CSIscan: Learning CSI for Efficient Access Point Discovery in Dense WiFi Networks

The recent ubiquitous deployment of Wi-Fi access points (APs) has offered vehicles to use the high-speed and low-cost Internet service via the roadside APs. However, the high mobility of vehicles and the limited coverage of APs render some challenges. First, it results in frequent handoffs, thus leading to long delay and low service availability. Second, the available AP sets and their channel quality change dynamically, making the AP selection problem even harder. Therefore, there is a strong need to develop an efficient association control mechanism that provides efficient vehicular Wi-Fi access. In this paper, we present a theoretical framework to formulate the optimal association problem through non-linear integer programming, whose objective function is to maximize the throughput or to minimize the handoff overhead. We show that this problem holds the totally unimodular (TU) property and is thus solvable in polynomial time. Then, we study the optimality of association control by comparing existing online algorithms through real trace-based simulations. The results show that there exists a large performance gap between the performance of existing online algorithms and the optimal one. We also observe that the association control algorithm can be further improved if it has access to future knowledge. Particularly, the offline optimal with future AP information improves the performance of the local optimal by up to 10%.

Next-generation Wi-Fi 8 (IEEE 802.11bn) targets ultra-high reliability (UHR) by introducing coordinated beamforming (CoBF). In dense networks with multiple access points (APs), simultaneous downlink (DL) multi-user MIMO (MU-MIMO) transmissions from multiple APs can cause severe intra-basic service set (intra-BSS) and inter-BSS interference. CoBF aided by only partial channel state information (CSI) feedback through medium access control (MAC) layer frame exchange is envisioned to support concurrent DL transmission with mitigated physical-(PHY-)layer interference. To improve the network throughput, not only the interference mitigation algorithm design requires careful design but also the selection of optimal AP CoBF clusters is crucial for dense AP deployments. This paper presents a cross-layer solution combining PHY and MAC layer design to optimize AP cluster formation for Wi-Fi 8 CoBF. At the PHY layer, we introduce two beamforming nulling strategies: full nulling, which completely cancels all intra-BSS and inter-BSS interference when sufficient spatial degrees of freedom are available, and partial nulling, which is used under limited degrees of freedom to reduce interference as much as possible. Based on this, we formulate the cross-layer problem that aims to optimize the network throughput, to which we propose an exact linear programming (LP) optimization to determine the optimal AP cluster formation. A greedy clustering algorithm is proposed as a low-complexity alternate. Simulation results demonstrate that the proposed CoBF approach significantly mitigates interference and achieves substantial throughput gains in dense AP scenarios. Furthermore, the LP-optimized AP clustering yields the higher network throughput than the greedy heuristic and mixed integer linear programming (MILP) by up to 12% and 26%, highlighting the benefits of global optimization in terms of performance and time complexity.

ABSTRACTUnderstanding the potential role of Smartphones and other portable wireless devices as relay nodes in message dissemination and content delivery in Delay Tolerant and Opportunistic networks depend on the knowledge about patterns and the number of encounter events among mobile nodes. One of the main challenges for extracting encounters is overlapping in the radio coverage areas of nearby access points (APs). Data about the usage of Wi-Fi networks can be used to perform an analysis of encounters among mobile devices. A realistic estimation of the number of encounters among mobile nodes is now a big challenge. In this paper, the effects of overlapping of radio coverage area among multiple APs for extracting realistic encounters among mobile devices has been discussed, and also an analytical approach has been proposed for extracting realistic encounters from overlapping in the coverage areas of multiple nearby APs. A significant difference was observed between the number of encounters by considering and ignoring overlapping. Our study finds that Wi-Fi datasets are not reliable source to estimate the number of encounters when there are overlapping in radio coverage areas of multiple APs.

The past few decades have seen significant advancements in WiFi indoor localization leveraging fine-grained Channel State Information (CSI). However, existing methods often require multiple access points (APs) or the target device to share sensing data. This cooperative localization complicates the deployment of practical localization systems. In this paper, we present SniFi, a non-cooperative localization method that seamlessly integrates with existing WiFi infrastructure. Unlike prior methods, SniFi does not require multiple CSI-capable APs or additional user actions. We use a single network interface card (NIC) to sniff WiFi traffic and obtain the angle information needed for localization. For APs that do not support CSI acquisition, we first utilize Beamforming Feedback Information (BFI) to estimate the Angle of Departure (AoD). Then, by analyzing the sniffed packets, we can obtain the CSI and the corresponding Angle of Arrival (AoA) from the target device to the sniffer. During experiments, we also address the challenge of angle ambiguity of the latest Intel AX210 NIC by integrating map constraints and temporal continuity. These techniques together enable us to achieve accurate localization without making changes to existing AP networks. Extensive evaluations across various environments and APs demonstrate that SniFi achieves decimeter-level accuracy in median error.

Mobile edge computing (MEC) is an innovative computing paradigm to enhance the computing capacity of mobile devices (MDs) by offloading computation‐intensive tasks to MEC servers. With the widespread deployment of wireless local area networks, each MD can offload computation task to server via multiple wireless access points (WAPs). However, computation offloading can bring a higher system cost if all users select the same access points to offload their tasks. This study proposes a computation offloading strategy and resource allocation optimisation scheme in a multiple wireless access points network with MEC, which aims to minimise the system cost by providing the optimal computation offloading strategy, transmission power allocation, bandwidth assignment, and computation resource scheduling. The proposed scheme decouples the optimisation problem into subproblems of offloading strategy and resource allocation since the problem is NP‐hard. The offloading strategy involves the optimal access point selection, which is analysed by the potential game. The resource allocation is obtained using Lagrange multiplier. The authors' analysis and simulation results verify the convergence performance of the proposed scheme, and the proposed scheme outperforms the simple resource allocation scheme and the offloading strategy optimisation scheme in terms of the system cost.

Due to its high-dimensional data characteristics, the channel state information (CSI) of Wi-Fi signals has become a strong candidate for use in indoor localization. In addition, machine learning techniques can improve the accuracy of indoor localization systems using multiview CSI data received at multiple access points (APs). However, in complex environments, most CSI views collected at APs in non-line-of-sight (NLoS) configurations relative to a transmitter may lose so much useful data information as to become nonsalient. In this paper, we propose a practical machine learning approach named unsupervised view-selective deep learning (UVSDL), in which only the most salient CSI view is selected in an unsupervised manner to be applied in regression for localization. In an experiment in a complex building, our variational deep learning (VDL)-based regression method with the most salient CSI view achieves a localization accuracy of 1.36 m, significantly outperforming the best-known system BiLoc by 25 %.

The prevalence of WiFi infrastructures has enabled the advancement of a wide range of WiFi-based intelligent applications. Behavioral sensing, as an increasingly popular example, is attracting growing research interest in developing a device-free, accurate, and privacy-friendly interaction. Recent advances in behavioral research have shown great promise, but most of existing systems fail to capture the full scope of the relationship between WiFi signals and behaviors. In this work, we propose a behavioral sensing framework by modeling channel state information (CSI) of WiFi signals. The key insight is to harness antenna diversity and model signals reflected off the human body from multiple WiFi access points (APs) to infer daily activities. To this end, a CSI tensor is first constructed by aggregating the CSI from all WiFi APs. Next, a CANDECAMP/PARAFAC (CP) approach is leveraged for factorizing this CSI tensor into several single-rank tensors as key features. Then, a features optimization strategy is applied to the extracted features for selecting those correlated and dominant features by an embedded graph learning and constraint. Finally, a recurrent neural network (RNN) model is used to take the optimized features and capture a robust mapping between the input and output for recognizing daily activities in homes without requiring a prior calibration and any explicit involvement from the users. We design and implement a behavioral sensing prototype reusing existing WiFi and then carry out comprehensive experiments in two indoor environments. Extensive experimental results suggest that our scheme can accurately and effectively recognize the users’ behaviors and also outperforms existing ones.

Overlaid cellular technology has been considered as a promising candidate to enhance the capacity and extend the coverage of cellular networks, particularly indoors. The deployment of small cells (e.g. femtocells and/or picocells) in an overlaid setup is expected to reduce the operational power and to function satisfactorily with the existing cellular architecture. Among the possible deployments of small-cell access points is to manage many of them to serve specific spatial locations, while reusing the available spectrum universally. This contribution considers the aforementioned scenario with the objective to serve as many active users as possible when the available downlink spectrum is overloaded. The case study is motivated by the importance of realizing universal resource sharing in overlaid networks, while reducing the load of distributing available resources, satisfying downlink multi-channel assignment, controlling the aggregate level of interference, and maintaining desired design/operation requirements. These objectives need to be achieved in distributed manner in each spatial space with as low processing load as possible when the feedback links are capacity-limited, multiple small-cell access points can be shared, and data exchange between access points can not be coordinated. This contribution is summarized as follows. An adaptive downlink multi-channel assignment scheme when multiple co-channel and shared small-cell access points are allocated to serve active users is proposed. It is assumed that the deployed access points employ isotropic antenna arrays of arbitrary sizes, operate using the open-access strategy, and transmit on shared physical channels simultaneously. Moreover, each active user can be served by a single transmit channel per each access point at a time, and can sense the concurrent interference level associated with each transmit antenna channel non-coherently. The proposed scheme aims to identify a suitable subset of transmit channels in operating access points such that certain limits on the aggregate interference or number of serving access points are satisfied, while reducing the load of processing. The applicability of the results for some scenarios, including the identification of interference-free channels in operating access points is explained. Numerical and simulations results are shown to clarify achieved gains with the use of the proposed scheme under various operating conditions.

Optimizing the Access Point (AP) deployment has a great role in wireless applications due to the need for providing an efficient communication with low deployment costs. Quality of Service (QoS), is a major significant parameter and objective to be considered along with AP placement as well the overall deployment cost. This study proposes and investigates a multi-level optimization algorithm called Wireless Optimization Algorithm for Indoor Placement (WOAIP) based on Binary Particle Swarm Optimization (BPSO). WOAIP aims to obtain the optimum AP multi-floor placement with effective coverage that makes it more capable of supporting QoS and cost-effectiveness. Five pairs (coverage, AP deployment) of weights, signal thresholds and received signal strength (RSS) measurements simulated using Wireless InSite (WI) software were considered in the test case study by comparing the results collected from WI with the present wireless simulated physical AP deployment of the targeted building - Computer Science Department at University of Baghdad. The performance evaluation of WOAIP shows an increase in terms of AP placement and optimization distinguished in order to increase the wireless coverage ratio to 92.93% compared to 58.5% of present AP coverage (or 24.5% coverage enhancement on average).

As smartphones are increasingly popular, location-based services (LBSs) have become one of the crucial applications in daily lives. While outdoor localization is relatively easy leveraging GPS signals, localization in indoor environments is difficult due to the lack of GPS. Thus, due to the pervasive deployment of WiFi access points, there have been numerous studies on WiFi based indoor positioning. However, the multi-path fading of WiFi signals causes time-varying received signal strengths of WiFi signals, which leads to poor accuracy of WiFi localization. Moreover, WiFi scanning period, about 3∼4 seconds in general smartphone, may provide poor quality of services in the context of refreshment interval. Motivated by these limitations, we study the usability of tw o other sources that are currently available in smartphones: magnetic field strength and cellular signal strength, for indoor positioning purposes. Our preliminary measurements show that these two sources satisfy three properties needed for localization: time-in-variance, location representativeness and universality. Because these three sensors have their own characteristics, some problems in single sensory data based localization could be complementarily overcome. We will show how to combine the three smartphone sensory data for indoor positioning based on each module's characteristics, and how much accuracy is achieved by the hybrid localization.

As the demand for high-throughput communications in wireless LANs increases, the need for expanding channel bandwidth also increases. However, the use of wider band channels results in the decrease of the number of channels available. Therefore, if multiple access points (APs) are in proximity and the cells overlap, it is difficult for each AP to use an orthogonal channel, and competitions thus increase between the APs using the same channel. To cope with this problem, this paper proposes coordinating APs to increase throughputs at Nash equilibria. A centralized controller coordinates the APs, which jointly select multibandwidth channels. To clarify the effect of coordinating APs, we assume a simple scenario where the cells of three APs overlap, and each AP can select multibandwidth channels. In this scenario, we consider two packet length assumptions. In the first case, the data bit amount of each packet is assumed to be the same. Thus, the packet length changes depending on a data rate. The other case assumes that the packet length is the same regardless of the data rate. Through game-theoretic analysis, it can be clarified that the coordinated APs are able to select channels more effectively than if each AP independently selects channels and that the total throughput of the coordinated APs at Nash equilibria is improved by at most 80 % in two packet length assumptions.

The access point (AP) deployment is a fundamental task for constructing an accurate localization system. Existing literature mainly deals with the AP placement problem using optimal geometry analysis since the target-AP geometry will affect the localization performance. However, some non-ideal phenomena in practical scenario, e.g., the existence of obstacles, array orientation and path loss, will degrade the accuracy of angle-of-arrival (AoA) estimation as well as the localization accuracy. In this article, we reformulate the AP planning incorporating these factors. We decompose the problem into two subproblems, namely AP selection problem and error minimization problem. The AP selection problem selects the minimum number of APs to satisfy a desired localization accuracy, aided by a refined orientation updating procedure. We design a centralized and a distributed error minimization algorithm to further decrease the localization error. The centralized algorithm shows superiority in time efficiency. Nevertheless, the case with large number of APs may lead to excessive computational cost. Accordingly, we further devise the distributed algorithm which is adaptive to large-scale deployment. Numerical studies in indoor environments with barriers are conducted to verify our proposed approach.

In this paper, we optimize the slot allocation for uplink communication in a network comprising multiple VLC based access points (APs) and an RF-AP in an indoor environment. Instead of connecting a node to either a single VLC-AP or an RF-AP, node exploits the resources of multiple APs. An optimization problem is formulated for the allocation of these resources with the objective of increasing the overall network throughput. As a result, we get a resource allocation scheme that utilize multiple VLC and RF APs (MV + RF), in contrast to previous schemes that optimize resource allocation utilizing the resources of a single VLC and use RF for overloaded traffic, namely (SV O RF). We compare the performance of the proposed (MV + RF ) with (SV O RF) and (MV O RF) in which a node is assigned resources from multiple VLC-APs and RF-AP is utilized only for overloaded traffic. The results demonstrate that the proposed scheme outperforms other schemes both at the node and the network level and achieves on average 26 to 35% improved throughput over different parameter settings.

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.

This paper analyzes the ergodic capacity of a user-centric virtual cell (VC) dense network, where multiple access points (APs) form a VC for each user equipment (UE) and transmit data cooperatively over millimeter wave (mmWave) channels. Different from traditional microwave radio communications, blockage phenomena have an important effect on mmWave transmissions. Accordingly, we adopt a distance-dependent line-of-sight (LOS) probability function and model the locations of the LOS and non-line-of-sight (NLOS) APs as two independent non-homogeneous Poisson point processes (PPP). Invoking this model in a VC dense network, new expressions are derived for the downlink ergodic capacity, accounting for: blockage, small-scale fading and AP cooperation. In particular, we compare the ergodic capacity for different types of fading distributions, including Rayleigh and Nakagami. Numerical results validate our analytical expressions and show that AP cooperation can provide notable capacity gain, especially in low-AP-density regions.