Deployment Of Access Points Research Articles

Enhancing Indoor Localization Accuracy through Multiple Access Point Deployment

This study addresses the limitations of wireless local area networks in indoor localization by utilizing Extra-Trees Regression (ETR) to estimate locations based on received signal strength indicator (RSSI) values from a radio environment map (REM). We investigate how integrating numerous access points can enhance indoor localization accuracy. By constructing an extensive REM using RSSI data from various access points collected by a mobile robot in the intended interior setting, we evaluate several machine learning regression techniques. Our research pays special attention to an optimized ETR model, validated through 10-fold cross-validation and hyperparameter tuning. We quantitatively evaluate the efficiency of our suggested multi-access-point approach using root mean square error (RMSE) for REM evaluation and location error metrics for accurate localization. The results show that incorporating multiple access points significantly improves indoor localization accuracy, providing a substantial improvement over single-access-point systems when assessing interior radio frequency environments.

Mobility aware blockage management of a multi-user multi-cell hybrid Li-Fi Wi-Fi system with freeform diversity receiver

In order to accommodate high data traffic, the co-deployment of heterogeneous networks like light fidelity (Li-Fi) and wireless fidelity (Wi-Fi) can offer supplementary small-cell layers of support and bring about a paradigm shift in achievable system throughput and quality of service. However, dense deployment of Li-Fi access points(APs) in an indoor arena often leads to co-channel-interference (CCI) that can be fruitfully diminished by adopting a customized optical front-end called freeform diversity receiver (FDR). With regard to multi-user association, this work judiciously imparts the motivation behind the adoption of FDR in a hybrid Li-Fi Wi-Fi network (HLWNet). Based on different mobility scenarios and blockage conditions the performance of the proposed HLWNet has been evaluated. A Li-Fi channel model with FDR and a rule-based resource allocation algorithm (RBRA) has been proposed for the purpose. Nevertheless, the network data quality of the multi-user system has been estimated in terms of packet loss, latency, and fairness index. Unlike the existing optimal resource allocation (ORA), the RBRA demonstrates superior network performance. Additionally, the execution time in the RBRA is reduced by a factor of 89 compared to the optimal resource allocation algorithm. Simulation results show a satisfactory fairness index of more than 0.85 and latency within 2.1 ms for a 10-user association inside an indoor environment of 25m2 floor area. In the absence of LOS blockage, the system throughput exhibits minimal variation, staying consistent for both methods with less than a 2% difference. However, significant improvement in average user throughput and effective system throughput has been observed compared to the existing studies. Despite line-of-sight (LOS) blockage, the proposed system with RBRA consistently maintains throughput within the range of 2.01 Gbps to 2.55 Gbps. The average user throughput, varying from 182 Mbps to 480 Mbps, is contingent upon the number of associated users, which ranges from 4 to 14.

Force-Based Self-Organizing MANET/FANET with a UAV Swarm

This paper introduces a novel distributed algorithm designed to optimize the deployment of access points within Mobile Ad Hoc Networks (MANETs) for better service quality in infrastructure-less environments. The algorithm operates based on local, independent execution by each network node, thus ensuring a high degree of scalability and adaptability to changing network conditions. The primary focus is to match the spatial distribution of access points with the distribution of client devices while maintaining strong connectivity to the network root. Using autonomous decision-making and choreographed path-planning, this algorithm bridges the gap between demand-responsive network service provision and the maintenance of crucial network connectivity links. The assessment of the performance of this approach is motivated by using numerical results generated by simulations.

SE-Loc: Security-Enhanced Indoor Localization With Semi-Supervised Deep Learning

Wireless indoor localization has become unavoidable for industrial indoor location-based services. Given the ubiquitous deployment of wireless access points (APs), WiFi fingerprinting of Received Signal Strength (RSS) has been widely adopted for indoor localization. Meanwhile, existing RSS fingerprint-based methods lack security-oriented considerations and are vulnerable to malicious attacks. When security vulnerabilities are exploited, mobile users may confront indoor localization mismatches, faults and even localization system failures. In this paper, we propose SE-Loc, a semi-supervised learning-based technique to enhance security and resiliency of fingerprint-based localization. The architecture of SE-Loc consists of two parts: (1) a correlation-based AP selection for processing RSS fingerprints and fingerprint-image generation, and (2) a deep learning model based on a denoising autoencoder and convolutional neural networks for robust feature learning and location matching. Extensive experiments show that under potential AP attacks, SE-Loc demonstrates superior performance on indoor localization over the state-of-the-art methods. With up to 100 malicious attacking APs in the UJIIndoorLoc edge server, SE-Loc can still achieve the lowest error fluctuation of 1.7 m and the highest average localization accuracy of 8.9 m.

A Review of Miscellaneous Spectrum Sensing Algorithms in 5G Ultra-dense Networks

The continual development of advanced networks within the Fifth Generation (5G) of wireless systems, and beyond, has seen the rise of multiple important research directions. These include cognitive radio (CR) and ultra-dense networks (UDNs), which are the focus of this article. The CR systems rely on an accurate assessment of the radio environment, which is provided by the spectrum sensing functionality. A review of such algorithms that are characterized by the detection of miscellaneous features of the received signal, together with their performance comparison, is presented. In addition, the application of a simple and adequate solution is assessed through its probability of detection, for a relevant UDN system model under the critical density limitation for the access point (AP) deployment

EasyAPPos: Positioning Wi-Fi Access Points by Using a Mobile Phone

Determining the location of Wi-Fi access points (APs) is vital for various Wi-Fi-based applications such as localization, security, and AP deployment. Considerable effort has been exerted in the field of AP localization. In contrast to studies that require additional robots with specialized antenna arrays, we present EasyAPPos, a lightweight, always-on, and user-centered AP positioning solution that utilizes widely available mobile phones. We focus on addressing three challenges in AP positioning. First, the patch antenna on a mobile phone has a limited angular range due to its size, but our approach proposes a method for utilizing human natural rotation to enhance angular diversity. Second, our angle-based algorithm does not require synchronous clocks between the mobile device and the APs, in contrast to existing algorithms that require this synchrony to transform propagation delays into positions. Nevertheless, our algorithm can still utilize asynchronous delay information. Third, the low bandwidth of Wi-Fi beacon frames, which only provide limited capacity to counteract the effects of multipath, is addressed by performing AP positioning under challenging conditions. We validate EasyAPPos through simulations and experiments, which demonstrate its ability to achieve decimeter-level positioning accuracy even under harsh conditions.

User-Oriented Edge Node Grouping in Mobile Edge Computing

In mobile edge computing networks, densely deployed access points are empowered with computation and storage capacities. This brings benefits of enlarged edge capacity, ultra-low latency, and reduced backhaul congestion. This paper concerns edge node grouping in mobile edge computing, where multiple edge nodes serve one end user cooperatively to enhance user experience. Most existing studies focus on centralized schemes that have to collect global information and thus induce high overhead. Although some recent studies propose efficient decentralized schemes, most of them did not consider the system uncertainty from both the wireless environment and other users. To tackle the aforementioned problems, we first formulate the edge node grouping problem as a game that is proved to be an exact potential game with a unique Nash equilibrium. Then, we propose a novel decentralized learning-based edge node grouping algorithm, which guides users to make decisions by learning from historical feedback. Furthermore, we investigate two extended scenarios by generalizing our computation model and communication model, respectively. We further prove that our algorithms converge to the Nash equilibrium with upper-bounded learning loss. Simulation results show that our mechanisms can achieve up to 96.99% of the oracle benchmark.

Multifrequency Wireless Channel Measurements and Characterization in Large Indoor Office Environments

This paper performs extensive channel measurements and characteristics analysis to investigate large-scale fading (LSF) and small-scale fading (SSF) of wireless local area network (WLAN) channels in large indoor office environments. Multi-frequency single-input single-output (SISO) channel measurements are conducted at 3, 5.5, and 6.5 GHz under the same conditions to explore the frequency dependence of LSF, delay spread (DS), and K-factor (KF). Then, SISO channel measurements with different half-power beamwidths (HPBWs) of antennas are performed at 5.5 GHz in access point (AP) to user equipment (UE) and AP to AP scenarios. The effects of antenna HPBW on LSF, DS, and KF are investigated, thereby inspiring the AP deployment in high density (HD) scenarios. Finally, 32×64 multiple-input multiple-output (MIMO) channel measurements at 5.5 GHz are conducted to study the SSF of the time non-stationarity and multi-link correlation. The time non-stationarity, including the parameters drifting and cluster evolution caused by the movement of the UE, is verified by measurement results. Multi-link correlations are illustrated from perspectives of the angular power spectral density (APSD) and correlation matrix collinearity (CMC). The results show that the distance between users and separation angle can affect the multi-link correlation.

Handover Experiment of 60-GHz-Band Wireless LAN in over 200-km/h High-Speed Mobility Environment

High-frequency wireless communication is drawing attention because of its potential to actualize huge transmission capacity in the next generation wireless system. The use of high-frequency bands requires dense deployment of access points to compensate for significant distance attenuation and diffraction loss. Dense deployment of access points in a mobility environment triggers an increase in the frequency of handover because the number of candidate access points increases. Therefore, simple handover schemes are needed. High-frequency wireless systems enable station position to be determined using their wideband and highly directional communication signals. Thus, simple handover based on position information estimated using the communication signal is possible. Interruptions caused by handover are also a huge barrier to actualizing stable high-frequency wireless communications. This paper proposes a seamless handover scheme using multiple radio units. This paper evaluates the combination of simple handover and the proposed scheme based on experiments using a formula racing car representing the fastest high-speed mobility environment. Experimental results show that seamless handover and high-speed wireless transmission over 200Mbps are achieved over a 400-m area even at station velocities of greater than 200km/h.

FineAP: Fine-Grained Access Point Deployment Strategy for 60 GHz Millimeter-Wave Wireless Networks

Traditional access point (AP) deployment is based on the principle of minimum area coverage, which takes the AP signal range as the radius and performs a geometric calculation to determine the deployment location. These methods lack support for various contexts and requirements, making them only useful in a conventional rectangular environment. As a result, we design a fine-grained AP deployment (FineAP) strategy, which first uses ray-tracing to determine the signal distribution of various locations in the environment, and then uses a hybrid swarm intelligence optimization algorithm based on the principle of signal matrix maximization to determine the AP deployment location. When compared to traditional deployment methods, FineAP can handle a variety of application scenarios with close to upbound performance and the least minimum of overhead.

Study of Radio over Fiber Transmission with a Single Line in Ring Topology for Deployment of MIMO Antenna Access Points in a Cell-less Network

The massive growth of wireless traffic goes hand in hand with the deployment of advanced radio interfaces as well as network densification. This growth has a direct impact on the radio access architecture, which today is moving from centralized to distributed deployments through the use of a large number of access points (APs). This paper verifies the feasibility of deploying multiple APs in series on a single line in a ring topology in a cell-less network. On the one hand, this technique will further improve the communication capacity and flexibility of a Radio-over-Fiber (RoF) based mobile communication system and will reduce its construction cost. And on the other hand, this deployment topology is a solution to achieve a massive cell-free Multiple-Input Multiple-Output (MIMO) architecture and a cost-effective fronthaul solution. First, a passive optical add/drop multiplexer (OADM) is used to extract and add downlink and uplink signals from the remote access points of one kilometer. Then, a deployment model is developed with version 17 Optisystem software. The results obtained showed that the quadrature amplitude modulation (QAM) does not adapt to this multi-carrier transmission to deploy several AP in series on a single line. Thus, the performance degradation increases when the number of APs integrated on the line increases.

UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments

Recent studies on indoor positioning using Wi-Fi fingerprinting are motivated by the ubiquity of Wi-Fi networks and their promising positioning accuracy. Machine learning algorithms are commonly leveraged in indoor positioning works. The performance of machine learning based solutions are dependent on the availability, volume, quality, and diversity of related data. Several public datasets have been published in order to foster advancements in Wi-Fi based fingerprinting indoor positioning solutions. These datasets, however, lack dual-band Wi-Fi data within symmetric indoor environments. To fill this gap, this research work presents the UTMInDualSymFi dataset, as a source of dual-band Wi-Fi data, acquired within multiple residential buildings with symmetric deployment of access points. UTMInDualSymFi comprises the recorded dual-band raw data, training and test datasets, radio maps and supporting metadata. Additionally, a statistical radio map construction algorithm is presented. Benchmark performance was evaluated by implementing a machine-learning-based positioning algorithm on the dataset. In general, higher accuracy was observed, on the 5 GHz data scenarios. This systematically collected dataset enables the development and validation of future comprehensive solutions, inclusive of novel preprocessing, radio map construction, and positioning algorithms.

When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization

As an emergent computing paradigm, mobile edge computing (MEC) can provide users with strong computing, storage, and communication services by moving the server to the user side. In recent years, applications such as virtual reality and augmented reality have brought higher requirements on transmission and computing capabilities. However, in the traditional cellular-based MEC, users at the edge of the cell will suffer severe signal attenuation and inter-cell interference, leading to a great reduction in achievable rate and proneness to transmission outage and offloading failure. To overcome this limitation, we combine the user-centric network (UCN) with MEC computing services and propose a novel framework called user-centric MEC (UCMEC). Through the dense deployment of access points, UCMEC can provide users with efficient, reliable, low-cost user-centric wireless transmission and edge computing services. To further exploit the benefits of UCMEC, we jointly optimize the task partition, transmit power control, and computing resource allocation decision to minimize the total energy consumption under delay constraints. Simulation results show that our proposed optimization scheme can bring users lower energy consumption and delay, and higher successful offloading probability than traditional MEC.

Hybrid Indoor Positioning Method of BLE and PDR Based on Adaptive Feedback EKF With Low BLE Deployment Density

Location-based service (LBS) has gradually penetrated into all aspects of human life. The current indoor positioning algorithms include triangulation, least squares, fingerprint, maximum likelihood estimation, and so on. These positioning algorithms have requirements for the deployment density of the access point (AP). Since the AP deployment density is directly related to the equipment cost, the AP-based positioning method has a high-cost problem that cannot be ignored. Furthermore, the positioning accuracy of traditional algorithms will sharply decline in the positioning field with low AP deployment density. In order to solve the positioning problem in application scenarios with low AP deployment density, this article proposes a hybrid indoor positioning method of Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR) based on the adaptive feedback extended Kalman filter (AFEKF). The AFEKF algorithm uses an adaptive feedback EKF to deeply fuse the position of the BLE, the range measurement, and the result of PDR localization, making full use of the information of the BLE (the position, the orientation, and the range measurement) instead of only the range measurement. The range measurement is deeply fed back to the estimated position, thus adaptively adjusting the position estimate at the next moment, making the position estimate more accurate and the algorithm more robust. Experiments in large underground parking lots show that the AFEKF algorithm proposed in this article can achieve high-precision and low-cost indoor positioning, and effectively solve the positioning problem in open scenes with low AP deployment density.

IEEE 802.11 graph models

There are several recent research lines addressing Wi-Fi network planning and optimization, both in terms of channel assignment and access point deployment. The problem with these works is that evaluation is usually performed with specific and closed models regarding signal propagation, throughput computation, and utility definition. Also, many of the models in the literature make assumptions about the role of wireless stations, or the co-channel interference, which -while being valid in the context of a single research work- makes very difficult to compare different approaches, to re-use concepts from previous mechanisms to create new ones, or to generalize mechanisms to other scenarios. This makes the different research lines in Wi-Fi network planning and optimization progress in an isolated manner.This paper aims to address such a recurring problem by proposing a graph-based generic model for Wi-Fi utility computation in network planning scenarios, as well as providing a collection of scenario graphs which may be used to benchmark different planning and optimization approaches. Experiments are conducted to show the validity of the model and the significance of its features, along with its extensibility to other scenarios.

Joint Resource Allocation to Minimize Execution Time of Federated Learning in Cell-Free Massive MIMO

Due to its communication efficiency and privacy-preserving capability, federated learning (FL) has emerged as a promising framework for machine learning in 5G-and-beyond wireless networks. Of great interest is the design and optimization of new wireless network structures that support the stable and fast operation of FL. Cell-free massive multiple-input–multiple-output (CFmMIMO) turns out to be a suitable candidate, which allows each communication round in the iterative FL process to be stably executed within a large-scale coherence time. Aiming to reduce the total execution time of the FL process in CFmMIMO, this article proposes choosing only a subset of available users to participate in FL. An optimal selection of users with favorable link conditions would minimize the execution time of each communication round while limiting the total number of communication rounds required. Toward this end, we formulate a joint optimization problem of user selection, transmit power, and processing frequency, subject to a predefined minimum number of participating users to guarantee the quality of learning. We then develop a new algorithm that is proven to converge to the neighborhood of the stationary points of the formulated problem. Numerical results confirm that our proposed approach significantly reduces the FL total execution time over baseline schemes. The time reduction is more pronounced when the density of access point deployments is moderately low.

A Proof of Concept on Digital Twin-Controlled WiFi Core Network Selection for In-Flight Connectivity

The in-flight connectivity (IFC) turns to a crucial need from luxury with technological advances. The WiFi-enabled IFC (W-IFC) meets most of this need by deploying access points within the aircraft. These access points can allow Internet connectivity through various core network links air-to-ground (A2G), air-to-satellite (A2S), and air-to-air (A2A) at different times during the flight. More specifically, the core network of W-IFC should be selected from these links according to their availabilities throughout the aircraft's flight. However, the ultra-dynamic characteristic of aeronautical networks caused by aircraft's high speed reduces W-IFC's core network selection efficiency. The problems on the core network selection of W-IFC increase the core network selection delay with higher packet losses. Additionally, the core network selection of W-IFC becomes more complex when user traffic heterogeneity is added to this connection availability. These complexities necessitate the continuous monitoring of the aircraft environment while dealing with multiple data entries. At that point, the digital twin (DT) technology enables us a continuous monitoring and management opportunity in a virtual manner for the ultra-dynamic aeronautical environment. By considering this, in this article, we aim to introduce a proof-of-concept (PoC) about the utilization of digital twin technology in WiFi core network selection for IFC. Our proposed DT module executes the hybrid combination by utilizing the connectivity and traffic-based core network selection models simultaneously. Here, the connectivity-based core network selection focuses on determining aircraft's possible core network links, while the traffic-based selection considers heterogeneous traffic flows of passengers. Results reveal that the proposed DT-controlled model reduces the WiFi core network selection delay 36 percent with 25 percent packet delivery improvement. Also, we prove the feasibility of the PoC W-IFC model through twinning rate with near-real-time measurements. And we show the decision performance of DT with false positive and false negative rates.

A Robust Integration Platform of Wi-Fi RTT, RSS Signal, and MEMS-IMU for Locating Commercial Smartphone Indoors

As the cornerstone of indoor location-based services (ILBSs), the smartphone-based real-time locating and tracking technologies are now becoming the key for implementing seamless indoor/outdoor location-based applications. The Wi-Fi received signal strength (RSS)-based positioning system is widely used because of the widespread deployment of Wi-Fi access points in the indoor environment. Correspondingly, the positioning performance of the RSS-based method is limited significantly by the complex and time-varying indoor environment. Contrary to the conventional RSS-based techniques, based on the introduction of a two-way ranging approach in the IEEE 802.11-REVmc <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> protocol, the Wi-Fi round trip time (RTT) ranging technique provides high-resolution and low-latency ranging observation on smartphones. In this work, a robust integration platform and related positioning algorithms of tightly coupled heterogeneous observables from Wi-Fi and MEMS-IMU are developed for smartphone positioning. The proposed framework optimizes the relative and absolute positioning observables in the integration process and improves the accuracy and stability as compared to the solutions, which are based on a single positioning technology. Moreover, the OQECS is established to evaluate the quality of each observation in real time before feeding the data to the adaptive filter. The experimental results demonstrate that the proposed platform achieves improvement in accuracy and robustness in both real-time tests and simulation tests performed using the polluted data. The average positioning accuracy is 0.572 m, which is 20.22% better than the results obtained from a standard EKF method.

Security performance analysis for cell-free massive multiple-input multiple-output system with multi-antenna access points deployment in presence of active eavesdropping

This article investigates the influence of multiple-antenna deployment at access points on physical layer security under cell-free massive multiple-input multiple-output systems. To embody the concept of network multiple-input multiple-output, cell-free massive multiple-input multiple-output employs a great deal of geographically distributed access points, which jointly serve multiple users in the same frequency-time resource. Assuming a malicious active eavesdropper having knowledge of instantaneous channel state information, we derive the asymptotic closed-form expression of the achievable secrecy rate, which is used to evaluate network security performance. A distributed power allocation scheme is developed based on channel fading degrees of different users. All of access points distribute a larger proportion of local transmission power to the users with better channel conditions. We verified that the security performance of the multi-antenna access points outperforms that of the single one. Compared to equal power allocation, the proposed power control algorithm can further boost the network security performance.

Joint Power Control and Passive Beamforming in Reconfigurable Intelligent Surface Assisted User-Centric Networks

As a promising technology with disruptive innovation, user-centric network can meet the data traffic demand and user service demand in the future mobile networks. It completely transforms the traditional cell-centric paradigm into user-centric paradigm, requiring the deployment of a large number of access points (APs). However, the massive deployment of APs leads to issues such as high hardware cost, huge power consumption and complex interference management. Reconfigurable intelligent surfaces (RIS), as another promising technology, can expand signal coverage, suppress interference, reduce hardware cost and power consumption. Inspired by this, we propose a novel RIS assisted user-centric network that uses RIS to replace some low utilization APs to reduce cost as well as power consumption, and deploy more RIS to achieve energy-efficient user-centric communication. In order to take full advantages of RIS, we jointly optimize passive beamforming at RIS and power control at AP to maximize the energy efficiency of the network. Since this problem is intractable and non-convex, an effectively alternating optimization algorithm, capitalizing on fractional programming and successive lower-bound maximization is proposed. The simulation results verify that the proposed algorithm outperforms reference algorithms in terms of energy efficiency and sum rate.