Wi-Fi Standards Research Articles

On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning

For over forty years, TCP has been the main protocol for transporting data on the Internet. To improve congestion control algorithms (CCAs), delay bounding algorithms such as Vegas, FAST, BBR, PCC, and Copa have been developed. However, despite being designed to ensure fairness between data flows, these CCAs can still lead to unfairness and, in some cases, even cause data flow starvation in WiFi networks under certain conditions. We propose a new CCA switching solution that works with existing TCP and WiFi standards. This solution is offline and uses Deep Reinforcement Learning (DRL) trained on features such as noncongestive delay variations to predict and prevent extreme unfairness and starvation. Our DRL-driven approach allows for dynamic and efficient CCA switching. We have tested our design preliminarily in realistic datasets, ensuring that they support both fairness and efficiency over WiFi networks, which requires further investigation and extensive evaluation before online deployment.

Reviewing wireless broadband technologies in the peak smartphone era: 6G versus Wi-Fi 7 and 8

With the arrival of the peak smartphone era, users are upgrading their smartphones less frequently, and data growth is decelerating. To ensure effective spectrum management decisions, policy makers require a thorough understanding of prospective wireless broadband technologies, current trends and emerging issues. Here, we review the sixth cellular generation (‘6G’), in comparison to two new Wi-Fi standards, including IEEE 802.11be (‘Wi-Fi 7’) and IEEE 802.11bn (‘Wi-Fi 8’). We identify three emerging issues necessary for successful telecommunication policy. Firstly, evidenced-based policy making needs to be able to measure effectively how much demand takes place where and how. Thus, new datasets are needed reflecting real usage by different wireless broadband technologies, for indoor and outdoor users. Secondly, with data consumption growth slowing, there needs to be an urgent reassessment of spectrum demand versus allocation. Past forecasts do not reflect recent data and regulators urgently need to re-evaluate the implications for spectrum management. Finally, regulators need new and improved Lifecycle Impact Assessment metrics of cellular versus Wi-Fi architectures, to support successful policy decisions which mitigate energy and emissions impacts.

The Path Towards Virtualized Wireless Communications: A Survey and Research Challenges

To keep up with the increasing number of connected devices in people’s daily lives, it is necessary to develop intelligent mechanisms that perform the entire network management, interconnecting Wi-Fi, and the emerging beyond Fifth-Generation (5G) communications. Hence, it is essential to consider multiple usage scenarios, while managing end devices’ limitations. As a result, developing a system that allows network operators to link Wi-Fi services on their main networks becomes a critical issue. These include a new paradigm that tackles optimal and dynamic resource allocation techniques. Thus, to consider in a combined way, the applications requirements, the resources available, and the different tiers involved, mechanisms such as virtualization and slicing have emerged to handle the heterogeneous context of the next-generation wireless communications. Moreover, the allocation of Radio Access Network (RAN) resources needs to be addressed. For this purpose, Open-RAN has in mind an open environment, which relies on virtualized functions and is mostly vendor agnostic. This technology will enable high data rates while maintaining adequate Quality of Service (QoS) in wireless communications. This paper advances current literature, which mainly discusses these themes individually, by providing a comprehensive survey in Next Generation Wireless Communications, highlighting their integration with beyond 5G Communications. First, we introduce the Wi-Fi evolution and explain the main standards developed over the years. Second, we present the most recent Wi-Fi standards, Wi-Fi 6 and 7, compared with 5G and beyond. Lastly, we explain the concepts related to slicing, virtualization, RAN, Open-RAN and the open research challenges.

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.

Nonbinary LDPC Coded QAM Signals With Optimized Mapping: Bounds and Simulation Results

This paper studies specific properties of nonbinary low-density parity-check (NB LDPC) codes when used in coded modulation systems. The paper is focused on the practically important NB LDPC codes over the Galois extension fields GF(2m) with m ≤ 6 used with QAM signaling. Performance of NB LDPC coded transmission strongly depends on the mapping of nonbinary symbols to signal constellation points. We obtain a random coding bound on the maximum-likelihood decoding error probability for an ensemble of random irregular NB LDPC codes used with QAM signaling for specific symbol-to-signal point mappings. This bound is based on the ensemble average squared Euclidean distance spectra derived for these mappings. The simulation results for the belief-propagation decoding in the coded modulation schemes with the NB quasi-cyclic (QC)-LDPC codes under different mappings are given. Comparisons with the optimized binary QC-LDPC codes in the WiFi and 5G standards, as well as with the new bound, are performed.

CPS: Cross-interface network Partitioning and Scheduling towards QoS-aware data flow delivery in multimedia IoT

In the Internet of Things (IoT), multimedia traffic for audio, image, and video accounts for the largest proportion (over 78.7%) of the total traffic, bringing forward the vision of multimedia IoT (M-IoT). As part of the realization of loT, M-IoT is a general network paradigm that constitutes many smart objects equipped with the capability to collect multimedia data from the physical environment and deliver the data to other things. To satisfy a certain level of user experience, Quality of Service (QoS) is required to be regulated to ensure acceptable delivery of the multimedia content. As the most widely-used wireless technology, WiFi has been recommended for IoT communications for its high data rate, native IP compatibility, and good reusability of the existing infrastructures. However, WiFi suffers from channel contention, especially during multi-hop communications, which degrades the QoS performance and hinders its use for many M-IoT services. Although numerous protocols have been proposed to mitigate WiFi contention, they often consume much WiFi bandwidth for network control, lowering the level of achievable QoS performance. To address this issue, we propose a distributed Cross-interface network Partitioning and Scheduling (CPS) protocol, which leverages the co-existing ZigBee communications to divide the network into partitions and allows only one node in each partition to use its WiFi interface to transmit data at any time, for bandwidth-efficient and delay-constrained data flow delivery in M-IoT. A prototype node is implemented by integrating COTS ZigBee and WiFi interfaces into a BeagleBone Green wireless platform for IoT. Extensive field experiments are conducted in a multi-hop network of 24 prototype nodes that deliver real multimedia data (images and videos). The experiment results show that CPS outperforms the standard WiFi and a state-of-the-art contention control scheme (by 62.6% and 26.4% under high data traffic, respectively) in terms of a QoS metric capturing two basic performance metrics (i.e., bandwidth efficiency and end-to-end delay) of multi-hop communications, while retaining fair QoS performance and high energy efficiency.

Transmission quality evaluation of multistreaming video data from the UAV to the ground station in network simulator 3 simulation environment using WI-FI standards

The article presents the results of transmitting video data simulation in real time from video cameras on board Unmanned Aerial Vehicles (UAVs) to a ground station (GS). The main purpose of the article was to evaluate the impact of the flying source node number on Quality of Service (QoS) metrics, such as Packet Delivery Ratio and Goodput at OSI model application layer. A scenario was considered when hovering over UAVs, in the zone of reliable communication, transmitted a stream of video data to a ground station, then the distance between the UAV and the GS increased to repeat the experiment. The bitrate was maintained at the same level, close to the bitrate of real high-definition video. The experiment was carried out in the Network Simulator 3 (NS-3) simulation environment for scenarios with one, two, four, and eight source nodes. Simulation results have shown the negative impact of the increasing the number of flying source nodes on Quality of Service (QoS) metrics such as Packet Delivery Rate (PDR) and Goodput. Recommendations regarding the applicability of different standards of the 802.11 family to scenarios for using UAVs in the multistreaming video data transmission mode are given. A change in the relationship of useful throughput on the number of source nodes by 15 % was also found.

Extending Wi-Fi Direct Single-Group Network to Multi-Group Network Based on Android Smartphone

Nowadays, a very widespread of smartphones, especially Android smartphones, is observed. This is due to presence of many companies that produce Android based phones and provide them to consumers at reasonable prices with good specifications. The actual benefit of smartphones lies in creating communication between people through the exchange of messages, photos, videos, or other types of files. Usually, this communication is through the existence of an access point through which smartphones can connect to the Internet. However, the availability of the Internet is not guaranteed in all places and at all times, such as in crowded places, remote areas, natural disasters, or interruption of the Internet connection for any reason. To create a communication between devices, it is resorted to creating an ad hoc network using Device-to-Device technology. Wi-Fi Direct technology offers a suitable platform for creating an ad hoc network, as it supports the speed and range of standard Wi-Fi. In this paper, a mechanism is proposed to build an infrastructure-less ad hoc network, through developing the Wi-Fi direct protocol for Android smartphones. This network provides users ability to have a reliable communication, using the reliable Transmission Control Protocol only, and can continuously expand. Therefore it would be very beneficial in the absence of other infrastructure communication media such as cellular or Wi-Fi internet access.

Mitigating Trade-Off in Unlicensed Network Optimization Through Machine Learning and Context Awareness

Unlicensed cellular networks are being deployed worldwide by cellular operators to meet the rising data demands. However, the unlicensed band has existing incumbents such as Wi-Fi and radar systems. This creates a highly dynamic environment, making harmonious unlicensed coexistence difficult. Consequently, conventional optimization techniques are not sufficient to offer latency-critical applications and services. A data-driven hybrid optimization approach is necessary for optimal network performance with low convergence times. However, a largely unexplored problem in dense unlicensed network optimization is the accuracy-speed trade-off, that is, achieving high accuracy in optimization objectives with minimal time costs. This work seeks to address this problem through a hybrid optimization approach that combines machine learning and network optimization. It investigates the use of more precise higher-order network feature relationships (NFRs) in optimization formulations and the consequent trade-off that arises between the increase in convergence time (Speed) and the nearness to optimal results (Accuracy). In addition, it demonstrates the relevance of context awareness of network conditions and the traffic environment to mitigate the trade-off. To that end, a context-aware network feature relationship-based optimization (CANEFRO) approach is proposed and validated through decision matrix analysis. The experiments were carried out on a coexistence testbed consisting of both unlicensed LTE standards (LTE-U & LAA) and two Wi-Fi standards (802.11n/ac) on multiple channel bandwidths. In addition, LTE-U & LAA are contrasted on signaling and user data traffic data models and resource block allocation performance. More importantly, CANEFRO demonstrates the impact of the network context on the degree of feature relationship ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{nd}$ </tex-math></inline-formula> & <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3^{rd}$ </tex-math></inline-formula> degree polynomials), objective of optimization (SINR and Capacity), and the network use case (Accuracy vs. Speed). CANEFRO is also used to contrast LTE-U & LAA optimization performance. In particular, the decision matrix analysis demonstrates a higher decision score for LAA by as much as 42% compared to LTE-U.

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).

Implementasi Arsitektur Firewall Sebagai Sistem Keamanan Jaringan Wifi 802.11ax Aruba Software Defined Wan (SD-Wan)

The concept of WLAN technology continues to evolve so as to be able to keep up with the increasing number of user densities, schemes with density Access Point (AP) and User Stations (STA), IEEE 802.11 creates an IEEE 802.11ax (TGax) to develop a set of physical layer specifications (PHY) and Media Access New Control (MAC). IEEE 802.11ax is called a high efficiency WLAN (HE). currently in the concept of its application in the implementation of the firewall architecture on the 802.11ax WIFI network security system. This study aims to determine the concept of an 802.11ax Wifi network security system architecture developed by Aruba with the latest Access Point system that is able to support the latest Wi-Fi standards and is a wireless Access Point product that may be the first to get Wi-Fi Alliance (WFA) certification in Indonesia. the field of the latest WPA3 and Enhanced Open security system standards, and can provide much stronger encryption services and have simpler IoT security configuration settings. This research uses the literature method where the researcher collects data - information data related to the subject of the research conducted by the researcher. As well as using the data analysis technique used is using content analysis in the form of written documentation and organizing data into categories, describing it into units, what is meant by data analysis techniques. The results of this study.

On the Design of Beamforming Feedback for Wi-Fi Sensing

In recent years, channel state information (CSI) has been widely used for Wi-Fi sensing. Most of the existing Wi-Fi sensing researches are conducted based on the CSI matrix. It is noted that since the IEEE 802.11ac standard, beamforming weights feedback will be the only feedback type supported in the mainstream Wi-Fi standards. This letter aims to investigate Wi-Fi sensing based on beamforming weights feedback, where two feedback schemes namely Scheme 1 and Scheme 2 are analyzed and discussed. In Scheme 1, the transmit beamforming matrix (i.e., beamforming weight) is obtained by directly performing eigenvalue decomposition (EVD) on the channel autocorrelation matrix. As for Scheme 2, the transmit beamforming matrix is obtained based on performing EVD on the conjugate transpose of the channel autocorrelation matrix. The analysis shows that Scheme 1 retains the angle of departure (AOD), together with Doppler differences and time delay differences between different paths, while Scheme 2 can retain the entire AOD, Doppler, and time delay information. The effects of imperfect systems on sensing performance in practice are also discussed in this letter.

Neural Network-Based OFDM Receiver for Resource Constrained IoT Devices

Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used for communication links in many current and emerging Internet of Things (IoT) applications, including the latest WiFi standards. For such OFDM-based transceivers, many core physical layer functions related to channel estimation, demapping, and decoding are implemented for specific choices of channel types and modulation schemes, among others. To decouple hard-wired choices from the receiver chain and thereby enhance the flexibility of IoT deployment in many novel scenarios without changing the underlying hardware, we explore a novel, modular Machine Learning (ML)-based receiver chain design. Here, ML blocks replace the individual processing blocks of an OFDM receiver, and we specifically describe this swapping for the legacy channel estimation, symbol demapping, and decoding blocks with Neural Networks (NNs). A unique aspect of this modular design is providing flexible allocation of processing functions to the legacy or ML blocks, allowing them to interchangeably coexist. Furthermore, we study the implementation cost-benefits of the proposed NNs in resource-constrained IoT devices through pruning and quantization, as well as emulation of these compressed NNs within Field Programmable Gate Arrays (FPGAs). Our evaluations demonstrate that the proposed modular NN-based receiver improves bit error rate of the traditional non-ML receiver by averagely 61 percent and 10 percent for the simulated and over-the-air datasets, respectively. We further show complexity-performance tradeoffs by presenting computational complexity comparisons between the traditional algorithms and the proposed compressed NNs.

SISTEM DE COMUNICAȚIE MOBIL, GSM-WIFI, PENTRU VEHICULELE AGRICOLE

The research carried out in this paper aimed to create a communication network for a fleet of agricultural vehicles by means of which to transmit and receive information on the status of work, working time, processed areas, number of machines used and much more necessary information. Communication is based on GSM and WiFi standards, which bring the most secure security standards in data protection to this network. The major advantage of the solution presented is that all the important information is concentrated in one place. The proposed communication system consists of a mobile device that routes the WiFi network on a GSM network that has a minimum 3G standard but also works with 5G, depending on the GSM operator and the devices that are mounted on vehicles, which communicate over the WiFi network. The communication device was mounted on an electric tractor, where it was powered by the tractor's power source. In this communication architecture, the vehicle transmitted information to a control centre and also to other vehicles in the vicinity. The system was able to receive data from other devices, authorized in the system, which could be fixed in the field. The collected data were transmitted to a server in a remote location. The obtained results showed a decrease of signal strength while getting further away from the tractor, with good results up to 100 m radius.

Development of Double C-Shaped Left-Handed Metamaterial for Dual-Band Wi-Fi and Satellite Communication Application with High Effective Medium Radio and Wide Bandwidth

The development and improvement of the dual-band 802.11ac standard Wi-Fi and wide bandwidth satellite communication devices are currently research subjects that have garnered significant interest. In this paper, double C-shaped two split-ring resonator (SRR) bounded unit cells were developed, which are applicable for S, C, and X band applications, including dual-band Wi-Fi communication devices and satellite communication applications for its effective medium ratio (EMR) of 15.6, which results in a 2.4 GHz resonance frequency and wide bandwidth (S21 &lt; −10 dB) of 1650 MHz at an 11.5 GHz resonance frequency. A copper resonator and the popular substrate material Rogers RT 5880 (thickness of 1.575 mm) were adopted for analyzing the characteristics of this unit cell. The 8×8 mm2 structure was designed and simulated using a commercially available electromagnetic simulator CST (Computer Simulation Technology) Studio Suite 2019, which was utilized at four resonance frequencies: 2.4 GHz, 5.6 GHz, 8.93 GHz, and 11.5 GHz. The electric field, magnetic field, and surface current distribution were examined by modifying the metamaterial unit cell design structure, showing effective results. To confirm the CST simulation results, the newly designed double C-shaped double-negative metamaterial (DNG) was also simulated with the Ansys High-Frequency Structure Simulator (HFSS) and compared with the extracted results. The suggested metamaterial is advised for usage in Wi-Fi and satellite communication applications for superior long-distance communication performance and efficiency with the compactness of the structure.

Aggregation-Based Dynamic Channel Bonding to Maximise the Performance of Wireless Local Area Networks (WLAN)

Channel bonding is considered by the IEEE 802.11ac amendment to improve wireless local area network (WLAN) performance. In this article, the channel bonding and aggregation method were proposed to increase wireless local area network performance (WLANs). It combines many channels (or lanes) to boost the capacity of modem traffic. Channel bonding is the combination of two neighbouring channels within a certain frequency band to increase wireless device throughput. Wi-Fi employs channel bonding, also known as Ethernet bonding. Channel bandwidth is equal to the uplink/downlink ratio multiplied by the operational capacity. A single 20 MHz channel is divided into two, four, or eight power channels. At 80 MHz, there are more main and smaller channels. Performance of short-range WLANs is determined through graph-based approach. The two-channel access techniques including channel bonding proposed for the IEEE 802.11ac amendment are analysed and contrasted. The novel channel sizing algorithm based on starvation threshold is proposed to expand the channel size to improve WLAN performance. Second-cycle throughput is estimated at 20 Mbps, much beyond the starvation threshold. Our test reveals access points (AP) 1, 2, and 4 have enough throughput. A four-AP WLAN with a 5-Mbps starvation threshold is presented. C160 = 1 since there is only one 160 MHz channel. MIR (3, 160 (a, a, a)) =0, indicating that AP 3’s predicted throughput is 0. The algorithm rejects the 160 MHz channel width since ST is larger than 0. The channel width in MHz is given by B =0,1 MIR. The MIR was intended to maximise simultaneous broadcasts in WLANs. The authors claim that aggregation with channel bonding outperforms so all WLAN APs should have a single-channel width. It usually outperforms fairness-based measures by 15% to 20%. Wi-Fi standards advise “channel bonding,” or using higher frequency channels. Later standards allow channel bonding by increasing bands and channel lengths. Wider channels enhance average WLAN AP throughput, but narrower channels reduce appetite. Finally, it is concluded that APs are more useful than STAs.

Аналитический обзор нововведений стандарта IEEE 802.11ax

Nowadays, due to the rapid development of small-scale wireless networks, the increase in the amount of the traffic consumption (due to the pandemic and the transfer of employees to remote work), the increase in the number of wireless devices and the requirements for the bandwidth of communication channels and different delays arising in them, characteristics of the 802.11ac standard the bandwidth described in this standard were no longer enough. To eliminate these shortcomings, the IEEE 802.11ax standard was introduced in 2019, and finally approved on February 1, 2021. This standard is commercially known as Wi-Fi 6. In the 802.11ax standard, developers focused on energy efficiency, capacity and bandwidth of the communication channel. Improving these parameters resulted in a quadruple increase in the average throughput per user and improved customer experience. These improvements are relevant for both external and internal subscribers. The article presented new technologies used in the IEEE 802.11ax (Wi-Fi 6) standard, such as MU-MIMO, OFDMA, Color Code BSS and Beamforming. MU-MIMO allows providers to simultaneously transmit different data to multiple subscriber stations. OFDMA technology allows providers to increase the bandwidth of the communication channel. The Color Code BSS technology allows us to identify the network as a “friend” or a “foe” when exchanging data between the router and other devices within the 802.11ax standard Wi-Fi network.

A renewal theory based performance and configuration framework of the IEEE 802.11ah RAW mechanism

IEEE 802.11ah is a new Wi-Fi standard for sub-1Ghz communications, aiming to address the challenges of the Internet of Things (IoT). Significant changes in the legacy 802.11 standards have been proposed to improve the network performance in high contention scenarios, the most important of which is the Restricted Access Window (RAW) mechanism. This mechanism promises to increase the throughput and energy efficiency by dividing stations into different groups. Under this scheme, only the stations belonging to the same group may access the channel, which reduces the collision probability in dense scenarios. However, the standard does not define the RAW grouping strategy. In this paper, we develop a new mathematical model based on the renewal theory, which allows for tracking the number of transmissions within the limited RAW slot contention period defined by the standard. We then analyze and evaluate the performance of RAW mechanism. We also introduce a grouping scheme to organize the stations and channel access time into different groups within the RAW. Furthermore, we propose an algorithm to derive the RAW configuration parameters of a throughput maximizing grouping scheme. We additionally explore the impact of channel errors on the contention within the time-limited RAW slot and the overall RAW optimal configuration. The presented analytical framework can be applied to many other Wi-Fi standards that integrate periodic channel reservations. Extensive simulations using the MATLAB software validate the analytical model and prove the effectiveness of the proposed RAW configuration scheme.

SimHumalator: An Open-Source End-to-End Radar Simulator for Human Activity Recognition

Radio-frequency-based noncooperative monitoring of humans has numerous applications ranging from law enforcement to ubiquitous sensing applications such as ambient assisted living and biomedical applications for nonintrusively monitoring patients. Large training datasets, almost unlimited memory capacity, and ever-increasing processing speeds of computers could drive forward the data-driven deep-learning-focused research in the abovementioned applications. However, generating and labeling large volumes of high-quality, diverse radar datasets is an onerous task. Furthermore, unlike the fields of vision and image processing, the radar community has limited access to databases that contain large volumes of experimental data. Therefore, in this article, we present an open-source motion capture data-driven simulation tool, SimHumalator, that can generate large volumes of human micro-Doppler radar data in passive WiFi scenarios. The simulator integrates IEEE 802.11 WiFi Standards (IEEE 802.11 g, n, and ad) compliant transmissions with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics and the sensor parameters. The simulated signatures have been validated with experimental data gathered using an in-house-built hardware prototype. This article describes simulation methodology in detail and provides case studies on the feasibility of using simulated micro-Doppler spectrograms for data augmentation tasks.

Blind CFO Compensation for Next Gen WLAN Systems

With the next Gen Wireless Local Area Networks (WLANs) all geared up to enhance the network wireless capacity in gigabits with utmost reliability for mixed environments utilizing the MIMO OFDM/OFDMA technique with reduced subcarrier spacing and denser modulation schemes. These stern requirements make the system to be highly susceptible to Carrier Frequency Offset (CFO) and the IEEE standard specifies a very stringent frequency offset limit to be well within ±20ppm which is a demanding ask to be met to keep all the WiFi stations to operate within their specified channels with minimum spectral leakage. The current article proposes a novel blind carrier frequency offset compensation method which efficiently computes the CFO. The simulation results prove that the performance of the proposed algorithm not only satisfies the prescribed limits of ±20ppm but it is also robust to even higher offsets like ±25ppm. The proposed algorithm can be utilized in WiFi standards such as IEEE 802.11 n/ax. Rayleigh interference with additive white Gaussian noise (AWGN) channel was applied to analyze the algorithm’s performance. This technique offers better synchronization and reliable communication.