Dense Wi-Fi Research Articles

Optimal channel assignment on dense Wi-Fi networks using Thermodynamic Threshold Accepting

Channel assignment in Wi-Fi 6 dense networks is a challenging problem which critically impacts the performance of WLAN communications. Although there have been many proposals in the area of IEEE 802.11 channel assignment, LCCS has emerged as the de facto standard for distributed channel selection. Notwithstanding, centralized optimizers overcome the performance of distributed channel assignment algorithms, so their study is of utmost importance. One of the most successful optimization proposals in the state-of-the-art for Wi-Fi 4 is based on simulated annealing (SA). However, SA presents a significant limitation: its parameters must be adjusted manually for each scenario, otherwise yielding suboptimal solutions. In this paper, we propose a new technique for channel assignment optimization of Wi-Fi dense networks, called Thermodynamic Threshold Accepting (TTA), including modifications inspired by Threshold Accepting (TA) and Thermodynamic Simulated Annealing (TSA). With respect to SA, TTA avoids manual tuning, being able to adapt to the specific setting automatically. We evaluate TTA in a dense Wi-Fi model and, compared to SA, we show that our approach reaches optimal results without requiring any prior fine-tuning. We also show that TTA is even able to slightly overcome the performance of an accurately calibrated SA optimizer for the same number of iterations.

Enhanced-SETL: A multi-variable deep reinforcement learning approach for contention window optimization in dense Wi-Fi networks

In this paper, we introduce the Enhanced Smart Exponential-Threshold-Linear (Enhanced-SETL) algorithm, a new approach that uses the multi-variable Deep Reinforcement Learning (DRL) framework to simultaneously optimize multiple settings of the Contention Window (CW) in IEEE 802.11 wireless networks. Unlike traditional DRL methods that adjust only a single CW parameter, our innovative approach simultaneously optimizes both the CW minimum (CWmin) and CW Threshold (CWThreshold), significantly improving network traffic control. We utilize a Double Deep Q-learning Network (DDQN) for dynamic updates of these CW settings, broadcasted across the dense Wi-Fi networks. This dual adjustment method, coupled with dynamic, data-driven updates, not only enhances throughput, but also reduces collision rates, and ensures fairness access across both static and dynamic wireless environments. Enhanced-SETL achieves a throughput improvement ranging from 3.55% up to 43.73% and from 3.98% up to 30.15% in static and dynamic scenarios over standard protocols and state-of-the-art DRL models, while maintaining a fairness index near 99% across diverse stations, showcasing its effectiveness and adaptability in various network conditions.

Lyapunov Drift-Plus-Penalty-Based Cooperative Uplink Scheduling in Dense Wi-Fi Networks.

In high-density network environments with multiple access points (APs) and stations, individual uplink scheduling by each AP can severely interfere with the uplink transmissions of neighboring APs and their associated stations. In congested areas where concurrent uplink transmissions may lead to significant interference, it would be beneficial to deploy a cooperative scheduler or a central coordinating entity responsible for orchestrating cooperative uplink scheduling by assigning several neighboring APs to support the uplink transmission of a single station within a proximate service area to alleviate the excessive interference. Cooperative uplink scheduling facilitated by cooperative information sharing and management is poised to improve the likelihood of successful uplink transmissions in areas with a high concentration of APs and stations. Nonetheless, it is crucial to account for the queue stability of the stations and the potential delays arising from information exchange and the decision-making process in uplink scheduling to maintain the overall effectiveness of the cooperative approach. In this paper, we propose a Lyapunov drift-plus-penalty framework-based cooperative uplink scheduling method for densely populated Wi-Fi networks. The cooperative scheduler aggregates information, such as signal-to-interference-plus-noise ratio (SINR) and queue status. During the aggregation procedure, propagation delays are also estimated and utilized as a value of expected cooperation delays in scheduling decisions. Upon aggregating the information, the cooperative scheduler calculates the Lyapunov drift-plus-penalty value, incorporating a predefined model parameter to adjust the system accordingly. Among the possible scheduling candidates, the proposed method proceeds to make uplink decisions that aim to reduce the upper bound of the Lyapunov drift-plus-penalty value, thereby improving the network performance and stability without a severe increase in cooperation delay in highly congested areas. Through comprehensive performance evaluations, the proposed method effectively enhances network performance with an appropriate model parameter. The performance improvement is particularly notable in highly congested areas and is achieved without a severe increase in cooperation delays.

A novel energy-saving method for campus wired and dense WiFi network applying machine learning and idle cycling techniques

University campus networks need wired (ethernet) and dense wireless fidelity networks that have devices like access points, switches, and routers that are always turned on. Consequently, they generate two important problems: the energy bill and the influence of carbon dioxide into the atmosphere. Energy savings are the solution to those problems. There are several proposals to augment the energy savings separately in ethernet and wireless fidelity, but there is no integrated method to simultaneously reduce them in both parts of the networks. Our novel method combines idle cycling and machine learning techniques to efficiently obtain energy savings in both parts of the network simultaneously. We categorize network devices into two groups: (a) those that are always turned on and (b) those that can be dynamically turned on or off based on network performance. We formulated two algorithms that decide when to turn on and off access points. We use Ward's machine learning hierarchical clustering technique to optimize the energy savings of our model in the network of the Unidades Tecnológicas de Santander (Bucaramanga, Colombia). We showed that energy savings of 21.5 kWh per day are possible. The success of the model in this context highlights its potential to achieve substantial energy savings.

Information Technology for Maximizing Energy Consumption for Useful Information Traffic in a Dense Wi-Fi 6/6E Ecosystem

In Wi-Fi standards, a relatively narrow range of frequency spectrums is declared as working, on the operation of which additional restrictions are imposed in different countries. When creating dense wireless network ecosystems focused on massive information traffic, this circumstance causes significant interference even in the case of using Wi-Fi 6/6E-compatible equipment. An effective solution to this problem is the implementation of a centralized management mechanism for the relevant parameters of the target network ecosystem. The growing attention to ecology and rational use of electricity makes the problem of maximizing energy consumption for useful information traffic in a dense Wi-Fi 6/6E ecosystem an urgent task. Only the addressed information traffic between the transmitter and the target subscriber, which are subjects of the OFDMA technology and the MU-MIMO multiple access system (with an emphasis on the latter), is considered useful. To solve the problem, the authors formalized the Wi-Fi 6/6E ecosystem’s energy consumption model, which takes into account the specifics of OFDMA and MU-MIMO, the influence of the communication channel characteristics on the speed of target information transfer, and detailed energy consumption for maintaining the network infrastructure in a functional state. Based on the created model, the research problem is represented by the difference between two monotonic functions, relative to which the problem of optimization with restrictions is set. The process of solving this problem is presented in the form of information technology with a branch-and-bound hierarchy and a nested unconditional optimization problem. The results of simulated modelling in the MATLAB-NS3 environment showed a significant advantage of the authors’ approach. The energy power consumption by the Wi-Fi 6/6E ecosystem, the parameters of which were adjusted with the help of the authors’ information technology, decreased by more than four times.

An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks

In dense Wi-Fi networks, achieving load balancing is critical to optimize network utilization and provide equitable network consumption among the users. Traditional Wi-Fi networks have issues in attaining effective load balancing. Software-Defined Networking (SDN) has presented a viable solution by isolating the data plane and control plane, enabling more agile and cost-effective networks. In this paper we put forward an Adaptive Symmetrical Load Balancing (ASLB) scheme to ensure fairness of load symmetry in Software Defined Wi-Fi Networks (SD-Wi-Fi), while also optimizing the flows transition process using an Analytical Hierarchal Process (AHP). User activity is monitored by access points (APs), which operate under OpenFlow standards. Three essential features, packet volume, packet category and delay hindrance, are used for flow assignment to various controllers. The controllers are arranged in two tiers, universal and regional controllers. The universal controller (UC) handles the workload statistics of regional controllers (RC) in the form of clusters. Extensive simulations using OMNeT++ simulator are performed. The performance parameters taken into consideration are throughput, delay, packet loss rate, network transition count and workload distribution. Our findings demonstrate that the ASLB technique effectively optimizes the network utilization and ensures equitable network consumption among the end users. The proposed scheme outperforms the Mean Probe Delay scheme (MPD), Channel Measurement-based Access Selection scheme (CMAS), Received Signal Strength Indicator-based scheme (RSSI) and Distributed Antenna Selection scheme (DASA), being 40% higher in throughput and 25% lower in delay.

Joint Access Point Placement and Power-Channel-Resource-Unit Assignment for IEEE 802.11ax-Based Dense WiFi Network With QoS Requirements

IEEE 802.11ax is the standard for the new generation WiFi networks. In this paper, we formulate the problem of joint access point (AP) placement and power-channel-resource unit assignment for 802.11ax-based dense WiFi. The objective is to minimize the number of APs. Two quality-of-service (QoS) requirements are to be fulfilled: (1) a two-tier throughput requirement which ensures that the throughput of each station is good enough, and (2) a fault tolerance requirement which ensures that the stations could still use WiFi even when some APs fail. We prove that this problem is NP-hard. To tackle this problem, we first develop an analytic model to derive the throughput of each station under the OFDMA mechanism and a widely used interference model. We then design a heuristic algorithm to find high-quality solutions with polynomial time complexity. Simulation results under both fixed-user and mobile-user cases show that: (1) when the area is small (50 x 50 m2), our algorithm gives the optimal solutions; when the area is larger (80 x 60 m2), our algorithm can reduce the number of APs by 34.9-87.7% as compared to the Random and Greedy algorithms. (2) Our algorithm can always get feasible solutions that fulfill the QoS requirements.

ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks

Device mobility in dense Wi-Fi networks offers several challenges. Two well-known problems related to device mobility are handover prediction and access point selection. Due to the complex nature of the radio environment, analytical models may not characterize the wireless channel, which makes the solution of these problems very difficult. Recently, cognitive network architectures using sophisticated learning techniques are increasingly being applied to such problems. In this paper, we propose data-driven machine learning (ML) schemes to efficiently solve these problems in wireless LAN (WLAN) networks. The proposed schemes are evaluated and results are compared with traditional approaches to the aforementioned problems. The results report significant improvement in network performance by applying the proposed schemes. The proposed scheme for handover prediction outperforms traditional methods i.e. received signal strength method and traveling distance method by reducing the number of unnecessary handovers by 60% and 50% respectively. Similarly, in AP selection, the proposed scheme outperforms the strongest signal first and least loaded first algorithms by achieving higher throughput gains up to 9.2% and 8% respectively.

BuSAR: Bluetooth Slot Availability Randomization for Better Coexistence With Dense Wi-Fi Networks

The last decade has witnessed the ever-increasing deployment of Wi-Fi networks and the explosion of Bluetooth-based applications. As a result, the coexistence of Bluetooth piconets with highly-dense Wi-Fi networks is a common phenomenon currently. Unlike Wi-Fi that conducts carrier sensing before channel access, Bluetooth adopts frequency hopping based on a predefined hop sequence, which inevitably incurs considerable cross-technology interference to Wi-Fi. While the Adaptive Frequency Hopping technique is standardized for interference reduction, it does not perform well in current practice where densely-deployed Wi-Fi networks commonly cover the whole 2.4 GHz unlicensed spectrum. In this context, this article presents BuSAR, a novel approach to account for the coexistence problem between Bluetooth piconets and dense Wi-Fi networks. BuSAR embodies the first work to aim at mitigating the cross-technology interference between Bluetooth and highly-dense Wi-Fi networks in a distributed manner. At the heart of BuSAR lies a subtle technique called B l u etooth s lot a vailability r andomization, which exploits the redundancy of erroneous Bluetooth packets for better Bluetooth/Wi-Fi coexistence. With BuSAR adopted, multiple Bluetooth piconets are guaranteed to operate independently and only a lightweight algorithm is needed to be implemented at each Bluetooth device. Both theoretical analysis and experimental results validate the feasibility and superiority of BuSAR.

Adversarial Learning-Enabled Automatic WiFi Indoor Radio Map Construction and Adaptation With Mobile Robot

Location-based service (LBS) has become an indispensable part of our daily lives. Realizing accurate LBS in indoor environments is still a challenging task. WiFi fingerprinting-based indoor positioning system (IPS) has achieved encouraging results recently, but the time and labor overhead of constructing a dense WiFi radio map remains the key bottleneck that hinders it for real-world large-scale implementation. In this article, we propose WiGAN an automatic fine-grained indoor ratio map construction and the adaptation scheme empowered by the Gaussian process regression conditioned least-squares generative adversarial networks (GPR-GANs) with a mobile robot. First, we develop a mobile robotic platform that constructs the spatial map and radio map simultaneously in the easily accessed free space. GPR-GAN first establishes a Gaussian process regression (GPR) model using the real received signal strength (RSS) measurements collected by our robotic platform via LiDAR SLAM in the free space. Then, the outputs of the GPR are adopted as the input of GAN's generator. The learning objective of GAN is to synthesize realistic RSS data in a constrained space where it has not been covered and model the irregular RSS distributions in complex indoor environments. Real-world experiments were conducted in a real-world indoor environment, which confirms the feasibility, high accuracy, and superiority of WiGAN over existing solutions in terms of both RSS estimation accuracy and localization accuracy.

Instrumentation for measuring users’ goodputs in dense Wi-Fi deployments and capacity-planning rules

Before a dense Wi-Fi network is deployed, Wi-Fi providers must be careful with the performance promises they made in their way to win a bidding process. After such deployment takes place, Wi-Fi-network owners—such as public institutions—must verify that the QoS agreements are being fulfilled. We have merged both needs into a low-cost measurement system, a report of measurements at diverse scenarios and a performance prediction tool. The measurement system allows measuring the actual goodput that a set of users are receiving, and it has been used in a number of schools on a national scale. From this experience, we report measurements for different scenarios and diverse factors—which may result of interest to practitioners by themselves. Finally, we translate all the learned lessons to a freely-available capacity-planning tool for forecasting performance given a set of input parameters such as frequency, signal strength and number of users—and so, useful for estimating the cost of future deployments.

A dynamic access point allocation algorithm for dense wireless LANs using potential game

This work introduces an innovative Access Point (AP) allocation algorithm for dense Wi-Fi networks, which relies on a centralised potential game developed in a Software-Defined Wireless Networking (SDWN)-based framework. The proposed strategy optimises the allocation of the Wi-Fi stations (STAs) to APs and allows their dynamic reallocation according to possible changes in the capacity of the Wi-Fi network. This paper illustrates the design of the proposed framework based on SDWN and the implementation of the potential game-based algorithm, which includes two possible strategies. The main novel contribution of this work is that the algorithm allows us to efficiently reallocate the STAs by considering external interference, which can negatively affect the capacities of the APs handled by the SDWN controller. Moreover, the paper provides a detailed performance analysis of the algorithm, which describes the significant improvements achieved with respect to the state of the art. Specifically, the results have been compared against the AP selection considered by the IEEE 802.11 standards and another centralised algorithm dealing with the same problem, in terms of the data bit rate provided to the STAs, their dissatisfaction and Quality of Experience (QoE). Finally, the paper analyses the trade-off between efficient performance and the computational complexity achieved by the strategies implemented in the proposed algorithm.

People Counting by Dense WiFi MIMO Networks: Channel Features and Machine Learning Algorithms.

Subject counting systems are extensively used in ambient intelligence applications, such as smart home, smart building and smart retail scenarios. In this paper, we investigate the problem of transforming an unmodified WiFi radio infrastructure into a flexible sensing system for passive subject counting. We first introduce the multi-dimensional channel features that capture the subject presence. Then, we compare Bayesian and neural network based machine learning tools specialized for subject discrimination and counting. Ensemble classification is used to leverage space-frequency diversity and combine learning tools trained with different channel features. A combination of multiple models is shown to improve the counting accuracy. System design is based on a dense network of WiFi devices equipped with multiple antennas. Experimental validation is conducted in an indoor space featuring up to five moving people. Real-time computing and practical solutions for cloud migration are also considered. The proposed approach for passive counting gives detection results with 99% average accuracy.

Bridging the Transition from IEEE 802.11ac to IEEE 802.11ax: Survival of EDCA in a Coexistence Environment

TGax aims at developing the IEEE 802.11ax protocol to provide enhanced throughput and power efficiency in dense WiFi deployment environments. The new specification shifts the focus from EDCA access to point coordination access, which aims at improving network utilization and power efficiency. Furthermore, it introduces, for the first time, the concept of RU allocation and orthogonal frequency division multiple access (OFDMA) techniques. The new specification addresses the spectral inefficiency in existing transmission by including schedule-based multi-user multiple-input, multiple- output (MU-MIMO) and multi-user OFDMA (MU-OFDMA) techniques to facilitate multi-user uplink transmission for high efficiency (HE) devices. However, complete adoption of the HE protocol is likely to take place over a period of time to offset the deployment cost. To ensure the coexistence of non-HE devices during the transition period, EDCA based single user transmission will remain the basic access technique and a focal point of spectral inefficiency for non-HE devices in the uplink direction. Motivated by our work on the performance of IEE 802.11ac, we propose an EDCA based uplink transmission technique for non-HE devices that allows multi-user transmission to improve spectral efficiency in coexistent WLAN networks.

<italic>C-SCAN</italic>: Wi-Fi Scan Offloading via Collocated Low-Power Radios

Wi-Fi channel scanning—the task of searching for available channels at a given location—is a fundamental feature to maintain always-available and high-quality wireless connectivity in today’s mobile devices. However, it is a challenging task to design an intelligent scanning algorithm that can discover available access points (APs) in a short time period, because the scanning station has no prior knowledge on the APs in its vicinity. Therefore, traditional scanning algorithms seek to discover available APs by scanning the full set of channels, including channels where no APs exist. This often induces unnecessary scanning latency and/or energy consumption. In this paper, we present a novel scheme, called C-SCAN , that exploits a low-power wireless personal area network interface, such as Bluetooth or ZigBee integrated into the device to offload Wi-Fi scanning overhead by suppressing unnecessary scanning of AP-free Wi-Fi channels. To this end, C-SCAN inspects channel information with a low-power Bluetooth radio and identifies which Wi-Fi channels are in use, prior to the actual channel scanning with a Wi-Fi interface. By excluding the channels determined to be empty, the Wi-Fi scanning manager can perform scanning only on available Wi-Fi channels. Thereby, a significant performance gain in terms of delay and energy is obtained. We implement a prototype of C-SCAN using a Bluetooth-compliant wireless transceiver and demonstrate its efficiency. Experimental results show that C-SCAN achieves high detection accuracy with low latency, even in dense Wi-Fi environments.

CADWAN: A Control Architecture for Dense WiFi Access Networks

The growing demands of ubiquitous computing are leading to the densification of wireless access networks. The challenges of high density deployments can be addressed by network-wide centralized control. To this end, we propose CADWAN, a Control Architecture for efficient management of Dense WiFi Access Networks. Its main advantages are: flexibility (it supports software-defined wireless networking), scalability (it uses a three-tier optimization framework), and extendibility (it exploits a unified control interface with support for heterogeneous devices). Furthermore, CADWAN is complementary to ongoing developments in IEEE 802.11, especially 802.11ax.

TCP Download Performance in Dense WiFi Scenarios: Analysis and Solution

How does a dense WiFi network perform, specifically for the common case of TCP download? While the empirical answer to this question is ‘poor’, analysis and experimentation in prior work has indicated that TCP clocks itself quite well, avoiding contention-driven WiFi overload in dense settings. This paper focuses on measurements from a real-life use of WiFi in a dense scenario: a classroom where several students use the network to download quizzes and instruction material. We find that the TCP download performance is poor, contrary to that suggested by prior work. Through careful analysis, we explain the complex interaction of various phenomena which leads to this poor performance. Specifically, we observe that a small amount of upload traffic generated when downloading data upsets the TCP clocking, and increases contention on the channel. Further, contention losses lead to a vicious cycle of poor interaction with autorate adaptation and TCP's timeout mechanism. To reduce channel contention and improve performance, we propose a modification to the AP scheduling policy to improve the performance of large TCP downloads. Our solution, WiFiRR, picks only a subset of clients to be served by the AP during any instant, and varies this set of “active” clients periodically in a round-robin fashion over all clients to ensure that no client starves. We have done extensive evaluation of WiFiRR in simulation and in real settings. By reducing the number of contending nodes at any point of time, WiFiRR improves the download time of large TCP flows upto $3.5\times$ of our classroom scenario. We also compare WiFiRR with state-of-the-art prior work WiFox, WiFiRR improves download time by $2.25\times$ over WiFox.

Stochastic-Geometry-Based Performance Analysis of Delayed Mobile Data Offloading With Mobility Prediction in Dense IEEE 802.11 Networks

Mobile data offloading through Wi-Fi is a promising solution to alleviate the explosive data increase in cellular network. While extensive attempts have been made at mobile data offloading, previous studies have rarely paid attention to network characteristics (e.g. Wi-Fi deployment density) and its effect on overall mobile offloading performance. In this paper, we propose a stochastic geometry model for the performance analysis of delay-tolerant offloading with mobility prediction. We aim to understand how the network parameters, such as network size and channel condition, affect the long-term offloading potential in the dense Wi-Fi sitting. The deployment of Wi-Fi is modeled as an independent Poisson point process (PPP) to take the effect of interference and CSMA/CA-based medium access control protocol into account. Then the semi-Markov process is used to model the user’s movement taking the sojourn time into account. Based on the PPP deployment and semi-Markov process, we can obtain the potential offloading traffic. Through the above proposed analytical studies, the network providers can easily obtain a rough estimation on the average offloading performance from a given dense network.

A Method for Power Saving in Dense WiFi Networks

Currently environment protection is a big challenge. One way of protection is focusing on power saving of electrical devices. Although computers and networks are not the main air polluting sources, they are contributing in a considerable way. Multiple proposals to reduce the power consumption have been presented due to the massive usage of the networking equipment. It is also important the economic profit (and not only the decreased pollution) derived by the reduction, at home and enterprises, of costs generated by the high power consumption of devices always on and, often, not utilized for a long time. In WiFi networks the main efforts to reduce consumption are focused on the user terminals, excluding the access points as main target. In this paper, we present a method to power saving focused on reducing consumption in the devices of access to the network, concretely of the access points. With this proposal, the consumption in the access points decreases and the costs would be reduced but keeping the utility for which they were designed.

Novel learning-based spatial reuse optimization in dense WLAN deployments

To satisfy the increasing demand for wireless systems capacity, the industry is dramatically increasing the density of the deployed networks. Like other wireless technologies, Wi-Fi is following this trend, particularly because of its increasing popularity. In parallel, Wi-Fi is being deployed for new use cases that are atypically far from the context of its first introduction as an Ethernet network replacement. In fact, the conventional operation of Wi-Fi networks is not likely to be ready for these super dense environments and new challenging scenarios. For that reason, the high efficiency wireless local area network (HEW) study group (SG) was formed in May 2013 within the IEEE 802.11 working group (WG). The intents are to improve the “real world” Wi-Fi performance especially in dense deployments. In this context, this work proposes a new centralized solution to jointly adapt the transmission power and the physical carrier sensing based on artificial neural networks. The major intent of the proposed solution is to resolve the fairness issues while enhancing the spatial reuse in dense Wi-Fi environments. This work is the first to use artificial neural networks to improve spatial reuse in dense WLAN environments. For the evaluation of this proposal, the new designed algorithm is implemented in OPNET modeler. Relevant scenarios are simulated to assess the efficiency of the proposal in terms of addressing starvation issues caused by hidden and exposed node problems. The extensive simulations show that our learning-based solution is able to resolve the hidden and exposed node problems and improve the performance of high-density Wi-Fi deployments in terms of achieved throughput and fairness among contending nodes.