operational policies from the literature and for different transportation scenarios: first a dense urban scenario, then a line-shaped, rural one. Based on the results of these case studies, we find that the efficiency gains in ride sharing are relatively small and potentially hard to justify against quality of service concerns such as reduced convenience, loss of privacy, and higher total travel and drive times. Furthermore, in the assessed scenarios, the relatively low occupancy of the vehicles suggests that smaller vehicles with 4-6 seats, able to handle occasional ride sharing, may be preferable to larger and more expensive vehicles such as minibuses.

A Multi-Frequency Investigation of Air-to-Ground Urban Propagation Using a GPU-Based Ray Launching Algorithm

Unmanned Aerial Vehicles (UAV), also known as “drones”, are attracting increasing attention as enablers for many technical applications and services, and this trend is likely to continue in the next future. When compared to conventional terrestrial communications, those making use of UAVs as base- or relay-stations can definitely be more useful and flexible in reaction to specific events, like natural disasters and terrorist attacks. Among the many and different fields, UAV enabled communications emerge as one of the most promising solutions for next-generation mobile networks, with a special focus on the extension of coverage and capacity of mobile radio networks. Motivated by the air-to-ground (A2G) propagation conditions which are likely to be different than those experienced by traditional ground communication systems, this paper aims at investigating the narrowband properties of the air-to-ground channel for 5G communications and beyond by means of GPU accelerated ray launching simulations. Line of sight probability as well as path loss exponent and shadowing standard deviations are analysed for different UAV flight levels, frequencies and dense urban scenarios, and for different types of on board antennas. Thanks to the flexibility of the ray approach, the role played by the different electromagnetic interactions, namely reflection, diffraction and diffuse scattering, in the air-to-ground propagation process is also investigated. Computation time is reported as well to show that designing UAV communication networks and optimising their performances in a fast and reliable manner, might avoid exhausting – multiple - measurement campaigns.

MmWave M2M Networks: Improving Delay Performance of Relaying

For future wireless networks, applications such as Industrial Internet of Things are strictly delay-sensitive. Meanwhile, millimeter-wave (mmWave) communication is a promising means to provide ultra-high data rate and ultra-low latency services to massive number of devices. In order to minimize uplink end-to-end delay in such machine-to-machine (M2M) mmWave communications, we investigate buffer-aided multi-hop relaying networks and formulate the problem as a multi-tier queueing system. We propose a Minimum-Delay relaying scheme, and by leveraging stochastic geometry, we present a tractable analytical framework to investigate the signal-to-interference-plus-noise-ratio (SINR) distribution of devices at each-tier, thereby computing the expected delay and delay outage probabilities using Lagrange optimization. A state-of-art max-SINR relaying scheme is analyzed for comparison, and the performance of Minimum-Delay relaying in 3-tier architecture is further analyzed. The derived average delay and delay outage probability are validated through simulations based on multiple cells in a dense urban scenario. Numerical results show that the proposed Minimum-Delay relaying scheme achieves significant lower average end-to-end delay than direct association or the max-SINR relaying scheme. Furthermore, results for Jain’s fairness and spectral efficiency reveal that the Minimum-Delay relaying scheme has even greater performance improvement under high traffic loads.

Decoupled Downlink and Uplink Access for Aerial Terrestrial Heterogeneous Cellular Networks

To enable reliable connectivity in highly dynamic and dense communication environments, aerial-terrestrial heterogeneous cellular networks (AT-HCNs) have been proposed as a plausible enhancement to the conventional terrestrial HCNs (T-HCNs). In dense urban scenarios, users are often located in clusters and demand high bandwidth in both downlink (DL) and uplink (UL). We investigate this scenario and model the spatial distribution of clustered users using a Matern cluster process (MCP). Based on our analysis we then argue that decoupling of DL and UL in such a setting can significantly improve coverage performance and spectral efficiency. We further obtain closed-form expressions for the system coverage probability, spectral efficiency, and energy efficiency by using the Fox H-function. The obtained results confirm the validity of the proposed analytical model. Our simulations further indicate a significant performance improvement using decoupled access and provide quantitative insights on AT-HCN system design.

Analysis of solar PV potential for roofs and façades in high dense residential urban scenario of Singapore

This paper aims to understand the potential of solar photovoltaics (PV) on building’s roof and façades in the dense residential neighborhoods of Singapore. The physical urban morphological parameters such as height, built form, distance between the buildings, orientation, plot ratio, building volume ratio and site coverage have impacts on the solar irradiation on the roof and façades at various intensities. With these parameters in consideration, solar irradiation has been simulated and analyzed for 12 existing neighborhoods of Singapore along with 36 additional modified neighborhoods.

Randomized Kaczmarz algorithm for massive MIMO systems with channel estimation and spatial correlation

SummaryTo exploit the benefits of massive multiple‐input multiple‐output (M‐MIMO) technology in scenarios where base stations (BSs) need to be cheap and equipped with simple hardware, the computational complexity of classical signal processing schemes for spatial multiplexing of users shall be reduced. This calls for suboptimal designs that perform well the combining/precoding steps and simultaneously achieve low computational complexities. An approach on the basis of the iterative Kaczmarz algorithm (KA) has been recently investigated, assuring well execution without the knowledge of second order moments of the wireless channels in the BS, and with easiness since no tuning parameters, besides the number of iterations, are required. In fact, the randomized version of KA (rKA) has been used in this context because of global convergence properties. Herein, modifications are proposed on this first rKA‐based attempt, aiming to improve its performance‐complexity trade‐off solution for M‐MIMO systems. We observe that long‐term channel effects degrade the rate of convergence of the rKA‐based schemes. This issue is then tackled herein by means of a hybrid rKA initialization proposal, which lands within the region of convexity of the algorithm and assures fairness to the communication system. The effectiveness of our proposal is illustrated through numerical results, which bring more realistic system conditions in terms of channel estimation and spatial correlation than those used so far. We also characterize the computational complexity of the proposed rKA scheme, deriving upper bounds for the number of iterations. A case study focused on a dense urban application scenario is used to gather new insights on the feasibility of the proposed scheme to cope with the inserted BS constraints.

Cost-Optimal Deployment of a C-RAN With Hybrid Fiber/FSO Fronthaul

Centralized radio access network (C-RAN) has been considered as an architectural solution able to reduce capital and operational expenditure in dense 5G cellular networks while allowing better network performance. The C-RAN approach decouples baseband units from antenna sites and places them in selected locations, connected by the so-called fronthaul links. These links require expensive high-capacity connections, thus calling for cost-efficient deployment. This paper presents a hybrid fronthaul solution for C-RAN based on both optical fibers and free-space optics (FSO) to enhance fronthaul flexibility and minimize deployment costs. Two design strategies based on integer linear programming are proposed for both greenfield and brownfield deployments. The first strategy is referred to as joint planning (JP) and is based on the joint minimization of the number of deployed remote radio heads (RRHs) and the cost of the hybrid fiber/FSO fronthaul. The second strategy is based on two-step disjoint planning (DP) that first identifies a cost-optimal RRH placement and then finds the corresponding minimum cost deployment for the fronthaul links. Results obtained with JP and DP are compared in dense urban area scenarios (i.e., with characteristics similar to festivals or concerts), highlighting the advantage of the JP approach compared to DP, both in terms of costs and an enhanced flexibility during the network design process.

Location-Free Spectrum Cartography

Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals, as occurs in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps relies on features of these positioning signals rather than on location estimates. Specific learning algorithms are built upon this approach and offer a markedly improved estimation performance than existing approaches relying on localization, as demonstrated by simulation studies in indoor scenarios.

Performance of a dense urban massive MIMO network from a simulated ray-based channel

Massive MIMO network deployments are expected to be a key feature of the upcoming 5G communication systems. Such networks are able to achieve a high level of channel quality and can simultaneously serve multiple users with the same resources. In this paper, realistic massive MIMO channels are evaluated both in single and multi-cell environments. The favorable propagation property is evaluated in the single-cell scenario and provides perspectives on the minimal criteria required to achieve such conditions. The dense multi-cell urban scenario provides a comparison between linear, planar, circular, and cylindrical arrays to evaluate a large-scale multi-cell massive MIMO network. The system-level performance is predicted using two different kinds of channel models. First, a ray-based deterministic tool is utilized in a real North American city environment. Second, an independent and identically distributed (i.i.d.) Rayleigh fading channel model is considered, as often used in previously published massive MIMO studies. The analysis is conducted in a 16-macro-cell network with both randomly distributed outdoor and indoor users. It is shown that the physical array properties like the shape and configuration have a large impact on the throughput statistics. Although the system-level performance with i.i.d. Rayleigh fading can be close to the deterministic prediction in some situations (e.g., with large linear arrays), significant differences are noticed when considering other types of arrays. The differences in the performance of the various arrays utilizing the exact same network parameters and the same number of total antenna elements provide insights into the selection of these physical parameters for upcoming 5G networks.

Processing and Communication Delays in EWS: On the Performance of the Earthcloud Prototype

A Seismic Alert System (SAS), also called Earthquake Warning System (EWS) or Earthquake Early Warning System (EEW or EEWS), represents one of the most important measures that can be taken to prevent and minimize earthquake damage. These systems are mainly used to detect P-waves and the faster seismic waves and to subsequently trigger an alarm about the incoming S-waves, the slower and most dangerous seismic waves. In some cases, distributed systems are also able to alert some locations before the impending P-waves strike them. This paper presents Earthcloud, a cloud-based SAS that aims to provide all the former capabilities while retaining financial accessibility. Earthcloud first results, generated from four months of data acquisition, are compared with those coming from other systems. In particular, the paper focuses on processing and communication delays, showing how the Earthcloud new detection strategy may minimize delays. Although a thorough test campaign with more sensor nodes is needed to assess performance reliably, especially for highly dense urban scenarios, initial results are promising, with total latencies for Earthcloud always kept under the 1-second mark, despite being at the expense of solid magnitude estimation.

Path Loss Model for UAV-Assisted RFET

In this letter, we develop channel models for unmanned aerial vehicle-aided air-to-ground (AtG) radio frequency energy transfer (RFET). AtG path loss plays a key role in RFET-based recharging of field nodes. Representative field environments consist of different types of built-up areas, namely, suburban, urban, dense urban, urban high-rise, and rural agriculture deployment scenarios. For emulating the built-up areas, ITU-R recommendations are considered, whereas for rural agricultural field deployment scenario, dynamics of vegetation growth with time is considered. Accuracy of the proposed path loss models are validated by conducting real AtG radio frequency transmission experiments in emulated suburban and agricultural fields.