Signal perturbation based support vector regression for Wi-Fi positioning

Indoor localization using fingerprinting techniques became more attracting to researchers in recent years because of their high accuracy. However, unpredictable Received Signal Strength (RSS) is one of the challenges. In our proposed system, it can be reduced by using strong Access Points (APs) selection method to select a subset of reliable APs and decrease the input of feature dimension. In addition, the computational cost due to a large fingerprint database was addressed by using Affinity Propagation clustering algorithm. The efficient indoor localization system relies on Support Vector Regression (SVR) due to its advantage of high generalization ability. The detailed results of real experiments indicate that the proposed system using SVR achieved a high accuracy compared to the conventional indoor algorithm.

It is usually possible to associate with more than one Access Point (AP) in IEEE 802.11 Wireless LANs. AP selection is a crucial issue because the performance achieved by a user heavily depends on the AP selected. A received signal strength (RSS)-based association mechanism is specified by the IEEE 802.11 standard. However, this does not consider the channel conditions and AP load, which leads to a low throughput and a low user transmission rate. An alternative to using the RSS is to exploit the airtime metric, which provides users with information on how busy the channel is, but the results of our simulations show that the airtime metric cannot indicate the exact status of the channel. In this paper, we present a new association framework using channel occupancy in order to provide users with exact channel status information of APs. Via the proposed scheme, users can compare the achievable throughput provided by each AP and select the best AP. We validate our proposed association method via simulation results, which show that the throughput improvement of the method is worthy of notice.

WLAN is now widely used for emergency purposes because of its fast deployment capability. Efficient and intelligent arrangement and selection of access points (AP) are very crucial in emergency situations. Coverage and capacity are two central problems faced when APs are placed in a disaster scene. The paper proposes a power efficient algorithm for AP selection in emergency situations. The current AP selection algorithm only considers received signal strength, and this algorithm arise problems like overloading in APs, congestion and power wastage in networks. But the proposed Dynamic Load Balancing Fairness Algorithm (DLBFA) overcomes all these problems by using a new selection score, obtained by optimizing channel throughput and voice quality. Apart of this score the number of nodes connected to an AP and RSSI are also considered. The main advantage of DLBA is that it can work on both data and voice applications.

In wireless local area networks (WLANs) made up of Extended Service Sets, access point (AP) selection is a key issue to improve the network performance and balance the traffic load. WLANs operating in the shared Industrial, Scientific and Medical band can benefit from the use of cognitive radio (CR) techniques to enable dynamic access to spectrum holes that are free from interference. In this paper, we propose an optimal Green AP Selection (GAPS) scheme, in which AP selection is optimized to maximize the system throughput while minimizing the energy consumption, for multi-rate WLANs enhanced by CRs. Different from most existing AP selection schemes, GAPS takes into account of the state transition tendency of APs and the influence of Automatic Rate Fallback mechanism in the Distributed Coordination Function. The AP selection problem is formulated as a restless bandit problem and solved by the primal-dual index heuristic algorithm based on first order relaxation to yield the GAPS scheme with the "indexability" property and hence a low complexity. GAPS is further divided into offline computation, which accounts for the bulk of the computations, and online selection, with a low complexity to facilitate implementation. Extensive simulation results illustrate the significant performance improvements of GAPS compared with existing AP selection schemes in different scenarios.

In this paper, we propose a surface correlation based fingerprinting method in sparse Wi-Fi environment. The proposed method can estimate location even with very few Wi-Fi APs by using accumulated spatial RSS pattern. We verify performance of the proposed method through several tests in a building with complex paths. Many studies have developed localization technologies that use measurements such as RSS (Received Signal Strength) or RTT (Round Trip Time) for Wi-Fi signal. All RF have signal noise and distortion problems as well as multipath problem in indoor environments. As the distance from the RF signal source increases, the discrimination of the RSS decreases due to RF signal noise and distortion. Therefore, ambiguity of RSS increases as the distance increases, the performance deteriorates. The conventional RF based localization method have tried to maintain dense RF environment to guarantee stable and accurate performance. However, in order to maintain the dense RF environment, it is becoming a barrier to commercialization of localization technology due to high cost of constructing RF infrastructure and maintenance difficulty. In this paper, we propose a Surface Correlation (SC) based fingerprinting technique to maintain high positioning performance even with few Wi-Fi APs. The surface is the accumulated RSS pattern data during the movement of a pedestrian, which means the spatial RSS pattern. Conventional fingerprinting methods using kNN (k-Nearest Neighbors) and WkNN (Weighted kNN) are very vulnerable to signal noise and distortion because they utilize instantaneous RSS data. The performance of Particle Filter (PF) based localization technology is also degraded in areas where Wi-Fi AP is sparsely distributed. However, the proposed method is very robust against signal noise and distortion because it compares the spatial RSS pattern with the reference RSS pattern on the fingerprinting database. This increases localization accuracy and availability for Wi-Fi coverage. For localization, the proposed method conduct correlation process for the spatial RSS pattern of a pedestrian with reference RSS pattern of database. So, the reference RSS pattern should be extracted from a reference trajectory that matches a pedestrian`s walking path. The proposed method utilize a database of ‘link-node’ structure to dynamically generate a reference trajectory in indoor environment with complicated path. It generate candidate reference trajectories using only MAC address and turn event of Pedestrian Dead-Reckoning (PDR). The proposed method fix a reference trajectory between these candidate trajectories through correlation process of measured RSS pattern with each reference RSS pattern. A reference point on the fixed reference having most similar RSS pattern to a pedestrian`s is considered as a current location. In order to improve performance at a fork where RSS ambiguity increases, the proposed method estimates preferentially for a reference trajectory matching with PDR turn event. The turn event can be wrong due to various motion of a pedestrian. The proposed method selects a correct reference trajectory by continuously performing correlation process on candidate reference trajectories. In order to verify the performance of the proposed surface correlation based fingerprinting method, we conducted several tests in a three-story building. Before the tests, we conducted a survey of Wi-Fi environment to construct fingerprinting database in the building. As a result of the survey, 170 APs are distributed in the building. We reduced the number of APs by 20% from 170 to 17, and analyzed performance of the proposed and conventional localization methods. The test results show that the performance of the conventional localization methods degrade as the number of applied APs decreases. The proposed fingerprinting method shows a 3 meter localization error even with very few Wi-Fi APs, and unlike the conventional fingerprinting method, the localization error is bounded to be within 5m due to the use of spatial RSS pattern. In this paper, we presented the fingerprinting method using the surface correlation in sparse Wi-Fi environment. The proposed fingerprinting method accumulates Wi-Fi RSS during movement of a pedestrian. The proposed method compares the spatial RSS pattern, which is accumulated RSS data, with reference RSS patterns of fingerprinting database. By comparing the RSS patterns, the proposed method greatly improves accuracy as well as stability. In addition, it can be effectively applied in a sparse Wi-Fi environment by improving the availability of ambiguous areas of Wi-Fi coverage. Through these results, we expect to be able to provide accurate and reliable location information even in indoor weak RF environment.

Background: Cell Free massive MIMO, containing a very large number of distributed access points (APs), which is a promising technology to provide high data rate, spectral efficiency (SE), and energy efficiency (EE). The system performance of cell-free M-MIMO is maximum when selecting optimal access points (AP) from the large number of APs. The linear precoding methods of zeroforcing (ZF) and minimum mean square error (MMSE) are utilized in this study because they are devoid of self-interference and so improve the system capacity. Objective: The objective of this study is to maximize the system data rate in a cell-free M-MIMO network. Method: To maximize the system data rate, the maximum channel gain-based Access Point Selection (MCGAPS), Distance based Access Point Selection (DAPS), and Random-Access Point Selection (RAPS) algorithms are used to pick access points (APs) in a cell-free M-MIMO network. Because the MCGAPS algorithm selects those APs with the highest channel gain, the system’s rate is improved. Result & Discussion: The DAPS algorithm is used to choose the closest APs to the user. The APs were randomly chosen using RAPS. Random user selection (RUS) algorithm schedules the same number of users. Conclusion: It is observed that the DAPS and RUS algorithms jointly improve the system rate significantly in cell-free massive MIMO system compared to the other proposed algorithms.

This paper presents results for an approach for indoor location estimation that integrates received signal strength (RSS) data from both WiFi and GSM networks. Previous work has focused on relatively small indoor environments. In many potential applications, getting approximate location information, such as in which room the mobile user is, is adequate. A hierarchical clustering method is used to partition the RSS space. To choose the best transmitters in a partition, we assess the amount of RSS variance that is attributable to different base stations (BSs) or access points (APs) by transforming the RSS tuples into principal components (PCs). This allows us to retain most of the useful information of detectable transmitters in fewer dimensions. In our experiments, we collected WiFi and cellular RSS on the 2nd and 3rd-floor electronic engineering (EE) building in Queen Mary campus. The experiment results show that the proposed method can provide a good accuracy of room prediction, especially when we integrate WiFi RSS with GSM RSS together to do the positioning.

In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location; 2) Access Point (AP) selection; and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.

PurposeWireless local area networks (WLANs) are the predominant option for broadband wireless access network, and multiple access points (APs) will be much more available for wireless stations (WSTAs). Call admission control (CAC) on AP selection problem over 802.11 WLAN is a critical issue. In the existing architecture, strongest‐signal‐first is the default AP selection mechanism in 802.11 WLAN which uses the single criterion, received signal strength indicator, to select AP. However, this method suffers from bandwidth deficiency and unbalanced load among APs due to the uneven distribution of user load, thus degrading the system throughput. Instead, the purpose of this paper is to propose a multi‐criteria CAC on AP selection algorithms.Design/methodology/approachThe distributed multi‐criteria considered in order are RSSI, minimum required bandwidth of WSTA, estimated effective bandwidth (EEB) and AP‐WSTA distance. A semiMarkov model considering both packet retransmission limit, packet error rate and collision effect is proposed to predict the system throughput and validated through simulation results. Two multi‐criteria AP selection algorithms after EEB is evaluated are proposed and compared based on this analytical model.FindingsThe proposed algorithms outperform the traditional SSF algorithm in terms of the balance index for AP and the average system throughput.Originality/valveThe paper presents performance analysis for multi‐criteria CAC for distributed access point selection in WLANs.

Fingerprint based Wi-Fi localisation system often takes a lot of human efforts to measure the received signal strength (RSS) of dense grid in an indoor environment. In this paper, we propose a novel fingerprint database construction method with reduced human effort to obtain optimal length of RSS time series and grid division. We verify the chaotic characteristics of RSS time series, and use phase space reconstruction algorithm to calculate the optimal length of RSS data to be collected at each reference point. Then Gaussian process regression (GPR) for fingerprinting based indoor localisation is used to construct the database with information of limited reference points. The hyper-parameters of GPR is calculated by conjugate gradient descent algorithm. The performance of the proposed radio map construction framework is validated in real indoor environment, and with using Bayesian positioning method, the localisation error mean can be 1.5 m while the construction time of radio map is greatly reduced with ensuring accuracy.

With the announcement of IEEE 802.11 standards and the rapid development of the wireless transmission technology, WLANs (wireless local area networks) have become popular due to its low cost and being easy to set up. To achieve an uninterrupted transmission service, handover mechanisms must be carefully designed. However, current handover mechanisms require completely scanning surrounding channels which can result in long scanning delays and thus severely affect the performance of real-time applications. Apart from this, the handover mechanism in the standard specification chooses the new access point (AP) based only on the information of the received signal strength (RSSI). Since the congestion condition on the new AP is not considered in the selection process, the new AP may not it support the required enough transmission rate if it is heavily loaded. In this thesis, we propose to use the information of the round-trip delay between the probe request and the probe response for handover AP selection. Furthermore, the duration of scanning on a specific channel will be properly designed to reduce the scanning delay. We have verified the proposed method through implementation on the driver of the wireless network card. The experiment results show that the proposed method outperforms the standard mechanism on the handover performance.

Fault location identification is an important task to provide reliable service to the customer. Most existing artificial intelligence methods such as neural network, fuzzy logic, and support vector machine (SVM) focus on identifying the fault type, section, and distance separately. Furthermore, studies on fault type identification are focused on overhead transmission systems and not on underground distribution systems. In this paper, a fault location method in the distribution system is proposed using SVM, addressing the limitations of existing methods. Support vector classification (SVC) and regression analysis are performed to locate the fault. The method uses the voltage sag data during a fault measured at the primary substation. The type of fault is identified using SVC. The fault resistance and the voltage sag for the estimated fault resistance are identified using support vector regression (SVR) analysis. The possible faulty sections are identified from the estimated voltage sag data and ranked using the Euclidean distance approach. The proposed method identifies the fault distance using SVR analysis. The performance of the proposed method is analyzed using Malaysian distribution system of 40 buses. Test results show that the proposed method gives reliable fault location.

This paper proposes a cell-free massive multiple-input multiple-output (CF-mMIMO) architecture with joint list-based detection with soft interference cancellation (soft-IC) and access points (APs) selection. In particular, we derive a new closed-form expression for the minimum mean-square error receive filter while taking the uplink transmit powers and APs selection into account. This is achieved by optimizing the receive combining vector by minimizing the mean square error between the detected symbol estimate and transmitted symbol, after canceling the multi-user interference (MUI). By using low-density parity check (LDPC) codes, an iterative detection and decoding (IDD) scheme based on a message passing is devised. In order to perform joint detection at the central processing unit (CPU), the access points locally estimate the channel and send their received sample data to the CPU via the front haul links. In order to enhance the system's bit error rate performance, the detected symbols are iteratively exchanged between the joint detector and the LDPC decoder in log likelihood ratio form. Furthermore, we draw insights into the derived detector as the number of IDD iterations increase. Finally, the proposed list detector is compared with existing detection techniques.

An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms.

As a simple and inexpensive channel characteristic, received signal strength (RSS) finds extensive usage in localization applications. However, the quick changes in signal strength impact the localization precision. By averaging over access points (APs) with multiple frequencies and/or heights, this article suggests a novel approach to lowering RSS fluctuation. Initially focused on the plane-earth loss model, the study was later extended to include a multipath indoor propagation scenario that was simulated. We used ray-tracing software to model the indoor propagation situation. This research takes into account the results of three distinct methods for averaging RSS: height averaging, frequency averaging, and hybrid frequency and height (FH) averaging, which combines the two. We discovered that the Height-only strategy considerably decreased the RSS variation with distance for both settings we looked at. Using the frequency-only method even further reduced the variation. Using the Hybrid FH technique greatly enhances the results. Root mean square error values of 4.427 dB, 3.70 dB, and 3.5 dB, respectively, are provided for the averaging approaches and the ideal scenario in which no variance occurs. Another finding is that averaging with APs that have double the height or frequency will not improve the RSS distance variation.