Access Point Selection Method Research Articles

User-Centric Cell-Free Massive MIMO System for Indoor Industrial Networks

The cell-free massive multiple-input multiple-output (CFmMIMO) aims to provide uniform quality of service (QoS) for all users, and can be used in small-area scenarios such as indoor industrial networks. This paper studies the CFmMIMO system for indoor industrial scenarios. Firstly, an access point (AP) grouping based hierarchical network topology is proposed. Based on this, we propose an effective AP selection method. To reduce pilot contamination, a pilot assignment scheme based on inspection robot (IR) location is proposed. Considering the high reliability requirement of industrial data transmission, the power control and backhaul combining are jointly optimized to maximize the minimum signal to interference plus noise ratio (SINR). The scalability of the proposed CFmMIMO system is analyzed, and a scalable power control method is proposed. The simulations demonstrate the effectiveness of the AP selection method, pilot assignment scheme, and the joint optimization algorithm for power control and backhaul combining. Moreover, the impact of network scale and network load on system performance is evaluated and analyzed in the simulations.

User-Centric Access Point Selection in Cell-Free Massive MIMO Systems: A Game-Theoretic Approach

In this letter, we investigate user-centric access point (AP) selection for cell-free massive multiple-input multiple-output (MIMO) systems. In contrast to existing works, we study this problem from the perspective of game theory and model user-centric AP service cluster formation as a local altruistic game, which is proven to be an exact potential game (EPG) and to have at least one pure Nash equilibrium (NE). Then, an improved maximum non-neighbor-set-based concurrent spatial adaptive play (MNSB-C-SAP) algorithm is proposed to achieve the NE. Simulation results show that the proposed algorithm outperforms existing algorithms in terms of convergence speed and verify the superiority of the game-theoretic AP selection method compared with some existing approaches.

A New Method of User Association in Wireless Mesh Networks

The IEEE 802.11 based wireless mesh networks (WMNs) are becoming the promising technology to provide last-mile broadband Internet access to the users. In order to access the Internet through the pre-deployed WMN, the user has to associate with one of the access points (APs) present in the network. In WMN, it is very common that the user device can have multiple APs in its vicinity. Since the user performance majorly depends on the associated AP, how to select the best AP is always remaining as a challenging research problem in WMN. The traditional method of AP selection is based on received signal strength (RSS) and it is proven inefficient in the literature as the method does not consider AP load, channel conditions, etc. This paper proposes a new method of user association in WMN such that the user selects the AP based on achievable end-to-end throughput measured in the presence of other interfering APs. The proposed association metric is independent of routing protocol and routing metric used in WMN. The simulation results show that our method outperforms the RSS based AP selection method in WMN.

A Novel Indoor Fingerprint Localization System Based on Distance Metric Learning and AP Selection

A desirable fingerprint-based indoor localization (FIL) system aims to achieve an accurate positioning result within an acceptable time consumption, which is still challenging for application. Building a practical FIL system is a composite task of feature extraction and location estimation, resulting in related methods that is often hard to consider both the positioning accuracy and time consumption. This article proposes a novel FIL system that uses a combination of distance metric learning (DML) and access point (AP) selection method to tradeoff the positioning accuracy and time consumption. Specially, we first abstract the localization process to develop a mathematical model from the perspective of probability theory and reveal the significant impact of the received signal strength (RSS) similarity comparison on FIL. Then, we propose a perturbation theory-based AP selection method to select the best-position-discrimination AP subset from all to reduce the positioning time consumption. Meanwhile, we propose a DML-based method to extract the RSS distribution which involves the indoor environmental information, and further use it in RSS fingerprint similarity comparison to improve the positioning accuracy. We introduce the signal path-loss model into the proposed method for training to get the best similarity metric function. Finally, experimental results show that both the positioning accuracy and the time consumption are comparatively improved in the online phase by the proposed FIL system.

MAPS: Indoor Localization Algorithm Based on Multiple AP Selection

In recent years, indoor fingerprint-based localization algorithm has been widely used by applications on smart phone. In these localization algorithms, it’s very popular to use WiFi signal characteristics to represent the location fingerprint. With the fast popularization of WiFi, the WiFi access points (APs) could be seen everywhere. However, as the number of APs increases, the dimension of the fingerprint and the complexity of fingerprint-based localization algorithm subsequently increase. Responding to the above challenges, this paper proposes a novel indoor localization algorithm MAPS (indoor localization algorithm based on multiple access point selection). MAPS could effectively reduce the complexity of localization computation, and improve the performance of localization through AP selection method. With the first round AP selection, MAPS can obtain a stable subset of AP, thus reducing the dimension of fingerprint, and obtaining better discrimination. And with the second round of AP selection, AP subset could be further condensed to construct a decision tree in each location cluster. This step can further improve the localization performance. The experimental results shown, as compared with classical indoor localization algorithm, MAPS has better positioning accuracy, and could achieve the accuracy of over 90% within 2m location error.

A two-tier machine learning-based handover management scheme for intelligent vehicular networks

With the increasing demand for real-time road safety services and infotainment applications on vehicles, the development of an efficient wireless mobile communication became crucial for the content delivery of such services in Intelligent Vehicular Networks (IVN). Mobility management enables mobile hosts to communicate over the Internet from foreign networks. However, vehicles' high mobility and the rapid shifts in network topology affect the performance of traditional mobility management protocols. Hence, raising the challenge for seamless wireless communications over IVN. In this paper, we propose a two-tier Machine Learning-based scheme for handover management in intelligent vehicular networks. In the first tier, we use a recurrent neural network model to predict the receiving signal strength of Access Points (APs), to derive a handover trigger decision. In the second tier, a stochastic Markov model is used to select the next access point by utilizing the vehicle flow projections. The performance of the proposed protocol is evaluated using NS-2 simulator and generated vehicles mobility. Simulation results show that the proposed ML-based model outperformed related work in term of prediction accuracy, while the integration of the handover trigger scheme and the access point selection method improved network performance.

Assistive Approach for Pedestrian Dead Reckoning Based on RSSI Using Access Point Selection Method

Assistive Approach for Pedestrian Dead Reckoning Based on RSSI Using Access Point Selection Method

Access Point Selection for Hybrid Li-Fi and Wi-Fi Networks

Hybrid light fidelity (Li-Fi) and wireless fidelity (Wi-Fi) networks are an emerging technology for future indoor wireless communications. This hybrid network combines the high-speed data transmission offered by visible light communication and the ubiquitous coverage of radio-frequency techniques. While a hybrid network can improve the system throughput and users' experience, it also challenges the process of access point selection (APS) due to the mixture of heterogeneous access points. In this paper, the differences between homogeneous and heterogeneous networks regarding APS are discussed, and a two-stage APS method is proposed for hybrid Li-Fi/Wi-Fi networks. In the first stage, a fuzzy logic system is developed to determine the users that should be connected to Wi-Fi. In the second stage, the remaining users are assigned in the environment of a homogeneous Li-Fi network. Compared with the optimisation method, the proposed method achieves a close-to-optimal throughput at significantly reduced complexity. Simulation results also show that our method greatly improves the system throughput over the conventional methods, such as the signal strength strategy and load balancing, at slightly increased complexity.

Liberal method of access point selection in wireless mesh networks

IEEE 802.11-based wireless mesh networks (WMNs) are emerging as the promising technology to provide wireless broadband internet access. In order to access the internet via a pre-deployed WMN, a user has to associate with one of the access points (APs) present in the network. Received signal strength (RSS)-based association method defined by the IEEE 802.11 standard does not consider the channel conditions and the AP load into association process. This paper proposes a novel method of AP selection which considers the channel conditions, AP load and wireless mesh backhaul conditions. As lower-rate user association drops down the performance of higher-rate users already associated with the same AP, our method tries to minimise this negative impact. The proposed AP selection method is scalable and also independent of routing protocol and routing metric used in WMN. Through the simulations, we show that our method performs better than RSS-based AP selection method in terms of improving system throughput in WMNs.

Liberal method of access point selection in wireless mesh networks

IEEE 802.11-based wireless mesh networks (WMNs) are emerging as the promising technology to provide wireless broadband internet access. In order to access the internet via a pre-deployed WMN, a user has to associate with one of the access points (APs) present in the network. Received signal strength (RSS)-based association method defined by the IEEE 802.11 standard does not consider the channel conditions and the AP load into association process. This paper proposes a novel method of AP selection which considers the channel conditions, AP load and wireless mesh backhaul conditions. As lower-rate user association drops down the performance of higher-rate users already associated with the same AP, our method tries to minimise this negative impact. The proposed AP selection method is scalable and also independent of routing protocol and routing metric used in WMN. Through the simulations, we show that our method performs better than RSS-based AP selection method in terms of improving system throughput in WMNs.

Hidden Naive Bayes Indoor Fingerprinting Localization Based on Best-Discriminating AP Selection

Indoor fingerprinting localization approaches estimate the location of a mobile object by matching observations of received signal strengths (RSS) from access points (APs) with fingerprint records. In real WLAN environments, there are more and more APs available, with interference between them, which increases the localization difficulty and computational consumption. To cope with this, a novel AP selection method, LocalReliefF-C( a novel method based on ReliefF and correlation coefficient), is proposed. Firstly, on each reference location, the positioning capability of APs is ranked by calculating classification weights. Then, redundant APs are removed via computing the correlations between APs. Finally, the set of best-discriminating APs of each reference location is obtained, which will be used as the input features when the location is estimated. Furthermore, an effective clustering method is adopted to group locations into clusters according to the common subsets of the best-discriminating APs of these locations. In the online stage, firstly, the sequence of RSS observations is collected to calculate the set of the best-discriminating APs on the given location, which is subsequently used to compare with cluster keys in order to determine the target cluster. Then, hidden naive Bayes (HNB) is introduced to estimate the target location, which depicts the real WLAN environment more accurately and takes into account the mutual interaction of the APs. The experiments are conducted in the School of Environmental Science and Spatial Informatics at the China University of Mining and Technology. The results validate the effectiveness of the proposed methods on improving localization accuracy and reducing the computational consumption.

IEEE 802.11 무선랜 환경에서의 AP 선택 알고리즘

In the IEEE 802.11 Wireless LAN environment, the common Access Point (AP) selection of the existing terminal is based on signal strength. However, the signal strength-based AP selection method does not ensure an optimal data rate. Recently, several AP selection methods to solve this problem have been suggested. However, when we select AP, these have a latency problem and don`t consider dense environments of AP. In this paper, we confirm the problem of the conventional AP selection about the signal strength and the throughput through the actual measurement, and propose algorithm that selects AP by scoring link speed and wireless round trip time to compensate the problem. Furthermore, the proposed AP selection algorithm through the actual experiment proves the improved performance as compared with the existing methods.

A Proposal of Access Point Selection Method Based on Cooperative Movement of Both Access Points and Users

A Proposal of Access Point Selection Method Based on Cooperative Movement of Both Access Points and Users

무선랜 환경에서 IPTV 서비스 품질 향상을 위한 AP 선택 기법

무선랜 환경에서 사용자 데이터 수신율은 RSS (received signal strength) 뿐만 아니라 AP (access point)의 채널 경합 수준에 의하여 결정된다. 그러나 현재 사용자 단말은 AP로부터의 RSS를 기반으로 접속할 AP를 선정하므로 IPTV 서비스의 최소한의 요구사항을 보장할 수 없다. 따라서 본 논문에서는 기존의 IEEE 802.11 표준에 대한 수정없이 무선랜 환경에서 IPTV 서비스 요구사항을 만족시킬 수 있는 효율적인 AP 선택 기법을 제안한다. 사용자 단말은 주변 AP들의 채널 경합 수준과 현재 진행중인 IPTV 서비스의 품질을 모니터링 함으로써, 서비스 품질이 일정이하로 떨어지면 AP 부하정도가 가장 작은 AP로 재접속함으로써 IPTV 서비스의 최소한의 요구사항을 제공하도록 하였다. In a WLAN, the data reception rate of a user station depends not only on the received signal strength (RSS) from an AP (Access Point) but also on the contention level of the AP. However, since only the RSSs measured at the physical layer have been used for a station to select an AP to associate with, the station may not satisfy the minimum quality requirements necessary for a seamless IPTV service. Thus, in this paper, we propose an AP selection method to support quality requirements of the IPTV service in a wireless LAN without modifying the IEEE 802.11 standard. A user station keeps monitoring a contention level of APs around it and a quality of an ongoing IPTV service. If the quality deteriorates below a threshold, the station reassociates with another AP having the least load so that the ongoing IPTV program is played without disruption.

Proactive AP Selection Method Considering the Radio Interference Environment

SUMMARY In the near future, wireless local area networks (WLANs) will overlap to provide continuous coverage over a wide area. In such ubiquitous WLANs, a mobile node (MN) moving freely between multiple access points (APs) requires not only permanent access to the Internet but also continuous communication quality during handover. In order to satisfy these requirements, an MN needs to (1) select an AP with better performance and (2) execute a handover seamlessly. To satisfy requirement (2), we proposed a seamless handover method in a previous study. Moreover, in order to achieve (1), the Received Signal Strength Indicator (RSSI) is usually employed to measure wireless link quality in a WLAN system. However, in a real environment, especially if APs are densely situated, it is difficult to always select an AP with better performance based on only the RSSI. This is because the RSSI alone cannot detect the degradation of communication quality due to radio interference. Moreover, it is important that AP selection is completed only on an MN, because we can assume that, in ubiquitous WLANs, various organizations or operators will manage APs. Hence, we cannot modify the APs for AP selection. To overcome these difficulties, in the present paper, we propose and implement a proactive AP selection method considering wireless link condition based on the number of frame retransmissions in addition to the RSSI. In the evaluation, we show that the proposed AP selection method can appropriately select an AP with good wireless link quality, i.e., high RSSI and low radio