An Empirical Analysis of IEEE 802.11ax Muhammad, Siraj, Zhao, Jiamiao, and Refai, Hazem In 2020 International Conference on Communications, Signal Processing, and their Applications (ICCSPA) 2020
An empirical analysis of the newly released standard IEEE 802.11ax, widely known as Wi-Fi 6, is presented in this paper. Several tests were conducted to evaluate key performance metrics, including throughput and jitter as a function of network parameters (e.g., packet size and window size), as well as environment variables (e.g., SNR). Empirical models were developed using collected results to quantify the behavior of said metrics. Channel utilization of the system was also investigated and compared to its precedent, 802.11ac.
Revisiting Unlicensed Channel Access Scheme of 5G New Radio Muhammad, Siraj, and Refai, Hazem 2020
As the second phase of 5G standardization efforts encapsulated in Release 16 comes to its freeze and completion date in June 2020, aspects of some promised features and services started to crystallize. Among of which, New Radio (NR)-based access to unlicensed spectrum, commonly known as 5G NR-U. Current technical reports have identified Listen-Before-Talk (LBT) as a working assumption in the process of standardizing NR-U channel access scheme. LBT was originally developed for Licensed-Assisted Access (LAA) in release 13 of the 3GPP specifications, which was based on ETSI regulations. This research examines how next-generation wireless systems using LBT perform under vastly presumed 5G NR dense deployments, and how the coexistence landscape manifests in the homogeneous prospect rather than the widely investigated heterogeneous counterpart, e.g. with Wi-Fi. In this work, a simulator was developed in C++ to help analyze different intra-network NR-U co-channel scenarios under saturated traffic. The simulator was validated with Markov Chain analytical model to confirm the procedures and algorithms conform to the standard delineated by the 3GPP specifications. Simulation results indicated inefficiency in channel utilization of homogeneous dense deployments with high priority traffic classes. For instance, the effective channel utilization drops to less than 10% when only 20 devices share the channel with traffic tagged as priority 4, e.g., voice calls. Moreover, mean delay between successful packet transmissions in aforesaid scenario turned out to be around 1 second and exponentially increasing with the number of devices sharing the channel. We demonstrated through simulations how LBT devices can be unfair when sharing the channel with others exhibiting different traffic priority classes. A video streaming device – i.e. class 3 – for example, takes away 42% of the channel when sharing it with other 7 devices browsing the internet – i.e. class 2 – leaving them with 34% of useful channel time to split. The remaining 24% of the time packets collide with each other, rendering the channel futile and reducing the overall throughput. Literature is inundated with research on cross-technology coexistence analysis. This work aims to study same-technology wireless coexistence performance and underlines the importance of improving channel access mechanisms in next-generation wireless communication.
Ultra-Low Power IoT Traffic Monitoring System Muhammad, Siraj, Refai, Hazem, and Blakeslee, Matthew In 2018 IEEE 88th Vehicular Technology Conference (VTC 2018-Fall) 2018
Given the sizable anticipated proliferation of Internet of Things (IoT) devices, Forrester Research forecasts that the fleet management and transportation industry sectors will enjoy more growth than others. This may come as no surprise, since infrastructure (eg, roadways, bridges, airports) is a prime candidate for sensor integration to provide real-time measurements and to support intelligent decisions. The predicted increase of deployed devices makes it difficult to calculate the amount of energy required for these functions. Current estimates suggest that 2 to 4% of worldwide carbon emissions can be attributed to the information and communication industry . This paper presents novel algorithms designed to optimize power consumption of an intelligent vehicle counter and classifier sensors. Each was based on an event-driven methodology wherein a control block orchestrates the work of different components and subsystems. System duty-cycle is reduced through several techniques, and a reinforcement learning algorithm is introduced to control the system power policy, according to the traffic environment. Battery life for a sensor supported by a 2300 mAh battery was extended from 48-hour, adopted all-on policy to more than 400 days when leveraging the algorithms and techniques presented in this work.
Intelligent Power Aware Algorithms for Traffic Sensors Muhammad, Siraj 2018
The Internet of Things (IoT) is reshaping our world. Soon our world will be based on smart technologies. According to IHS Markit forecasts, the number of connected devices will grow from 15.4 billion in 2015 to 30.7 billion in 2020. Forrester Research predicts that fleet management and the transportation sectors lead others in IoT growth. This may come as no surprise, since the infrastructure (roadways, bridges, airports, etc.) is a prime candidate for sensor integration, providing real-time measurements to support intelligent decisions. The energy cost required to support the anticipated enormous number of predicted deployed devices is unknown. Currently, experts estimate that 2 to 4% of worldwide carbon emissions can be attributed to power consumption in the information and communication industry . This thesis presents several algorithms to optimize power consumption of an intelligent vehicle counter and classifier sensor (iVCCS) based on an event-driven methodology wherein a control block orchestrates the work of various components and subsystems. Data buffering and triggered vehicle detection techniques were developed to reduce duty cycle of corresponding components (e.g., microSD card, magnetometer, and processor execution). A sleep mode is also incorporated and activated by an artificial intelligence-enabled, reinforcement learning algorithm that utilizes the field environment to select proper processor mode (e.g., run or sleep) relative to traffic flow conditions. Sensor life was extended from 48 hours to more than 200 days when leveraging 2300 mAh battery along with algorithms and techniques introduced in this thesis.
Wireless technology identification using deep Convolutional Neural Networks Bitar, Naim, Muhammad, Siraj, and Refai, Hazem H. In 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) 2017
With the proliferation of wireless technologies and the ever-increasing growth in Internet of Things (IoT) devices operating the license-free Industrial, Scientific, and Medical (ISM) band, intelligent access systems capable of coexisting in crowded spectrum regions are of vital importance. In this work we study the adaptation of Convolutional Neural Networks (CNNs) to the problem of identifying coexisting wireless devices. We develop a machine learning conduit to facilitate the detection and identification of frequency domain signatures for 802.x standard compliant technologies. Spectrum scans across the entire ISM region (80-MHz) are recorded and a data-driven training process for a wide range of Signal-to-Noise Ratios (SNRs) is completed. Model accuracy is compared to that attained using standard feature based classification methods. Results indicate CNN models outperform their counterpart methods in terms of classification accuracy, connoting them to be highly effective tools for detecting and identifying coexisting devices despite acute overlap and interference presence. The proposed approach aims to advance cognitive wireless awareness by enhancing automatic detection and identification accuracy.
On the performance of WLAN and Bluetooth for in-car infotainment systems Mourad, Alaa, Muhammad, Siraj, Al Kalaa, Mohamad Omar, Refai, Hazem H., and Hoeher, Peter Adam Vehicular Communications 2017
The connected car is ushering in a new era of automotive design. Driven by increasing customer demand for connectivity and advances in electronics, connected cars are now equipped with advanced infotainment systems with a variety of applications. Seamless integration of consumer electronic (CE) devices into car infotainment systems is crucial for mimicking home and office user experience. Because wireless communication is more user-friendly than wired communication, it has become the preferred method for connecting CE devices to car infotainment systems. WLAN1and Bluetooth2are the most promising technologies for this purpose. Both systems operate in the spectrum-scarce 2.4 GHz unlicensed industrial, scientific and medical (ISM) radio band. The coexistence between WLAN and Bluetooth has garnered a significant amount of attention from both academic and industry researchers. However, the unique features of vehicle mobility and the high density of devices in a limited roadway area necessitate further investigation in the automotive domain. This paper focuses on the coexistence between WLAN and Bluetooth systems among vehicle infotainment applications, and on WLAN co-channel interference. Performance is evaluated using experimental measurements in real-world scenarios. The mobility effect is studied in detail. Results show that an onboard WLAN network is strongly affected by the surrounding networks. Coexistence duration decreases exponentially with relative speed between automobile networks. WLAN effect on Bluetooth is extremely high when WLAN’s non-overlapped channels 1, 6, and 11 are simultaneously occupied. WLAN interference leads to a significant number of clippings in Bluetooth audio signals, especially in high WLAN traffic load situations. An exponential decease in the number of clipping events as a function of speed is observed.
Bluetooth and IEEE 802.11n system coexistence in the automotive domain Mourad, Alaa, Muhammad, Siraj, Al Kalaa, Mohamad Omar, Hoeher, Peter Adam, and Refai, Hazem In IEEE Wireless Communications and Networking Conference, WCNC 2017
Connected cars have telecommunication services similar to those found in homes and offices. Passengers have come to expect efficiently using time spent in their cars working and engaging in entertainment. Fulfilling this desire requires advanced infotainment systems with a variety of capabilities and functions similar to mobile phones. As people become more attached to their mobile phones, seamless integration of phones into the car computer becomes more crucial. Bluetooth and IEEE 802.11 systems are often used to connect mobile phones to car computers. Both technologies operate in the 2.4 GHz unlicensed industrial, scientific and medical (ISM) radio bands. However, since early development of standards governing the ISM band, coexistence among devices sharing the band have been under close scrutiny. An increased use of Bluetooth and 802.11 systems in the automotive domain and the logistics of having extremely small distances between devices makes coexistence a challenging task. This paper presents performance evaluation of both WLAN and Bluetooth for typical automotive domain applications (i.e., music streaming and hands-free calling). Focused attention is paid to a scenario in which all three non overlapped WLAN channels are used. The effect of traffic load and WLAN power level are investigated. Results demonstrate that Bluetooth channels 71 to 78 are critical to maintain acceptable Bluetooth connectivity. Hands-free calling is more sensitive to interference than music streaming. Bluetooth effect on WLAN is small.