The Impact of Access Points Placement on Indoor Positioning Systems: A Probability-Based Approach

Abstract. The acquisition of locations of Wi-Fi access points (APs) in urban buildings plays an important role in smart city applications, such as indoor navigation and social media data mining. This paper proposes a crowdsourcing-based approach for self-localization of Wi-Fi APs with the assistance of indoor pedestrian network (AP Detector). The features extracted from local opportunity signals are adopted for floor identification, and the crowdsourced indoor trajectories are segmented and matched with extracted indoor pedestrian network for the further trajectory calibration. In addition, the iteration unscented Kalman filter is applied for the location and bias estimation of local Wi-Fi FTM stations using the constructed Wi-Fi ranging model. The experimental results indicate that the proposed AP Detector can realize accurate location estimation of Wi-Fi APs, which also provides an effective way for autonomous construction of indoor navigation database and hybrid localization.

In this paper, we study the physical-layer security in a 3-D multiuser visible light communication (VLC) network. The locations of access points (APs) and mobile users are modeled as two 2-D, independent and homogeneous Poisson point processes at distinct heights. Using mathematical tools from stochastic geometry, we provide a new analytical framework to characterize the secrecy performance in multiuser VLC networks. Closed-form results for the outage probability and the ergodic secrecy rate are derived for networks without AP cooperation. Considering the cooperation among APs, we give tight lower and upper bounds on the secrecy outage probability and the ergodic secrecy rate. To further enhance the secrecy performance at the legitimate user, a disk-shaped secrecy protected zone is implemented in the vicinity of the transmit AP. Based on the obtained results, it is shown that cooperating neighboring APs in a multiuser VLC network can bring performance gains on the secrecy rate, but only to a limited extent. We also show that building an eavesdropper-free protected zone around the AP significantly improves the secrecy performance of legitimate users, which appears to be a promising solution for the design of multiuser VLC networks with high security requirements.

Selective AP probing for indoor positioning in a large and AP-dense environment

How many small cell (SC) access points (APs) are required to guarantee a chosen quality of service in a heterogeneous network? In this chapter, we answer this question considering two different network models. The first is the downlink of a finite-area SC network where the locations of APs within the chosen area are uniformly distributed. A key step in obtaining the closed-form expressions is to generalize the well-accepted moment matching approximation for the linear combination of lognormal random variables. For the second model, we focus on a two-layer downlink heterogeneous network with frequency reuse-1 hexagonal macro cells (MCs), and SC APs that are placed at locations that do not meet a chosen quality of service from macro base stations (BSs). An important property of this model is that the SC AP locations are coupled with the MC coverage. Here, simple bounds for the average total interference within an MC makes the formulation possible for the percentage of MC area in outage, as well as the required average number of SCs (per MC) to overcome outage, assuming isolated SCs. Introduction Heterogeneous cellular networks (HCNs) are being considered as an efficient way to improve system capacity as well as effectively enhance network coverage [1, 2]. Comprising multiple layers of access points (APs), HCNs encompass a conventional macro cellular network (first layer) overlaid with a diverse set of small cells (SCs) (higher layers). Cell deployment is an important problem in heterogeneous networks, both in terms of the number and positioning of the SCs. Traditional network models are either impractically simple (such as the Wyner model [3]) or excessively complex (e.g., the general case of random user locations in a hexagonal lattice network [4]) to accurately model SC networks. A useful mathematical model that accounts for the randomness in SC locations and irregularity in the cells uses spatial point processes, such as Poisson point process (PPP), to model the location of SCs in the network [5–10]. The independent placement of SCs from the MC layer, has the advantage of analytical tractability and leads to many useful SINR and/or rate expressions. However, even assuming that wireless providers would deploy SCs to support mobile broadband services, the dominant assumption remains that SCs are deployed randomly and independent of the MC layer [11].

Precise localization of Wi-Fi Access Point (AP) is becoming increasingly important with the rise of diverse location-based services based on smartphone. In this article, we propose a new method for precise Wi-Fi AP localization using GPS information of smartphone. The idea is that the possible area of Wi-Fi AP location, called AP_Area, is first determined by measuring GPS information and the Received Signal Strength (RSS) of smartphones from Wi-Fi AP. As the number of measurement points from users' smartphones increases, the AP_Area can be successively narrowed down to the actual AP location. Simulation results show that the proposed algorithm can detect the Wi-Fi AP localization within 5 m (probability over than 80%).

With the fast-growing demand for mobile services, where to place the access points (APs) for providing uniformly and appropriately distributed signals in a wireless local area network (WLAN) becomes an important issue in the wireless network planning. Basically, AP placement will affect the coverage and strength of signals in a WLAN. The number and locations of APs in a WLAN are often decided on the basis of the trial-and-error method. Based on this method, the network planner first selects suitable locations to place APs through observation, and then keeps changing the locations to improve the signal strength based on the received signal. Such process is complicated, laborious and time-consuming. To overcome this problem, we investigate the back-propagation neural network (BPNN) algorithm to improve over the traditional trial-and-error method. Without increasing the number of APs, our approach only needs to adjust AP locations to overcome weak signal problems and thus increase the signal coverage for the Internet connection anywhere within the area. In our experiment, we established a WLAN on a C campus. Our experiments also indicate that placing APs according to the BPNN provided better signal coverage and met the students’ demands for connecting to the Internet from anywhere in the classroom.

The deployment of access points (APs) imposes a great impact on the performance of wireless local area networks (WLANs). Although there have been plenty of works on the optimal AP deployment (OAD), however, the issue remains open that how to deploy APs energy-savingly in 3-D environments while considering some practical demands. Therefore, this article investigates the OAD with the objective of minimizing the total AP transmission power while guaranteeing the effective coverage by jointly optimizing the transmission power and location of each AP. Different from existing works, our article fully considers not only real 3-D environments with various obstacles but also the differentiated coverage demand and the restrictions on AP locations. To solve this NP-hard optimization problem, this article first proposes a novel hybrid swarm intelligence optimization algorithm, named PSO-Lévy-DFOA. The computational results on 16 benchmark functions and comprehensive comparisons with other algorithms demonstrate the superior global convergence performance of PSO-Lévy-DFOA. The proposed PSO-Lévy-DFOA-based OAD method is then applied to WLAN planning in a two-story building. It has been shown that the total AP transmission power can be considerably reduced compared to the current deployment while still fulfilling the effective coverage.

This paper focuses on how to optimize the deployment location of the anchor Wi-Fi access points (APs) in order to enhance the indoor positioning performance of the finetime measurement (FTM) protocol. Compared to conventional Wi-Fi positioning, FTM protocol is able to achieve relatively good indoor positioning performance since it estimates distance between a sender and its receiver with the flight duration of Wi-Fi signals. However, the FTM-based positioning accuracy in NLoS environments is still not high, which restricts its application in indoor scenarios. To this end, we formulate an optimization problem to maximize the total number of LoS paths by optimizing the locations of the anchor APs. To solve the problem, we propose a Deep Q-Network (DQN)-based AP deployment optimization method (DQ-WiAPDoM), which utilizes DQN to optimize the locations of APs such that the total number of LoS paths over all covered area is maximized. Experimental results show that DQ-WiAPDoM converges well and is able to reduce the average positioning error efficiently.

In indoor location environment, shadowing, multipath and reflection phenomena make the position estimation inaccurate. Large number of Access Points (APs) equipped with single antenna could obtain enough Receive Signal Strength (RSS) information to describe signal characteristics of the target space. However, it results in a large investment costs. This paper provides a novel indoor positioning solution using the multi-antennas architecture to increase the accuracy of location estimation. The system obtains enough RSS to compute the user location and provides a reasonable cost of deployments in indoor location environment. Experimental and numerical results show that the proposed indoor location estimation demonstrates a high accuracy of positioning and outstanding performance when RSS is collected by smart antenna technique. More specifically, the accuracy is increased from 1.34% to 92.4% and the error variance is reduced by 96% as compared to the traditional location fingerprint method.

Cell-free massive MIMO (CF-mMIMO) provides wireless connectivity for a large number of user equipments (UEs) using access points (APs) distributed across a wide area with high spectral efficiency (SE). The energy efficiency (EE) of the uplink is determined by (i) the transmit power control (TPC) algorithms, (ii) the numbers, configurations, and locations of the APs and the UEs, and (iii) the propagation channels between the APs and the UEs. This paper investigates all three aspects, based on extensive (~30,000 possible AP locations and 128 possible UE locations) channel measurement data at 3.5 GHz. We compare three different TPC algorithms, namely maximization of transmit power (max-power), maximization of minimum SE (max-min SE), and maximization of minimum EE (max-min EE) while guaranteeing a target SE. We also compare various antenna arrangements including fully-distributed and semi-distributed systems, where APs can be located on a regular grid or randomly, and the UEs can be placed in clusters or far apart. Overall, we show that the max-min EE TPC is highly effective in improving the uplink EE, especially when no UE within a set of served UEs is in a bad channel condition and when the BS antennas are fully-distributed.

The most viable strategy to establish a dependable indoor positioning system is by employing the received signal strength (RSS) based fingerprinting technique, which encompasses both the offline and online phases. The offline phase involves constructing a radio map, which can be arduous in vast indoor environments. To tackle this, radio map interpolation is often used to interpolate RSS by utilizing the RSS recorded at a coarser level of known reference points (RPs). This paper proposes a novel RSS-based radio map interpolation to enhance the existing inverse distance weighting (IDW) interpolation technique. The method divides the deployment area into zones and optimizes the density of known RPs in each zone based on the number of access points (APs) with average RSS exceeding the threshold. It allocates higher RP density for the zones with poor AP coverage and reduces it for well-covered zones. Results demonstrates that the proposed method achieves substantial improvements over the baseline IDW scheme in average positioning error of up to 6.58% at the floor level and 3.77% overall.

Positioning services are increasingly used for applications such as navigation, advertising and social media. While outdoor navigation based on GPS and/or cellular systems works well, indoor navigation is a much tougher challenge. This paper proposes an indoor localization using Wi-Fi received signal strength indicator (RSSI) measurements from existing access points. Using RSSI data from multiple access points, additional access points will be added in the system thru linear regression statistical model to create virtual access points (VAPs). This paper fuses the low-cost and flexible system of fingerprint localization with VAP creation and the improvement in accuracy by adapting Kalman Filter (KF) and Particle Filter (Pf). The effectiveness of using KF with VAP has demonstrated positive results in previous research. This paper aims to integrate the use of PF in the system. To improve the performance of indoor localization, the proposed model will be an improved virtual access point localization, proposed filtering (VAP+KF+PF).

Cell-free massive multiple-input multiple-output (MIMO) is a form of distributed massive MIMO aiming to provide massive access and improve spectral efficiency by inheriting favorable properties of traditional massive MIMO, while mitigating detrimental effects such as shadowing and spatially correlated fading. This paper investigates how the throughput of cell-free massive MIMO is affected by non-orthogonal multiple access (NOMA) for the next-generation cellular networks under stochastic access point (AP) and user locations. Thus, we consider homogeneous Poisson point processes (PPPs) to model node locations, while considering a Rayleigh channel with log-distance path loss. The time division duplexing (TDD) mode is employed and uplink channels are estimated autonomously/locally at each AP via uplink pilots sent by users. Moreover, while unique pilots are used between NOMA clusters, pilots are reused within each cluster in order to strike a balance between the training overhead and number of clusters. Matched filter based precoding is performed within the downlink based on the estimated channels. The aggregate signal received from all access points is characterized based on the moment generating function and approximated via moment matching. Thereby, the achievable rates for the users are derived, under the consideration of error propagation due to imperfect successive interference cancellation (SIC). It is shown that NOMA increases the overall rate under environments with low path loss exponents and networks with high access point densities, while careful power allocation can significantly improve user fairness.

Towards a better indoor positioning system: A location estimation process using artificial neural networks based on a semi-interpolated database

Location-based service is one of the most popular buzzwords in the field of U-city. Positioning a user is an essential ingredient of a location-based system on a U-city. For the outdoor positioning, GPS based practical solutions have been introduced. However, GPS measurement error is too big to be used for U-campus services because the size of a campus is relatively smaller than a city. We propose Relative-Interpolation Method in order to improve the correctness of outdoor positioning. Besides, indoor positioning is necessary for U-campus while GPS signal is not available inside buildings. For the indoor positioning, Cricket, Active Badge, and so on have been introduced. These methods require special equipments dedicated for positioning. Our method does not require such equipments because it determines the user’s position based on the receiver signal strength indicators (RSSI) from access points (AP) which are already installed for WLAN. The algorithm we are using for indoor positioning is a kind of finger prints method. However our algorithm builds a decision tree instead of a look-up table in the off-line phase. Therefore, our method is faster than existing indoor positioning methods in the real-time phase. We have integrated our indoor and outdoor positioning methods and implemented a prototype of our indoor-outdoor positioning method on a laptop. Our experimental results are discussed in this paper.