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Field-Programmable Gate Array (FPGA)-based Software-Defined Radio (SDR) is well-suited for experimenting with advanced wireless communication systems, as it allows to alter the architecture promptly while obtaining high performance. However, programming the FPGA using a Hardware Description Language (HDL) is a time-consuming task for FPGA developers and difficult for software developers, which limits the potential of SDR. High-Level Synthesis (HLS) tools aid the designers by allowing them to program on a higher layer of abstraction. However, if not carefully designed, it may lead to a degradation in computing performance or significant increase in resource utilization. This work shows that it is feasible to design modern Orthogonal Frequency Division Multiplex (OFDM) baseband processing modules like channel estimation and equalization using HLS without sacrificing performance and to integrate them in an HDL design to form a fully-operational FPGA-based Wi-Fi (IEEE 802.11a/g/n) transceiver. Starting from no HLS experience, a design with minor overhead in terms of latency and resource utilization as compared to the HDL approach was created in less than one month. We show the readability of the sequential logic as coded in HLS, and discuss the lessons learned from the approach taken and the benefits it brings for further design and experimentation. The FPGA design generated by HLS was verified to be bit-true with its MATLAB implementation in simulation. Furthermore, we show its practical performance when deployed on a System-on-Chip (SoC)-based SDR using a professional wireless connectivity tester., Comment: 7 pages, extended version of poster accepted at FCCM 2023

Software Defined Radio (SDR) platforms are valuable for research and development activities or high-end systems that demand real-time adaptable wireless protocols. While low latency can be achieved using the dedicated digital processing unit of a state-of-the-art SDR platform, its Radio Frequency (RF) front-end often poses a limitation in terms of turnaround time (TT), the time needed for switching from the receiving to the transmitting mode (or vice versa). Zero Intermediate Frequency (ZIF) transceivers are favorable for SDR, but suffer from self-interference even if the device is not currently transmitting. The strict MAC-layer requirements of Time Division Duplex (TDD) protocols like Wi-Fi cannot be achieved using configurable ZIF transceivers without having to compromise receiver sensitivity. Using a novel approach, we show that the TT using the AD9361 RF front-end can be as low as 640 ns, while the self-interference is at the same level as achieved by the conventional TDD mode, which has a TT of at least 55 {\mu}s. As compared to Frequency Division Duplex (FDD) mode, a decrease of receiver noise floor by about 13 dB in the 2.4 GHz band and by about 4.5 dB in the 5 GHz band is achieved., Comment: Accepted by IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2022) as Work-In-Progress paper

CSI (Channel State Information) of WiFi systems contains the environment channel response between the transmitter and the receiver, so the people/objects and their movement in between can be sensed. To get CSI, the receiver performs channel estimation based on the pre-known training field of the transmitted WiFi signal. CSI related technology is useful in many cases, but it also brings concerns on privacy and security. In this paper, we open sourced a CSI fuzzer to enhance the privacy and security of WiFi CSI applications. It is built and embedded into the transmitter of openwifi, which is an open source full-stack WiFi chip design, to prevent unauthorized sensing without sacrificing the WiFi link performance. The CSI fuzzer imposes an artificial channel response to the signal before it is transmitted, so the CSI seen by the receiver will indicate the actual channel response combined with the artificial response. Only the authorized receiver, that knows the artificial response, can calculate the actual channel response and perform the CSI sensing. Another potential application of the CSI fuzzer is covert channels based on a set of pre-defined artificial response patterns. Our work resolves the pain point of implementing the anti-sensing idea based on the commercial off-the-shelf WiFi devices., Comment: Accepted by ACM WiSec 2021

The society as a whole, and many vertical sectors in particular, is becoming increasingly digitalized. Machine Type Communication (MTC), encompassing its massive and critical aspects, and ubiquitous wireless connectivity are among the main enablers of such digitization at large. The recently introduced 5G New Radio is natively designed to support both aspects of MTC to promote the digital transformation of the society. However, it is evident that some of the more demanding requirements cannot be fully supported by 5G networks. Alongside, further development of the society towards 2030 will give rise to new and more stringent requirements on wireless connectivity in general, and MTC in particular. Driven by the societal trends towards 2030, the next generation (6G) will be an agile and efficient convergent network serving a set of diverse service classes and a wide range of key performance indicators (KPI). This white paper explores the main drivers and requirements of an MTC-optimized 6G network, and discusses the following six key research questions: - Will the main KPIs of 5G continue to be the dominant KPIs in 6G; or will there emerge new key metrics? - How to deliver different E2E service mandates with different KPI requirements considering joint-optimization at the physical up to the application layer? - What are the key enablers towards designing ultra-low power receivers and highly efficient sleep modes? - How to tackle a disruptive rather than incremental joint design of a massively scalable waveform and medium access policy for global MTC connectivity? - How to support new service classes characterizing mission-critical and dependable MTC in 6G? - What are the potential enablers of long term, lightweight and flexible privacy and security schemes considering MTC device requirements?, Comment: White paper by http://www.6GFlagship.com

The wireless industry is driven by key stakeholders that follow a holistic approach of "one-system-fits-all" that leads to moving network functionality of meeting stringent E2E communication requirements towards the core and cloud infrastructures. This trend is limiting smaller and new players for bringing in new and novel solutions. For meeting these E2E requirements, tenants and end-users need to be active players for bringing their needs and innovations. Driving E2E communication not only in terms of QoS but also overall carbon footprint and spectrum efficiency from one specific community may lead to undesirable simplifications and a higher level of abstraction of other network segments may lead to sub-optimal operations. Based on this, the paper presents a paradigm shift that will enlarge the role of wireless innovation at academia, SME's, industries and start-ups while taking into account decentralized mandate-driven intelligence in E2E communications, Comment: internal organisation policy

This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack (PHY, MAC and network). First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning for non-machine learning experts to understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed., Comment: 35 pages, survey

The third Global Experimentation for Future Internet (GEFI 2018) workshop was held October 25-26, 2018 in Tokyo, Japan, hosted by the University of Tokyo. A total of forty-four participants attended, representing Belgium, Brazil, China, Denmark, France, Ireland, Japan, the Republic of Korea, and the United States. The workshop employed a mixed format of presentations and open group discussions to advance multi-national coordination and interoperation of research infrastructure for advanced networking and computer science research. Major topic areas included: softwareization and virtualization of radios and networks; testbed support for networking experiments; EdgeNet; a federated testbed of elastic optical networks; and reproducibility in experimentation. Workshop goals included both the formulation of specific new research collaborations and strategies for coordination and interoperation of research testbeds. Workshop outcomes include a variety of new and ongoing collaborative efforts, ranging from an agreement to pursue the development of optical "white boxes" in support of elastic optical testbeds to the identification of strategies for effective use of open-source software and hardware platforms in future research infrastructure.

Industrial wireless sensor networks (WSNs) are being used to improve the efficiency, productivity and safety of industrial processes. An open standard that is commonly used in such cases is IEEE 802.15.4e. Its TSCH mode employs a time synchronized based MAC scheme together with channel hopping to alleviate the impact of channel fading. Until now, most of the industrial WSNs have been designed to only support static nodes and are not able to deal with mobility. In this paper, we show how a single-hop, multi-gateway IEEE 802.15.4e TSCH network architecture can tackle the mobility problem. We introduce the Virtual Grand Master (VGM) concept that moves the synchronization point from separated Backbone Border Routers (BBRs) towards the backbone network. With time synchronization of all BBRs, mobile nodes can roam from one BBR to another without time desynchronization. In addition to time synchronization, we introduce a mechanism to synchronize the schedules between BBRs to support fast handover of mobile nodes., Comment: Short paper version of a paper submitted to Ad-Hoc Networks Journal by Elsevier

Many IoT applications make extensive use of constrained devices which are characterized by unexpected failures. In addition, nodes could be put offline temporarily for maintenance (e.g. battery replacement). These incidents lead to loss of dynamic data that is generated due to the interactions between nodes. For instance, configuration settings adjusted by sending PUT request to the sensor will be lost when the node is rebooted. The lost data, which we call dynamic states, leads to erroneous results or malfunctions of the IoT application. In this paper, we introduce an intelligent dynamic state recovery mechanism through deep packet inspection. A State Directory, placed at the gateway, intercepts every communication between an external device and constrained devices and stores (or updates) information that is important to restore dynamic states. When a node reports a reboot, the state directory replays the packets that generated the dynamic state so that all dynamic states are restored. We implemented the solution on a non-constrained device that acts as a gateway to the constrained network and tested the results using Cooja simulator.

This paper presents end-to-end learning from spectrum data - an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to (i) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments, and (ii) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this article is to present the conceptual framework of end-to-end learning for spectrum monitoring and systematically introduce a generic methodology to easily design and implement wireless signal classifiers. Furthermore, we investigate the importance of the choice of wireless data representation to various spectrum monitoring tasks. In particular, two case studies are elaborated (i) modulation recognition and (ii) wireless technology interference detection. For each case study three convolutional neural networks are evaluated for the following wireless signal representations: temporal IQ data, the amplitude/phase representation and the frequency domain representation. From our analysis we prove that the wireless data representation impacts the accuracy depending on the specifics and similarities of the wireless signals that need to be differentiated, with different data representations resulting in accuracy variations of up to 29%. Experimental results show that using the amplitude/phase representation for recognizing modulation formats can lead to performance improvements up to 2% and 12% for medium to high SNR compared to IQ and frequency domain data, respectively. For the task of detecting interference, frequency domain representation outperformed amplitude/phase and IQ data representation up to 20%., Comment: 16 pages, journal

As LoRaWAN networks are actively being deployed in the field, it is important to comprehend the limitations of this Low Power Wide Area Network technology. Previous work has raised questions in terms of the scalability and capacity of LoRaWAN networks as the number of end devices grows to hundreds or thousands per gateway. Some works have modeled LoRaWAN networks as pure ALOHA networks, which fails to capture important characteristics such as the capture effect and the effects of interference. Other works provide a more comprehensive model by relying on empirical and stochastic techniques. This work uses a different approach where a LoRa error model is constructed from extensive complex baseband bit error rate simulations and used as an interference model. The error model is combined with the LoRaWAN MAC protocol in an ns-3 module that enables to study multi channel, multi spreading factor, multi gateway, bi-directional LoRaWAN networks with thousands of end devices. Using the lorawan ns-3 module, a scalability analysis of LoRaWAN shows the detrimental impact of downstream traffic on the delivery ratio of confirmed upstream traffic. The analysis shows that increasing gateway density can ameliorate but not eliminate this effect, as stringent duty cycle requirements for gateways continue to limit downstream opportunities., Comment: 12 pages, submitted to the IEEE Internet of Things Journal

The future 5G wireless infrastructure will support any-to-any connectivity between densely deployed smart objects that form the emerging paradigm known as the Internet of Everything (IoE). Compared to traditional wireless networks that enable communication between devices using a single technology, 5G networks will need to support seamless connectivity between heterogeneous wireless objects and IoE networks. To tackle the complexity and versatility of future IoE networks, 5G will need to guarantee optimal usage of both spectrum and energy resources and further support technology-agnostic connectivity between objects. One way to realize this is to combine intelligent network control with adaptive software defined air interfaces. In this paper, a flexible and compact platform is proposed for on-the-fly composition of low-power adaptive air interfaces, based on hardware/software co-processing. Compared to traditional Software Defined Radio (SDR) systems that perform computationally-intensive signal processing algorithms in software, consume significantly power and have a large form factor, the proposed platform uses modern hybrid FPGA technology combined with novel ideas such as RF Network-on-Chip (RFNoC) and partial reconfiguration. The resulting system enables composition of reconfigurable air interfaces based on hardware/software co-processing on a single chip, allowing high processing throughput, at a smaller form factor and reduced power consumption., Comment: 9 pages, 3 figures

Predictable network performance is key in many low-power wireless sensor network applications. In this paper, we use machine learning as an effective technique for real-time characterization of the communication performance as observed by the MAC layer. Our approach is data-driven and consists of three steps: extensive experiments for data collection, offline modeling and trace-driven performance evaluation. From our experiments and analysis, we find that a neural networks prediction model shows best performance., Comment: 2 pages, 3 figures, accepted for publication in the EWSN'17 Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, Uppsala, Sweden - February 20-22, 2017

To enhance system performance of future heterogeneous wireless networks the co-design of PHY, MAC, and higher layer protocols is inevitable. In this work, we present WiSCoP - a novel embedded platform for experimentation, prototyping and implementation of integrated cross-layer network design approaches. WiSCoP is built on top of a Zynq hardware platform integrated with FMCOMMS1/2/4 RF front ends. We demonstrate the flexibility of WiSCoP by using it to prototype a fully standard compliant IEEE 802.15.4 stack with real-time performance and cross-layer integration., Comment: 2 pages, 2 figures, to be published in the EWSN'17 Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, Uppsala, Sweden - February 20-22, 2017