Future Wi-Fi Research Articles

Hybrid Services QoS Enhancement by Joint Traffic and Access Management in Unsaturated IEEE 802.11ax Network

With the ubiquitous proliferation of Internet of Thing (IoT) devices, Access Points (APs) of future Wi-Fi networks are expected to support more Stations (STAs) with increased traffic dynamics and diverse Quality-of-Service (QoS) requirements. While supporting STAs with guaranteed and non-guaranteed QoS requirements using hybrid dense access, the performance of Non-guaranteed STAs (NG-STAs) often suffers more due to the exacerbated random-access congestion and the increased random-access collision. To overcome these challenges, we propose a Joint Traffic and Access Management (JTAM) mechanism to reshape the access traffic of Guaranteed-STA (G-STAs) and arrange the access opportunities of NG-STAs. The reshaped traffic smooths the varying Resource Units (RUs) for random access, and the grouping strategy optimizes the access efficiency of NG-STAs. The analysis of unsaturated uplink OFDMA random access (UORA) back-off process theorizes the optimal grouping strategy under different saturated rates and channel error rates. Both analytical and simulation results show that JTAM improves the overall system throughput, the average NG-STA's throughput, and reduces the average NG-STA's access latency in the dense access scenario. JTAM shows robust performance when supporting high dynamic non-guaranteed traffic and maintains high fairness among NG-STAs groups.

Contention alleviation in WiFi networks by using light-weight machine learning model

The random access MAC (e.g., CSMA/CA) is known to be vulnerable to heavy collisions when the transmission contention level is high. Heavy collisions have typically been mitigated by adjusting the length of the waiting interval imposed before individual transmission attempts, called ’backoff time’. For example, there exist a large body of studies to enhance the efficiency of the WiFi backoff mechanism. In this paper, we propose a novel scheme that inserts a configurable non-random duration, called offset, before the beginning of the conventional CSMA/CA backoff. Since the existing backoff mechanism is untouched, the proposed scheme is compatible with all legacy and future WiFi standards. The size of offset is dynamically determined at run time by using a lightweight machine learning model. While machine learning models have been applied to the problem of contention alleviation in WiFi networks, the proposed scheme is unique in its flexibility and low overhead for model training and inferencing. Our scheme employs a simple lightweight DNN (Deep Neural Network) model which does not require a large amount of training data. The complexity of our machine learning model does not increase even if the number of the devices in the WiFi networks increases. Furthermore, the proposed scheme is executed in a distributed fashion, so that (near) real-time adaptation to dynamic change of traffic condition is possible at each end device that is not equipped with high computing resource (i.e., on-device DNN model execution).

Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks

Real-time applications (RTA) are an important use case for IEEE 802.11be, a new amendment to the Wi-Fi standard. This amendment introduces new complicated mechanisms to provide low delay and high reliability for RTA, but many of them are not supported by legacy devices that may be present in future Wi-Fi networks. In contrast, the preliminary channel access (PCA) method is designed to satisfy strict RTA requirements even in the presence of legacy devices and does not require significant changes to the Wi-Fi protocol. However, it significantly reduces the capacity for non-RTA traffic. This paper introduces a Smart PCA method, which improves the performance of all the stations in scenarios with multiple RTA stations. Extensive simulation shows that the Smart PCA method guarantees low delays for intensive RTA traffic in these scenarios. Moreover, it doubles the network capacity for the stations with non-RTA traffic.

Dynamic Traffic Allocation in IEEE 802.11be Multi-Link WLANs

The multi-link operation (MLO) is a key feature of the next IEEE 802.11be Extremely High Throughput amendment. Through its adoption, it is expected to enhance users’ experience by improving throughput rates and latency. However, potential MLO gains are tied to how traffic is distributed across the multiple radio interfaces. In this letter, we introduce a traffic manager atop MLO, and evaluate different high-level traffic-to-link allocation policies to distribute incoming traffic over the set of enabled interfaces. Following a flow-level approach, we compare both non-dynamic and dynamic traffic balancing policy types. The results show that the use of a dynamic policy, along with MLO, allows to significantly reduce the congestion suffered by traffic flows, enhancing the traffic delivery in all the evaluated scenarios, and in particular improving the quality of service received by video flows. Moreover, we show that the adoption of MLO in future Wi-Fi networks improves also the coexistence with non-MLO networks, which results in performance gains for both MLO and non-MLO networks.

Collision-Based Window-Scaled Back-Off Mechanism for Dense Channel Resource Allocation in Future Wi-Fi

Wireless local area networks (WLANs), known as Wi-Fi, are widely deployed to meet the enhanced needs of data-centric internet applications, such as wireless docking, unified communications, cloud computing, interactive multimedia gaming, progressive streaming, support of wearable devices, up-link broadcasts and cellular offloading. Wi-Fi networks typically adopt the Distributed Coordination Function (DCF)-based Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), which uses the Binary Exponential Back-off (BEB) algorithm at the MAC layer mechanism to access channel resources. Currently deployed Wi-Fi networks face huge challenges towards efficient channel access for denser environments due to the blind exponential increase/decrease of a contention window (CW) procedure that is inefficient for a higher number of contending stations. Several modifications and amendments have been proposed to improve the performance of the MAC layer channel access based on a fixed or variable CW size. However, a more realistic network density-based channel resource allocation solution is still missing. An efficient channel resource allocation is one of the most critical challenges for future highly dense WLANs, such as High-Efficiency WLAN (HEW). In this paper, we propose a Channel Collision-based Window Scaled Back-off (CWSB) mechanism for channel resource allocation in HEW. In our proposed CWSB, all contending stations select an optimized CW size for each back-off stage for collided or successfully transmitted data frames. We affirm the performance of the proposed CWSB mechanism with the help of an Iterative Discrete Time Markov Chain (I-DTMC) model. This paper evaluates the performance of our proposed CWSB mechanism in HEW Wi-Fi networks using an NS3 simulator in terms of the normalized throughput and channel access delay compared to the state-of-the-art BEB and a recently proposed mechanism.

A Phase Noise Resistant Constellation Rotation Method and Its Experimental Validation for NOMA Wi-Fi

Non-orthogonal multiple access (NOMA) is a well-accepted technology for future Wi-Fi systems to improve spectral efficiency. NOMA allows an access point (AP) to simultaneously serve multiple devices by splitting the transmit power between them. Using the superposition coding the AP combines signals of different frames with predefined power weights. However, as it is already known, the phase noise caused by hardware imperfections limits the performance of NOMA transmissions. The paper proposes a practical, easy-to-implement, and backward-compatible method to rotate the constellation of the low power signal in order to make it more robust to the phase noise. A mathematical model is developed to find such a rotation angle that minimizes the bit error rate (BER) of the low power frame. A new NOMA Wi-Fi prototype testbed is developed with the new feature of constellation rotation ability, which is used to validate the performance of our proposed method. Many experimental results show the proposed constellation rotation approach significantly decreases the BER in high signal-to-noise scenarios.

Study of Multi-Link Channel Access Without Simultaneous Transmit and Receive in IEEE 802.11be Networks

Native support for multi-link operation is a key novelty of the future Wi-Fi 7 technology defined by the IEEE 802.11be standard, which is currently under development. With the 6 GHz band recently granted for Wi-Fi operation, the novel multi-link feature enables simultaneous usage of multiple wide channels. Thus, it multiplies the capacity of Wi-Fi networks well beyond the 30 Gbps target of Wi-Fi 7. However, not all stations can simultaneously transmit and receive (STR) information on several links because of cross-channel interference. That is why it is proposed to synchronize the transmissions on different channels. The presence of legacy stations not supporting the multi-link operation raises many issues related to multi-link channel access performance and fairness. This paper evaluates various channel access approaches considered in the context of the Wi-Fi 7 non-STR operation and discusses how to organize channel access in multi-link Wi-Fi 7 congested networks to be both efficient and fair, even in the presence of legacy stations.

Basic simulation studies on 60 GHz wireless technology employing QAM modulation

The 60 GHz technology engage between 57 GHz to 66 GHz occupying an unlicensed band consisting of four channels with 2 GHz bandwidth each. The wavelength of such technology approximates 5 mm enabling milli-meter wave communication. It benefits transmission security for crucial data. Indoor networking equipment with large bandwidth and high-capacity streams lag free High-Definition video from tablet to television. This paper intends to present the simulation studies on 60 GHz mm-wave band technology employing QAM modulation for wireless applications. The experimental results were obtained by simulating the designed architecture in Agilent ADS software tool and further presented to deduce the insight of future Wi-Fi for wireless applications.

Time-Sensitive Networking in IEEE 802.11be: On the Way to Low-Latency WiFi 7.

A short time after the official launch of WiFi 6, IEEE 802.11 working groups along with the WiFi Alliance are already designing its successor in the wireless local area network (WLAN) ecosystem: WiFi 7. With the IEEE 802.11be amendment as one of its main constituent parts, future WiFi 7 aims to include time-sensitive networking (TSN) capabilities to support low latency and ultra-reliability in license-exempt spectrum bands, enabling many new Internet of Things scenarios. This article first introduces the key features of IEEE 802.11be, which are then used as the basis to discuss how TSN functionalities could be implemented in WiFi 7. Finally, the benefits and requirements of the most representative Internet of Things low-latency use cases for WiFi 7 are reviewed: multimedia, healthcare, industrial, and transport.

Performance Analysis of Synchronous Multi-Radio Multi-Link MAC Protocols in IEEE 802.11be Extremely High Throughput WLANs

The representative media access control (MAC) mechanism of IEEE 802.11 is a distributed coordination function (DCF), which operates based on carrier-sense multiple access with collision avoidance (CSMA/CA) with binary exponential backoff. The next amendment of IEEE 802.11 being developed for future Wi-Fi by the task group-be is called IEEE 802.11be, where the multi-link operation is mainly discussed when it comes to MAC layer operation. The multi-link operation discussed in IEEE 802.11be allows multi-link devices to establish multiple links and operate them simultaneously. Since the medium access on a link may affect the other links, and the conventional MAC mechanism has just taken account of a single link, the DCF should be used after careful consideration for multi-link operation. In this paper, we summarize the DCFs being reviewed to support the multi-radio multi-link operation in IEEE 802.11be and analyze their performance using the Markov chain model. Throughout the extensive performance evaluation, we summarize each MAC protocol’s pros and cons and discuss essential findings of the candidate MAC protocols.

Initial Access through Beamforming in mmWave

Future Wi-Fi, 5G Cellular and millimetre-wave (mmWave) will depend on highly directional links in order to prevail over exuberant path loss experienced in the different bands of frequency. However, in order to establish these type of links, the receiver and transmitter need mutual discovery which will result in high energy consumption and large latency. The proposed work deals with reduction of energy consumption and latency significantly with the help of a fully digital front-end. The digital beamformer will receive the spatial samples within a shot, from all directions. However, in analog front-ends, sampling is allowed for beamforming in one particular direction at a time resulting in the time period in which the mobile is “on” for longer. This will result in an increase in energy consumption by more than four times for the analog front-end when compared with digital front-ends, taking into consideration the antenna arrays’ size. However, from the power consumption point of view, using a fully digital beamforming post beam discovery is not recommended. Hence in order to overcome this drawback, a digital beamformer coupled with a 4-bit A-D convertor with low resolution is proposed. The use of low resolution will decrease the power consumption such that it is in the same zone as that of analog beam forming while it is possible to make use of the fully digital beamforming spatial multiplexing capabilities resulting in improved energy efficiency and reduced discovery latency.

Miniaturized array antenna with reduced beam squinting

In this paper, we propose a novel multi-band series-fed array of patch antennas, a three-element linear resonant array operating at five distinct frequencies. A wide-band phase-shifting network compensates for the inter-element phase differences of radiating elements, which results in the reduction of beam squinting over the operating frequency range. Compared to a resonant series-fed array with conventional phase-shifters, beam squinting is reduced by 70%. Also, the length of the suggested phase shifters is less than quarter wave-length (at f = 6.78 GHz). Size reduction of the radiating elements also contributes to the miniaturization, and compared to a conventional array of printed dipoles, directive radiation is obtainable with fewer radiating elements. Based on the two-port network model for radiating patches, a practical computational method—which results in the usage reduction of computational resources—is presented to calculate the final realized gain of the series-fed arrays. The proposed array operates within 5.4–7.45 GHz frequency range, which makes it suitable for current and future Wi-Fi, or mobile network applications. Besides, the design principles apply to any series-fed array antenna operating at dominant (TM010) resonant mode. The performance of the proposed array is validated via experimental measurements, and the results indicate a good agreement with the simulations.

Current Status and Directions of IEEE 802.11be, the Future Wi-Fi 7

While customers rivet their eyes on Wi-Fi 6, in the bowels of the IEEE 802.11 Working Group that creates Wi-Fi standards, the next generation Wi-Fi is being developed. At the very first sight, the new IEEE 802.11be amendment to the Wi-Fi standard is nothing but scaled 11ax with doubled bandwidth and the increased number of spatial streams, which together provide data rates as high as 40 Gbps. A bit deeper dive into the 802.11 activities reveals that 11be will support real-time applications. In reality, 11be introduces many more revolutionary changes to Wi-Fi, which will form a basement for further Wi-Fi evolution. Although by now (May 2020), the development process is at the very early phase without any draft specification, the analysis of the discussion in the 802.11 Working Group gives insights into the main innovations of 11be. In addition to the ones above, they include native multi-link operation, channel sounding optimization that opens the door for massive MIMO, advanced PHY and MAC techniques, the cooperation of various access points. The paper analyzes hundreds of features proposed for the new technology, focusing on the open problems that can be solved by the researchers who want to contribute to the development of 802.11be.

Mobile Vehicular Offloading With Individual Mobility

In vehicular communication, roadside infrastructure, such as WiFi APs, often requires a large amount of investment. In this paper, we propose the idea of Mobile Vehicular Offloading (MoVeOff), which doesn't require extra investment, but allows data transfer from on-board devices to mobile devices of drivers and passengers, for uploading to the Internet in the future. When they arrive at their homes, offices, or other places where WiFi connection is available, vehicular data will be offloaded in a delay-tolerant manner, by the ferrying of mobile devices. We build a realistic system to investigate the regularities in people's daily travelling and WiFi usage, analyze individual mobility, and establish a Long Short Term Memory (LSTM) model to predict one's future WiFi connectivity. Moreover, a mobility-aware routing scheme is developed for inter-vehicle communication. Each vehicle broadcasts its expected offloading probability and delay, so that messages are dynamically delivered to the nodes, whose offloading can guarantee delay and delivery ratio bounds required by applications. Thus, our scheme overcomes traditional opportunistic forwarding, and introduces predictable ferrying guaranteed by individual mobility. Through system running and simulations, we demonstrate that our scheme provides extra and stable offloading service for delay-tolerant data in the areas with sparse roadside infrastructures.

WiFi Sensing with Channel State Information

With the high demand for wireless data traffic, WiFi networks have experienced very rapid growth, because they provide high throughput and are easy to deploy. Recently, Channel State Information (CSI) measured by WiFi networks is widely used for different sensing purposes. To get a better understanding of existing WiFi sensing technologies and future WiFi sensing trends, this survey gives a comprehensive review of the signal processing techniques, algorithms, applications, and performance results of WiFi sensing with CSI. Different WiFi sensing algorithms and signal processing techniques have their own advantages and limitations and are suitable for different WiFi sensing applications. The survey groups CSI-based WiFi sensing applications into three categories, detection, recognition, and estimation, depending on whether the outputs are binary/multi-class classifications or numerical values. With the development and deployment of new WiFi technologies, there will be more WiFi sensing opportunities wherein the targets may go beyond from humans to environments, animals, and objects. The survey highlights three challenges for WiFi sensing: robustness and generalization, privacy and security, and coexistence of WiFi sensing and networking. Finally, the survey presents three future WiFi sensing trends, i.e., integrating cross-layer network information, multi-device cooperation, and fusion of different sensors, for enhancing existing WiFi sensing capabilities and enabling new WiFi sensing opportunities.

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method.

This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.

Passive target detection

Researchers at the University of Trento and the University of Cassino in Italy present an approach for detecting passive targets capable of detecting multiple complex target properties, such as position and posture, using wireless networks. Wireless detection is particularly important and useful in application fields like security and healthcare when the targets do not participate in the localisation process. In contexts where such localisation is needed wireless networks are nearly ubiquitous as electromagnetic fields propagate through objects like walls and furniture. Many existing detection technologies based on WiFi networks require wearable devices to operate. This requirement limits the adoption of wireless detection to cooperative users who are aware of being localised. It is not always possible or desirable to require patients in healthcare contexts to play an active part in localisation and of course in security applications intruders cannot be expected to be cooperate. Passive target perturbation of electromagnetic fields generated by existing wireless devices (below) compared to a standard background electromagnetic fields (above). Estimation of detailed passive target location-based features using volume of interest partitioning to improve the estimation of details using a step-by-step approach. One of the main difficulties associated with wireless detection of passive targets is the high complexity of the interactions between EM fields generated by wireless networks and the targets. The current limitation is poor detection reliability, which needs to be improved in order to make the detection truly usable in real-world scenarios. High variability of EM propagation in indoor scenarios, as well as the very high number of degrees of freedom of EM fields makes wireless detection very challenging. In this issue of Electronics Letters, the Cassino team reduce the complexity of wireless detection through the iterative estimation of multi-resolution target features. Their reported approach exploits the information contained in wireless signals by focusing step-by-step on different target behaviour details with an increasing resolution level. The estimation of very detailed target features, such as posture, is feasible with this approach. The main challenges the team had to address in this work included separation of wireless signal components related to the impact of the targets from those due to the environment; feature extraction from the wireless signals showing the complex relationship between electromagnetic fields and the targets; and system reliability with high generalisation capabilities for the proper estimation of target properties with reduced parameter calibration. The proposed approach in this Letter allows the exploitation of existing WiFi networks using information that is already available in standard WiFi devices. In the short term, such an approach can be considered a new functionality of WiFi networks. In the longer-term, developments reported in this Letter will exploit future WiFi standards trends, which will enable an increasing accuracy for understanding more complex target features, including vital signs like breathing rate and heartbeat, using commercial WiFi access points. “The next-generation development of the proposed passive-based approach will enable applications, for example in the field of healthcare, that are currently implemented using complex and expensive wearable devices”, states Cassino team member Federico Viani. The reported work falls within the research topic of wireless localisation of passive targets that has been developed by the ELEDIA Research Center for more than a decade. The research group of Viani and colleagues will continue to have a focus on wireless target detection methods for real world scenarios using existing infrastructures. The main limitations that still need to be addressed are related to long-term reliability. Over the last ten years, the high interest in IoT-based systems has stimulated the application of wearable technologies, thus enabling the development of new location-based services. Heterogeneous smart sensors have been designed to monitor the health and activity of users. Over the next ten years Viani expects that “the functionalities of wearable sensors and gadgets will be substituted by the development of smart environments that passively sense the occupants and understand their features without the need for wireless devices to be worn. Security-related application fields as well as healthcare and interactive gaming will benefit from novel passive detection approaches. For example, think about your WiFi access point that is able to detect sleep apnea or cardiac arrhythmia in real-time without using invasive sensors or expensive medical devices”.

Coexistence Issues in Future WiFi Networks

Current advancements within the WiFi domain, such as network densification, traffic offloading, and IoT support, pave the way for increased user and operator satisfaction with future (5G and beyond) network deployments. However, without seamless coexistence of novel and legacy WiFi devices as well as between heterogeneous WiFi networks, the goal of ubiquitous and effortless wireless access may be delayed. In this article, we identify several future WiFi use cases based on current trends in the research community as well as developments within IEEE 802.11 and analyze the functionalities required in these use cases, taking into consideration two important aspects: coexistence between new and legacy WiFi devices, and inter-network interference. Through our research we show that, despite several identified shortcomings and open issues, WiFi will definitely be a crucial component of future network deployments.

Gain Without Pain

Among numerous indoor localization systems proposed during the past decades, WiFi fingerprint-based localization has been one of the most attractive solutions, which is known to be free of extra infrastructure and specialized hardware. However, current WiFi fingerprinting suffers from a pivotal problem of RSS fluctuations caused by unpredictable environmental dynamics. The RSS variations lead to severe spatial ambiguity and temporal instability in RSS fingerprinting, both impairing the location accuracy. To overcome such drawbacks, we propose fingerprint spatial gradient (FSG), a more stable and distinctive form than RSS fingerprints, which exploits the spatial relationships among the RSS fingerprints of multiple neighbouring locations. As a spatially relative form, FSG is more resistant to RSS uncertainties. Based on the concept of FSG, we design novel algorithms to construct FSG on top of a general RSS fingerprint database and then propose effective FSG matching methods for location estimation. Unlike previous works, the resulting system, named ViVi, yields performance gain without the pains of introducing extra information or additional service restrictions or assuming impractical RSS models. Extensive experiments in different buildings demonstrate that ViVi achieves great performance, outperforming the best among four comparative start-of-the-art approaches by 29% in mean accuracy and 19% in 95th percentile accuracy and outweighing the worst one by 39% and 24% respectively. We envision FSG as a promising supplement and alternative to existing RSS fingerprinting for future WiFi localization.

On the Impact of LTE-U on Wi-Fi Performance

With the exponential growth in mobile data traffic taking place currently and projected into the future, mobile operators need cost effective ways to manage the load of their networks. Traditionally, this has been achieved by offloading mobile traffic onto Wi-Fi networks due to their low cost and increasingly ubiquitous deployment. Recently, LTE operating in the unlicensed spectrum has drawn significant interests from mobile operators due to the availability of the unlicensed spectrum. Using this technology, the unlicensed spectrum is directly utilized by LTE without the need to offload traffic to an alternative radio access technology such as Wi-Fi. However, the deployment of LTE networks in the unlicensed band poses significant challenges to the performance of current and future Wi-Fi networks. We discuss the LTE and Wi-Fi coexistence challenges and present analysis on performance degradation of the Wi-Fi networks at the presence of LTE.