Dense Deployment Research Articles

Optimal Feedback Rate for Multi-Antenna Maximum Ratio Transmission in Single-User MIMO Systems with One-Bit Analog-to-Digital Converters in Dense Cellular Networks

Stochastic geometry has emerged as a powerful tool for modeling cellular networks, especially in dense deployment scenarios where inter-cell interference is significant. Previous studies have extensively analyzed multi-antenna systems with partial channel state information at the transmitter (CSIT) using stochastic geometry models. However, most of these works assume the use of infinite-resolution analog-to-digital converters (ADCs) at the receivers. Recent advances in low-resolution ADCs, such as one-bit ADCs, offer an energy-efficient alternative for millimeter-wave systems, but the interplay between limited feedback and one-bit ADCs remains underexplored in such networks. This paper addresses this gap by analyzing the optimal feedback rate that maximizes net spectral efficiency in dense cellular networks, modeled using stochastic geometry, with both limited feedback and one-bit ADC receivers. We introduce an approximation of the achievable spectral efficiency to derive a differentiable expression of the optimal feedback rate. The results show that while the scaling behavior of the optimal feedback rate with respect to the channel coherence time remains unaffected by the ADC’s resolution, the absolute values are significantly lower for one-bit ADCs compared to infinite-resolution ADCs. Simulation results confirm the accuracy of our theoretical approximations and demonstrate the impact of ADC resolution on feedback rate optimization.

The concept of effective coverage radius use of the unlicensed high-frequency range in the operation of the 5G network

Economic expediency encourages mobile operators to deploy 5G networks in places with a high concentration of speed-demanding subscribers. In such conditions, sharp fluctuations in the volume of traffic with regulated requirements for the quality of service are inevitable. Note that 5G operates in the millimeter range. Accordingly, the quality of traffic service is affected both by the number of subscribers simultaneously initiating requests from one sector of coverage, and by the appearance of obstacles opaque to radio radiation in the space between the subscriber device and the base station. Effective smoothing of 5G traffic fluctuations, taking into account these disturbing factors, is an urgent task. The goal of this research is to evaluate the service quality parameters in a target area characterized by a specific user density. It takes into account that if the declared QoS requirements for connection speed for users in a network segment deployed within the licensed frequency range for 5G are not met, they can utilize a network segment deployed in the unlicensed high-frequency range for 5G under conditions of free competition. The metric being studied is the probability of session loss in the licensed network segment and the achievable transmission speed in the unlicensed network segment. Based on this, a method for assessing the density of base station deployment in the unlicensed network segment necessary to support the specified user density in the licensed network segment with defined QoS guarantees in terms of bandwidth is formalized. The experiment results showed that the probability of losing sessions with regulated requirements for the quality of service in both network segments, in addition to the base station placement density and subscriber devices, is significantly affected by the minimum data transfer rate, the intensity of obstacles, and the value of the Contention Window.

A Federated Reinforcement Learning Approach for Optimizing Wireless Communication in UAV-Enabled IoT Network With Dense Deployments

A Federated Reinforcement Learning Approach for Optimizing Wireless Communication in UAV-Enabled IoT Network With Dense Deployments

Deep reinforcement learning-based spectrum resource allocation for the web of healthcare things with massive integrating wearable gadgets

With the development of the future Web of Healthcare Things (WoHT), there will be a trend of densely deploying medical sensors with massive simultaneous online communication requirements. The dense deployment and simultaneous online communication of massive medical sensors will inevitably generate overlapping interference. This will be extremely challenging to support data transmission at the medical-grade quality of service level. To handle the challenge, this paper proposes a hypergraph interference coordination-aided resource allocation based on the Deep Reinforcement Learning (DRL) method. Specifically, we build a novel hypergraph interference model for the considered WoHT by analyzing the impact of the overlapping interference. Due to the high complexity of directly solving the hypergraph interference model, the original resource allocation problem is converted into a sequential decision-making problem through the Markov Decision Process (MDP) modeling method. Then, a policy and value-based resource allocation algorithm is proposed to solve this problem under simultaneous online communication and dense deployment. In addition, to enhance the exploration ability of the optimal allocation strategy for the agent, we propose a resource allocation algorithm with an asynchronous parallel architecture. Simulation results verify that the proposed algorithms can achieve higher network throughput than the existing algorithms in the considered WoHT scenario.

ZeroVCS: An efficient authentication protocol without trusted authority for zero-trust vehicular communication systems

Vehicular communication systems can provide two types of communications: Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V). However, in both cases, there is zero-trust between the communicating entities. This may give privilege to the unauthorized vehicles to join the network. Hence, a strong authentication protocol is required to ensure proper access control and communication security. In traditional protocols, such tasks are typically accomplished via a central Trusted Authority (TA). However, communication with TA may increase the overall authentication delay. Such delay may be incompatible with the future generation vehicular communication systems, where dense deployment of small-cells are required to ensure higher system capacity and seamless mobility (e.g., 5G onward). Further, TA may suffer from denial-of-service when the number of access requests becomes excessively large, because each request must be forwarded to TA for authentication and access control. In this article, we put forward an efficient authentication protocol without trusted authority for zero-trust vehicular communication systems, called ZeroVCS. It does not involve TA for authentication and access control, thus improving the authentication delay, reducing the chance of denial-of-service, and ensuring compatibility with the future generation vehicular communication systems. ZeroVCS can also provide communication security under various passive and active attacks. Finally, the performance-based comparison proves the efficiency of ZeroVCS.

Performance analysis of joint transmission in TDD-based mm-wave networks with BS heights

Dynamic-time division duplexing (D-TDD) is used in millimeter-wave (mm-wave) networks to allocate time-frequency resources for both downlink (DL) and uplink (UL) transmissions. This scheduling mechanism adapts to varying data traffic to meet the different quality of service (QoS) requirements of heterogeneous services. However, blockage is the major bottleneck to millimeter wave (mm-Wave) networks since it causes signal outages. Moreover, the dense deployment of base stations (BSs) to reduce the signal outage problem may lead to severe interference due to solid line-of-sight (LoS) BSs. To solve the problem of less coverage, this paper utilizes a joint transmission mechanism in the DL of D-TDD-based mm-Wave networks. Further, examining the network performance also entails considering the elevation disparity between the Base Station (BS) and User Equipment (UE). The primary focus of this work is to study the effects of joint transmission in D-TDD-based DL mm-Wave networks with different BS heights and directional beamforming using the stochastic geometry framework. For D-TDD with Joint Transmission, numerical evaluations indicate that the SINR coverage probability reaches approximately 0.7 when the coverage radius (rc) is 100 m. Conversely, at a coverage radius of 200 m, the SINR coverage probability drops to around 0.25. The simulation results demonstrate using MATLAB 2019b tool, that D-TDD-based mm-Wave networks achieve superior coverage using the joint transmission scheme compared to single BS association. Besides, results show that optimal UL and DL split and load balancing are required to achieve better DL coverage.

A strategy for the improvement of the bonding performance of 3D-printed concrete interlayer interfaces

3D-printed concrete, as an intelligent building technology, has enormous potential in construction. However, the weak bond interfaces between two adjacent layers of concrete are still a well-known problem affecting the mechanical properties of printed structures, with reduced interlayer shear and tensile properties. The reinforcement method of inserting steel bars in vertically printed layers is crucial and requires research and exploration of suitable methods, as well as finding the optimal reinforcement scheme that satisfies the interlayer interface when facing different loads. This study proposes a method of interlayer reinforcement by vertically laying steel fibers between layers, and investigates the effects of steel fiber offset, deployment density, and cross-sectional area on interlayer bonding strength. The strengthening effect of steel fiber on interlayer shear and tensile strength was tested through shear and splitting tests. Microscopic numerical models are used to study the evolution process of interlayer cracking. The correlation between different steel fiber variables and interlayer bonding strength was studied using a Generalized Grey Relational Analysis (GGRA). The results showed that the shear strength and tensile strength of the interlayer interface could be improved by 256.32 % and 353.61 %, respectively, through a reasonable fiber layout scheme. It is recommended to lay steel fibers with small offsets. When the interlayer interface is subjected to shear loads, priority should be given to increasing the cross-sectional area. When the interlayer interface is subjected to tensile loads, the deployment density should be increased first to achieve the best improvement in interlayer strength.

Security, privacy and performance concerns in ultra dense networks

Ultra Dense Networks (UDNs) have emerged as a pivotal technology in meeting the exponential growth in data demand and providing seamless connectivity in 5G and beyond networks. However, the high density of small cells in UDNs introduces significant challenges related to security, privacy, and performance. This survey paper presents a comprehensive review of the current state-of-the-art in addressing these concerns. It begins by exploring the unique security vulnerabilities inherent to UDNs, including the increased risk of eavesdropping, denial of service attacks, and unauthorized access due to the close proximity of small cells. The paper then discusses privacy issues, particularly the risks of location tracking and user data exposure, exacerbated by the dense deployment of base stations. In terms of performance, the paper evaluates the impact of interference, handover management, and resource allocation on network efficiency. Various proposed solutions, such as advanced encryption techniques, privacy-preserving algorithms, and interference mitigation strategies, are analyzed and compared. The survey concludes by identifying open research challenges and future directions, emphasizing the need for integrated approaches that simultaneously address security, privacy, and performance to ensure the robust operation of UDNs in next-generation wireless networks.

Deep Learning-Based Transmitter Localization in Sparse Wireless Sensor Networks.

In the field of wireless communication, transmitter localization technology is crucial for achieving accurate source tracking. However, the extant methodologies for localization face numerous challenges in wireless sensor networks (WSNs), particularly due to the constraints posed by the sparse distribution of sensors across large areas. We present DSLoc, a deep learning-based approach for transmitter localization in sparse WSNs. Our method is based on an improved high-resolution network model in neural networks. To address localization in sparse wireless sensor networks, we design efficient feature enhancement modules, and propose to locate transmitter locations in the heatmap using an image centroid-based method. Experiments conducted on WSNs with a 0.01% deployment density demonstrate that, compared to existing deep learning models, our method significantly reduces the transmitter miss rate and improves the localization accuracy by more than double. The results indicate that the proposed method offers more accurate and robust performance in sparse WSN environments.

Sensing Classroom Temperature, Relative Humidity, Illuminance, CO2, and Noise: An Integral Solution Based on an IoT Device for Dense Deployments.

Maintaining optimal Indoor Environmental Quality (IEQ) requires continuous measurement of certain variables. To this end, ASHRAE and BPIE recommend that at least the following areas of interest be considered when measuring IEQ: thermal comfort, illuminance, indoor air quality, and noise. At this time, it is not common to find an IoT device that is suitable for dense deployments in schools, university campuses, hospitals, and office buildings, among others, that measures variables in all of the above areas of interest. This paper presents a solution to the problem previously outlined by proposing an IoT device that measures variables across all of the aforementioned areas of interest. Moreover, in a radio frequency network with a tree-like structure of IoT devices, this device is able to assume the roles of sensor and hub node, sensor and router node, and only sensor node. The experimental results are satisfactory, and the detailed system design ensures the replicability of the device. Furthermore, the theoretical analysis paves the way for high scalability.

Support vector machine‐based handover scheme for heterogeneous ultra dense network of high‐speed railway

AbstractIn order to meet the growing demands and extend network coverage for high‐speed railway (HSR) system, the dense deployment of a large number of small cells (SCs) is considered for 5G networks. However, the deployment of dense SCs and the high speed of trains result in challenging problems such as interference, frequent handovers (HOs), increased HO failure rate, and consequently the deteriorated overall quality of service (QoS). In order to address the challenges in handover, an improved handover decision strategy is proposed based on Support Vector Machine (SVM). The HO decision making is considered as a classification problem taking into account available states that they may have in the HSR network. From the simulation results, it is observed that the proposed scheme is capable of decreasing the number of HO, HO failure rate and enhancing the network performance remarkably.

Evaluation of the In2Care Mosquito Station at low deployment density: a field study to manage Aedes aegypti and Culex quinquefasciatus (Diptera: Culicidae) in North Central Florida.

In the last 2 decades, there has been an increase in the geographic range and frequency of vector-borne diseases. Management of mosquito populations has become challenging due to increasing rates of resistance to existing insecticidal products and formulations. Several alternative tools have emerged to suppress or replace mosquito populations. One of these tools is the In2Care Mosquito Station (In2Care station). This dual-action station contains the insect growth regulator pyriproxyfen which disrupts the development of immatures and the entomopathogenic fungus Beauveria bassiana (B. bassiana) strain GHA which kills exposed adult mosquitoes. The In2Care stations have previously been shown to effectively control Aedes aegypti in field settings at a density of 6 stations/acre rather than the label-recommended 10 stations/acre. To further test the efficacy of low station density deployment, we deployed In2Care stations in the Pleasant Street Historic District of Gainesville, Florida, at a density of 3 stations/acre over a period of 2 years in the presence or absence of ground larvicidal applications. The deployment of stations resulted in no measurable impact on Ae. aegypti and Culex quinquefasciatus adult or immature abundance suggesting that the low-density deployment of In2Care stations is insufficient to reduce Ae. aegypti and Cu. quinquefasciatus abundance within treatment areas.

Levy flight based momentum search and Circle Rock Hyrax swarm optimization algorithms for energy effective clustering and optimal routing in wireless body area networks

In recent years, the connectivity of a new dimension has been brought to Wireless Body Area Networks (WBANs) through the concept of the Wearable Internet of Things (WIoT). WIoT offers valuable functionalities like data collection, analysis, and transmission for real-time health monitoring services. However, WBANs are characterized by a large number of battery-powered sensor nodes, making them resource-constrained. While keeping standard WSN abilities, WBAN's significant obstacles are data communication, social mobility, high interference in dense deployment, latency, and energy consumption of sensor nodes. As a result, the implementation and design of an application require dependable data transfer and an energy-efficient architecture. It proved critical to have as little delay as possible while delivering correct data. To overcome these issues, this study developed a novel energy-efficient clustered and optimized routing scheme in WBAN. Due to its effective scalability, this work employs an Improved Self-Executing Active Cross-Propagated (ISAC) clustering approach to form clusters in Wireless Body Area Networks (WBANs). Subsequently, the selection of the best Cluster Heads (CHs) is facilitated by the Levy Flight-based Momentum Search Algorithm (LFMSA). The Circle Rock Hyrax Swarm Optimization (CRH) algorithm is then utilized for energy-efficient routing. In determining the CH in each cluster, consideration is given to both residual energy and connectivity. The proposed framework minimizes the number of transmissions and overall transmission distance, optimizing inter and intra-cluster transmission distances. Additionally, the framework ensures energy-efficient packet delivery from CHs to the sink node. Performance comparisons, conducted using various statistical measures and simulated on the NS-2 platform, demonstrate that the proposed framework outperforms existing routing protocols and clustering methodologies. In comparison to previous methodologies, the proposed framework consistently achieves superior results.

Multi-objective optimization algorithm for multi-workflow computation offloading in resource-limited IIoT

Industrial internet of things (IIoT) connects traditional industrial devices with the network to provide intelligent services, which is regarded as the key technology for achieving Industry 4.0 and enabling the transformation of the manufacturing sector. Multi-access edge computing (MEC) has brought significant opportunities to expedite the development of IIoT. However, the unique task characteristics and dense deployment of IIoT devices, coupled with the resource starvation problem (RSP) arising from the limited resources of edge servers, pose challenges to the direct applicability of existing MEC algorithms in MEC-assisted IIoT scenarios. To this end, a multi-objective evolutionary algorithm is proposed to simultaneously optimize delay and energy consumption for multi-workflow execution in resource-limited IIoT. First, the initialization of execution location based on delay and the initialization of execution order satisfying the priority constraint can generate high-quality initial solutions. Then, the improved crossover and mutation operations guide the population evolution, which can span the large infeasible solution region. Finally, dynamic task scheduling (DTS) dynamically changes the execution location of tasks affected by RSP according to the execution efficiency, so as to avoid the tasks blindly waiting for server resources. The comprehensive simulation results demonstrate the effectiveness of the proposed method in achieving a balance between the execution delay and energy consumption of IIoT devices.

Design and performance evaluation of a 350 m free space optical communications link for pico-macrocell backhauling

Fibreless optics or free space optical communications (FSOC) has been at the forefront of many academic research in telecommunications due to its numerous benefits of large spectrum, high-speed data transmission, security, low transmit power, unlicensed spectrum and non-interfering links. Among the technical challenges of dense deployment of small cells in heterogeneous networks (HetNet) is a flexible and cost-effective backhaul link. This paper proposes, designs, simulates and evaluates the performance of a 350 m FSOC link under different atmospheric impairments for picocell to macrocell backhauling applications. The performance of the FSOC link is assessed by evaluating bit error rate (BER), eye diagram and quality factor (Q-factor). Results obtained recommend the FSOC link deployment for pico-macrocell backhauling under the weather conditions of clear sky with/without turbulence, heavy rain, heavy haze, heavy fog and wet snow.

Development of Fused Recurrent Neural Network‐Gated Recurrent Unit with Improved Sandpiper Optimization‐based Intelligent Node Localization Framework in WSN

SummaryEmploying mobile nodes or Sensor Nodes (SN) in the Wireless Sensor Networks (WSNs) is a critical task within the coverage area. However, these techniques produce huge errors in the evaluated distance between source nodes and unknown nodes, which increases the average NL error of the unknown node. The usage of a Global Positioning System (GPS) for all the SNs needs high cost for implementation in large‐scale WSNs. The designing of NL in WSN with less energy requirements and less cost is very significant. Enhanced NL is performed with deep learning techniques together with the heuristic strategy for performance enhancement. The developed localization model is mainly utilized for providing fast, scalable, and easily implementable results over heterogeneous and homogeneous networks. Moreover, it is applicable for sparse and dense deployment of nodes. Here, a hybridized deep structured approach is employed by integrating the Recurrent Neural Network (RNN) with a Gated Recurrent Unit (GRU) for calculating the distance among the source nodes to facilitate determining the optimal location for the unidentified node by analyzing the node information present in the received packets. Here, the optimization of constraints executed in the deep learning techniques with the Modified Exploration‐based Sandpiper Optimization Algorithm (ME‐SOA), and also it is utilized to find the optimal location of unknown nodes based on computed distance from the received packets that minimize the average localization error. The experimental results are validated and show the effectiveness of the proposed algorithm with a reduced error rate than other algorithms.

Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations.

Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson's disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left-right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.

A congestion-aware user–cell association scheme to assist with traffic offloading in a three-tier heterogeneous network

In this work, we aim to expand the cell range in congestion areas. To achieve this goal, we integrate UAV network into macro–micro cellular network to enhance coverage. However, the gain of deploying heterogeneous nodes is limit if there are no CRE (cell range expanding)-based method to steer users towards low-power nodes. Therefore, in order to further improve system access capacity, we propose a congestion-aware UA (user–cell association) scheme to offload queued traffic of cellular network into UAV network. To verify the superiority of proposed scheme, we utilize the low-complexity SG (stochastic-geometry) methods. Then in simulation analysis phase, we obtain five primary results: (a). The SG-based methods have lower computation complexity, and the convergence of derived expressions are verified. (b). The SG-based methods do not need the exact users’ location but the users’ density, which have improved the operability and robustness in infrastructure construction of wireless network. (c). The congestion-aware UA scheme can prompt network to adjust association tier flexibly, which is very suitable for traffic unevenly-distributed regions. (d). The optimal offsets of virtual power are estimated as B2 = −4 dB for small-cell and B3 = 3 dB for drone-cell, the resulting improvement in communication capacity is 8%. (e). We suggest the deployment density of UAV sites will no need to exceed λ3 = 41 (pks/km2).

DRAMS: Double-RIS assisted multihop routing scheme for device-to-device communication

Reconfigurable intelligent surfaces (RISs) is a promising solution for enhancing the performance of multihop wireless communication networks. In this paper, we propose a double-RIS assisted multihop routing scheme for a device-to-device (D2D) communication network. Specifically, the scheme is dependent on the already deployed RISs and users in the surroundings. Besides the RISs, the emphasis of this work is to make more use of the existing intermediate users (IUs), which can act as relays. Hence, the density of RIS deployment in the surroundings can be reduced, which leads to the avoidance of resource wastage. However, we cannot solely depend on the IUs because this implies complete dependence on their availability for relaying and as a result, the aspect of reliability in terms of delay-constrained information transfer cannot be guaranteed. Moreover, the IUs are considered capable of energy harvesting and as a result, they do not waste their own energy in the process of volunteering to act as a relay for other users. Numerical results demonstrate the advantage of the proposed scheme over some existing approaches and lastly, useful insights related to the scheme design are also drawn, where we characterize the maximum acceptable delay at each hop under different set-ups.

Tracking Seismic Velocity Perturbations at Ridgecrest Using Ballistic Correlation Functions

Abstract We present results based on data of a dense nodal array composed of 147 stations, deployed in 2022 near the epicenter of the 2019 Mw 7.1 Ridgecrest earthquake to investigate characteristics of the seismic wavefields. Through array analyses, we identified two primary components. First, we observed far-field P waves dominating the 0.5–1.2 Hz frequency range, which are likely primarily generated by wind-driven oceanic swell activity. Second, we detected near-field body waves resulting from anthropogenic activities in the frequency range 2–8 Hz. We examined noise correlation functions derived from data of the dense deployment and regional stations to explore fault-zone seismic velocity changes using ballistic arrivals, with a focus on velocity perturbation shortly before and after the Ridgecrest earthquake sequence. Our findings exhibit distinct behavior compared to results obtained through standard coda-wave interferometry. Particularly, we observed a decrease in P-wave travel time on certain station pairs prior to the 2019 earthquake sequence. Supported by detailed investigation of the local seismic wavefields, we interpret the decreasing P-wave travel time as likely caused by a velocity increase away from the fault, possibly related to fluid migration. However, additional information is necessary to verify this hypothesis.