Due to the emergence of various wireless sensing technologies, numerous positioning algorithms have been introduced in the literature, categorized intogeometry-driven positioning(GP) anddata-driven positioning(DP). These approaches have respective limitations, e.g., a non-line-of-sight issue for GP and the lack of a high-dimensional and labeled dataset for DP, which could be complemented by integrating both methods. To this end, this paper aims to introduce a novel principle calledcombinatorial data augmentation(CDA), a catalyst for the two approaches’ seamless integration. Specifically, GP-based data samples augmented from different positioning element combinations are calledpreliminary estimated locations(PELs), which can be used as high-dimensional inputs for DP. We confirm the CDA’s effectiveness from field experiments based on WiFiround-trip times(RTTs) andinertial measurement units(IMUs) by designing several CDA-based positioning algorithms. First, we show that CDA offers various metrics quantifying each PEL’s reliability, thereby extracting important PELs for WiFi RTT positioning. Second, CDA helps compute the observation error covariance matrix of a Kalman filter for fusing two position estimates derived by WiFi RTTs and IMUs. Third, we use the important PELs and the above position estimate as the corresponding input feature and the real-time label for fingerprint-based positioning as a representative DP algorithm. It provides accurate and reliable positioning results, with an average positioning error of 1.58 (m) and a standard deviation of 0.90 (m).

This paper introduces a novel approach, mobility-induced graph learning (MINGLE), to enhance the accuracy of Wi-Fi positioning. Traditional Wi-Fi positioning methods often struggle with accuracy due to obstructions and interference. MINGLE addresses these challenges by converting user movement patterns into graphs, which are then analyzed using graph neural network. This method involves creating two types of graphs, based on the time and direction of user mobility, and employs a novel cross-graph learning technique in conjunction with self-supervised learning. This approach has demonstrated significant improvements in positioning accuracy, achieving a remarkable accuracy of 1.301 (m) in an underground parking lot setting, without relying on labeled data samples.

A smartphone-based user mobility tracking could be effective in finding his/her location, while the unpredictable error therein due to low specification of built-in inertial measurement units (IMUs) rejects its standalone usage but demands the integration to another positioning technique like WiFi positioning. This paper aims to propose a novel integration technique using a graph neural network called Mobility-INduced Graph LEarning (MINGLE), which is designed based on two types of graphs made by capturing different user mobility features. Specifically, considering sequential measurement points (MPs) as nodes, a user’s regular mobility pattern allows us to connect neighbor MPs as edges, called time-driven mobility graph (TMG). Second, a user’s relatively straight transition at a constant pace when moving from one position to another can be captured by connecting the nodes on each path, called a direction-driven mobility graph (DMG). Then, we can design graph convolution network (GCN)-based cross-graph learning, where two different GCN models for TMG and DMG are jointly trained by feeding different input features created by WiFi RTTs yet sharing their weights. Besides, the loss function includes a mobility regularization term such that the differences between adjacent location estimates should be less variant due to the user’s stable moving pace. Noting that the regularization term does not require ground-truth location, MINGLE can be designed under semi- and self-supervised learning frameworks. The proposed MINGLE’s effectiveness is extensively verified through field experiments, showing a better positioning accuracy than benchmarks, say mean absolute errors (MAEs) being 1.510 (m) and 1.077 (m) for self- and semi-supervised learning cases, respectively.

Precise positioning has become one core topic in wireless communications by facilitating candidate techniques of beyond 5G and 6G. Nevertheless, most existing positioning algorithms, categorized into geometry-driven and data-driven approaches, fail to simultaneously fulfill diversified requirements for practical use, e.g., accuracy, real-time operation, scalability, maintenance, etc. This article aims at introducing a new principle, called combinatorial data augmentation (CDA), a catalyst for tightly integrating geometry and data-driven approaches. We first explain the concept of CDA and its critical advantages over the two standalone approaches, followed by validating its effectiveness by field experiments with WiFi round-trip time and inertial measurement units.

Recently, round-trip time (RTT) measured by a fine-timing measurement protocol has received great attention in the area of WiFi positioning. It provides an acceptable ranging accuracy in favorable environments when a line-of-sight (LOS) path exists. Otherwise, a signal is detoured along with non-LOS (NLOS) paths, making the resultant ranging results different from the ground truth, called an RTT bias, which is the main reason for poor positioning performance. To address it, we aim at leveraging the user mobility trajectory detected by a smartphone’s inertial measurement units, called pedestrian dead reckoning (PDR). Specifically, PDR provides the geographic relation among adjacent locations, guiding the resultant positioning estimates’ sequence not to deviate from the user trajectory. To this end, we describe their relations as multiple geometric equations, enabling us to render a novel positioning algorithm with acceptable accuracy. Depending on the mobility pattern being linear or arbitrary, we develop different algorithms divided into two phases. First, we can jointly estimate an RTT bias of each access point (AP) and the user’s step length by leveraging the geometric relation mentioned above. It enables us to construct a user’s relative trajectory defined on the concerned AP’s local coordinate system. Second, we align every AP’s relative trajectory into a single one, calledtrajectory alignment, equivalent to transformation to the global coordinate system. As a result, we can estimate the sequence of the user’s absolute locations from the aligned trajectory. Various field experiments extensively verify the proposed algorithm’s effectiveness that the average positioning error is approximately 0.369 (m) and 1.705 (m) in LOS and NLOS environments, respectively.

As the number of smartphone users exploded, the demand for Location-Based Service (LBS) has increased. It is important for the LBS to specify the user location by utilizing the sensor built in the smartphone. Unlike outdoor localization, which can employ GPS, there are many challenging issues in indoor localization including non-line-of-sight (NLOS) and multipath effect. In our paper, we focus on Wi-Fi Fine Timing Measurement (FTM) which is a new function of the Android Pie Operating System (OS). We propose line-of-sight (LOS) identification algorithms applicable to Wi-Fi FTM and apply these algorithms to indoor localization based on multilateration methods. We utilize a hypothesis test framework and Support Vector Machine (SVM) to identify LOS signals. We divide LOS/NLOS signals as low and high-quality signals according to the degree of multipath error. We achieve high-quality signals identification rate of 92.4% on average in the sample size 99 and of 78.3% on average in the sample size 29. Therefore, we obtain a 24.4% localization performance improvement compared to the perfect LOS detector by using only high-quality signals to localization.

Device-to-device (D2D) communication enables us to spread information in the local area without infrastructure support. In this paper, we focus on information spreading in mobile wireless networks where all nodes move around. The source nodes deliver a given information packet to mobile users using D2D communication as an underlay to the cellular uplink. By stochastic geometry, we derive the average number of nodes that have successfully received a given information packet as a function of the transmission power and the number of transmissions. Based on these results, we formulate a redundancy minimization problem under the maximum transmission power and delay constraints. By solving the problem, we provide an optimal rule for the transmission power of the source node.

We study bidding and pricing competition between two spiteful mobile network operators (MNOs) with considering their existing spectrum holdings. Given asymmetric-valued spectrum blocks are auctioned off to them via a first-price sealed-bid auction, we investigate the interactions between two spiteful MNOs and users as a three-stage dynamic game and characterize the dynamic game's equilibria. We show an asymmetric pricing structure and different market share between two spiteful MNOs. Perhaps counter-intuitively, our results show that the MNO who acquires the less-valued spectrum block always lowers his service price despite providing double-speed LTE service to users. We also show that the MNO who acquires the high-valued spectrum block, despite charing a higher price, still achieves more market share than the other MNO. We further show that the competition between two MNOs leads to some loss of their revenues. By investigating a cross-over point at which the MNOs' profits are switched, it serves as the benchmark of practical auction designs.

In mobile communication services, users wish to subscribe to high-quality service at a low price level, which leads to competition between mobile network operators (MNOs). The MNOs compete with each other by service prices after deciding the extent of investment to improve quality of service. Unfortunately, the theoretic backgrounds of price dynamics are not known to us, and as a result, effective network planning and regulative actions are hard to make in the competitive market. To explain this competition in more detail, we formulate and solve an optimization problem applying the two-stage Cournot and Bertrand competition model. Consequently, we derive price dynamics that the MNOs increase and decrease their service prices periodically, which completely explains the subsidy dynamics in the real world. Moving forward, to avoid this instability and inefficiency, we suggest a simple regulation rule, which leads to a Pareto-optimal equilibrium point. Moreover, we suggest regulator's optimal actions corresponding to user welfare and the regulator's revenue.

One of the challenges facing the next-generation wireless networks is to cope with the expected demand for data. This calls for an efficient spectrum regulation that can enable mobile subscribers to support high quality of service (QoS) and mobile network operators (MNOs) to leverage their profit streams. In this paper, we present a new spectrum allocation policy in a monopoly situation. The problem is formulated as a Stackelberg game. We show that the conventional spectrum leasing contract may lead to the unprecedented scenario in which costs outweigh their revenues. On the other hand, our proposed spectrum leasing contract can not only maximize user welfare but also leverage MNO's profit streams. We show that our spectrum leasing contract can increase user welfare and MNO's profit up to 75% and 20%, respectively, relative to the conventional spectrum leasing contract. Thus, regulators must rewrite their spectrum allocation policy in order to maximize user welfare and leverage MNO's profit streams.

Main purpose of this paper is to investigate the frequency allocation schemes combined with a downlink Coordinated Multi-Point (CoMP) joint transmission system. We suggest 6-sector directional antenna and according sector based frequency reuse scheme for CoMP system, and compare the edge user performance of conventional Fractional Frequency Reuse (FFR) systems and suggested CoMP system. The numerical results show that the suggested scheme is better than the conventional FFR systems in the performance and the energy efficiency perspectives.

In recent years, to satisfy people's need for wireless services, the number of access points of the 3G-based systems, WiFi and WiMAX has been increased exponentially by wireless service providers (WSPs). As a result, there are many WSPs coexisting in the same hotspot area, which drives price competition among WSPs. Existing researche shows that each WSP will lower its price to increase revenue or market share, and this kind of price competition will eventually damage every WSP with the revenue decrease. However in this paper, we show that there is another type of price competition, where the WSPs' decreasing or increasing price levels occurs periodically and there is no equilibrium point. We illustrate it by using an example of the duopoly price competition and suggest a simple regulation rule that leads to an equilibrium point. Moreover, we show that the equilibrium point is Pareto-optimal and is well balanced in the aspects of total revenue, fairness and social welfare.