Most works on intelligent reconfigurable surface (IRS)-assisted networks consider link-level simulations and do not address the upper protocol layers. This limitation becomes particularly relevant in practical systems like 802.11 Wi-Fi, where channel access is based on listen-before-talk (LBT) scheme, making conventional IRS scheduling strategies (time-division or centralized coordination) difficult to apply. Given these reasons, we shift the focus to random channel access based on carrier sense multiple access with collision avoidance (CSMA/CA) to explore how an IRS can be beneficial in such scenarios. We first discover that improving the SNR of links from stations (STAs) to an access point (AP) introduces the potential risk of hidden terminals among the STAs in the uplink. To analyze this, we introduce ns3IRS, a framework that integrates a model of an IRS into the ns-3 network simulator, which allows us to run the full WiFi stack within an IRS-assisted wireless network. We propose two solutions to mitigate the hidden terminal problem caused by the IRS also by assuring mutual carrier sensing: (i) splitting a single centralized IRS, or (ii) using additional small IRSs nearby the STAs – to create direct links among them, with the purpose of enabling mutual carrier sensing. Results show that these two solutions can improve the throughput compared to the traditional approach where virtual carrier sensing, i.e., CSMA/CA with Request-to-Send/Clear-to-Send (RTS/CTS), is used. For instance, for a system with four users, the proposed approaches improve the throughput from 25Mbit/s to around 35Mbit/s compared to the baseline scenario with RTS/CTS. Additionally, latency and jitter are decreased. However, as the number of users increases, achieving performance gains requires a larger IRS, since the gain scales with the number of reflecting elements, especially for the centralized IRS. In scenarios where scaling the IRS is impractical, CSMA/CA with RTS/CTS may become a more effective alternative.

Cloud radio access network (C-RAN) in cellular systems offers improved coverage and efficiency due to statistical multiplexing. Applying centralization to Wi-Fi networks could yield similar benefits, particularly in dense deployments like campus networks. However, this is challenging due to the usage of unlicensed, shared spectrum and strict protocol timing constraints like carrier sensing, making such systems highly sensitive to C-RAN induced delays. Existing approaches typically rely on radio-over-fiber and either demand very low delay or neglect neighboring Wi-Fi networks. In this paper, we present Ce-Fi, a centralized Wi-Fi architecture operating over a packet-based fronthaul (FH) connecting the central unit with the remote radio heads. Ce-Fi employs a two-level design with two bands: the network discovery and user association is handled standard compliantly on one band while another band is used for the actual data transmission with the centralized Wi-Fi. To tolerate the FH-induced delay, we propose minor changes to the 802.11 medium access control protocol like NAV extension and the usage of a piggybacking mechanism enabling the C-RAN-AP to transmit without prior channel contention. We evaluate Ce-Fi through simulations, demonstrating that it supports high throughput even under high FH-delays and in the presence of neighboring legacy networks.

Millimeter wave (mmWave) spectrum is associated with abundance of available spectrum and excellent spectrum reuse, but also has the significant downside of decreased resilience. Communication can be disrupted when the line of sight (LOS) path is blocked, or the link quality may degrade due to obstacles. These issues are exacerbated by mobility. One possible countermeasure against such signal shadowing and blockage is the use of intelligent reconfigurable surfaces (IRSs). However, while IRSs have the advantage to form a beam directly targeting the user, user-tracking and the need to orchestrate the IRS are very challenging. A practical solution towards improving the resilience is the use of non-reconfigurable reflecting surfaces (NRRSs), which enable communication through reflections by creating additional signal paths via a pre-configured reflection pattern. With sufficient number of reflections through NRRSs, the probability of communication outage is significantly reduced. The additional signal paths in NRRS-assisted networks can be very useful in scenarios characterized by repetitive mobility patters, especially since the cost and complexity are heavily reduced compared to IRS. While the benefits of IRS deployment have been extensively studied, most analyses are limited to the simulation domain. In contrast, this paper provides practical insights derived from measurements conducted on a mmWave band testbed. Results reveal that even with a few NRRSs, the resilience of a mmWave communication link could be dramatically improved resulting in close-to-zero packet loss. However, the channel becomes highly frequency-selective, which makes the use of advanced physical layers like IEEE 802.11be with adaptive modulation & coding per resource unit (RU) a necessity.

Next-generation Wi-Fi networks are expected to have an ultra-dense deployment of access points (APs), thus, interference from overlapping basic service sets (OBSSs) poses challenges for interference management. Wi-Fi 8 aims at mitigating such interference using multi-access point coordination (MAPC). One of the MAPC variants is coordinated beamforming (Co-BF), where neighboring APs direct their signals towards specific users. Besides beam steering, APs can also perform null steering, which is more complex but can bring greater performance gains. In this paper, we present a centralized approach named intelligent null steering by reinforcement learning (IntelliNull), designed to reduce interference from neighboring transmitters by coordinated nulling while maximizing the signal quality at each station. We show that training the beam and null steering mechanism with a deep deterministic policy gradient (DDPG), it is possible to steer beams toward associated stations while intelligently nulling the most destructive interference from OBSS rather than nulling random interference directions. This method enhances communication between the AP and neighboring stations by reducing channel access contention, enabling transmissions at full power, and reducing worst-case latency. The proposed IntelliNull agent continuously adapts to changes in the network environment, including node mobility using channel state information (CSI) collected in real-time. We also compare our IntelliNull, which is based on beamforming plus nulling, with the baseline which is based on beamforming only. Our results demonstrate that IntelliNull outperforms the baseline by effectively mitigating interference, leading to higher throughput and better signal-to-interference-plus-noise ratio (SINR), especially in dense deployment scenarios where beamforming alone fails to sufficiently suppress OBSS interference.

The densification of Wi-Fi deployments means that fully distributed random channel access is no longer sufficient for high and predictable performance. Therefore, the upcoming IEEE 802.11bn amendment introduces multi-access point coordination (MAPC) methods. This paper addresses a variant of MAPC called coordinated spatial reuse (C-SR), where devices transmit simultaneously on the same channel, with the power adjusted to minimize interference. The C-SR scheduling problem is selecting which devices transmit concurrently and with what settings. We provide a theoretical upper bound model, optimized for either throughput or fairness, which finds the best possible transmission schedule using mixed-integer linear programming. Then, a practical, probing-based approach is proposed which uses multi-armed bandits (MABs), a type of reinforcement learning, to solve the C-SR scheduling problem. We validate both classical (flat) MAB and hierarchical MAB (H-MAB) schemes with simulations and in a testbed. Using H-MABs for C-SR improves aggregate throughput over legacy IEEE 802.11 (on average by 80% in random scenarios), without reducing the number of transmission opportunities per station. Finally, our framework is lightweight and ready for implementation in Wi-Fi devices.

Multi-band splicing offers a promising solution to extend existing band-limited communication systems to support high-precision sensing applications. This technique involves performing narrow-band measurements at multiple center frequencies, which are then combined to effectively increase the bandwidth without changing the sampling rate. In this paper, we introduce a mmWave channel sounder based on multiband splicing, leveraging the sparse nature of wireless channels through compressed sensing and sparse recovery techniques for channel reconstruction. We focus on three sparse recovery methods: the widely used grid-based orthogonal matching pursuit (OMP) algorithm as a baseline, our newly developed two-stage mmSplicer algorithm, which extends the OMP method by introducing an additional stage for improving its performance for our application, and our adaptation of sparse reconstruction by separable approximation (SpaRSA), named Net-SpaRSA, optimized for wireless applications. All three algorithms are integrated into an experimental OFDM-based IEEE 802.11ac system. Our analysis centers on evaluating the performance of these algorithms under limited number of narrow-band measurements, demonstrating that accurate CIR estimation is achievable even using only 50% of the full wideband spectrum. Additionally, we analyze and compare the computational complexity of these algorithms to assess their practical feasibility

As an integration of digital twin and edge computing, the digital twin edge networks (DITENs) have been proposed in recent years to fill the gap between physical edge networks and digital systems. Meanwhile, the multi-access wireless environments in edge computing make it hard to provide ultra-reliable and low-latency communications for digital twin, especially when the jamming attacks can be launched by the adversaries. This paper studies the jamming-resilient physical-to-virtual communication (PTVC) problem in DITENs despite strong cooperative jamming. Note that the previous jamming models mainly focus on the jamming behaviors from individual adversaries and are restricted by the energy budget limitation and uniform jamming assumption. In this paper, we consider a more comprehensive jamming model, in whichfadversaries can cooperatively launch their jamming attacks in totallykwireless channels with unlimited power budget and non-uniform jamming signals. Then, based on the new proposed$(k,f)$-cooperative jamming model, we show that$k\gt f$is the necessary and also sufficient condition to solve the PTVC problem. On one hand, we prove that the PTVC problem is insoluble when$f\leq k$; on the other hand, two distributed algorithms are given as the solutions of the PTVC problem amongnphysical objectives and one sink node when$k\gt f$, the time complexity of which are$O\left ({{\frac {n \log n}{k (\log k-\log f)}}}\right)$and$O\left ({{\frac {n \log n}{\log k-\log f}}}\right)$based on the communication modes with/without acknowledgement, respectively. Both of the theoretical results and empirical simulations are conducted to show the resilience of our algorithms despite such a strong cooperative jamming model.

Adaptive beamforming is an enabling technology for millimeter-wave-based wireless communication which is used by many standards like 3GPP NR and IEEE 802.11ay. Supporting user mobility is challenging as efficient beam tracking is required. Therefore, a variety of beam tracking techniques have been proposed, many of which are machine learning (ML)-based. However, ML approaches are often not of practical use due to the long and complex learning phase. In this paper, we show the feasibility of virtual to real-world transfer learning. Our solution significantly speeds up the learning process as the learning happens mostly in a simulated environment and requires only little additional learning in the real-world deployment. As proof-of-concept, we implemented and evaluated a low-complexity beam tracking based on deep Q-network (DQN) reinforcement learning. The results reveal a substantial speed-up by a factor of 3× using transfer learning.

We present ToFMD, a system that can passively detect the movement of an indoor device-free user by relying on the absorptions and reflections of WiFi signals off his body. Our system is of practical use as it is able to reuse existing deployed commodity 802.11 infrastructure networks. It is based on the observation that the movement of an user is altering the multipath characteristics of the wireless channel which leads to changes in the measured time-of-flight (ToF) between adjacent WiFi access points. The system can detect moving people at different places in the room, it is not necessary that the user blocks the line-of-sight (LOS) path. In contrast to other approaches, our system works with inexpensive single antenna WiFi systems without access to low-level parameters like channel state information (CSI). Instead the ToF between any pair of access points is estimated using the fine time measurement (FTM) protocol, which is standardized within IEEE 802.11. The movement is detected with the help of a one class support vector machine. As proof of concept, we implemented our system with ultra-low cost ESP32 microcontrollers. The evaluation shows that our solution outperforms traditional approaches using RSSI except for the scenario with user motion in adjacent rooms. If our system is installed in the same room as the user, it is able to detect its movement with an accuracy of at least 95 %.

Resilient wireless networks ensure that the network maintains functionality despite adverse conditions like hardware and software failures, adversarial jamming attacks, or environmental changes like wireless channel fading which can disrupt connectivity. We show that Cross-Technology Communication (CTC), which was developed to enable direct communication between otherwise incompatible radio technologies, can be utilized to improve the resilience in wireless networks. This is achieved by increasing the number of independent wireless links between adjacent wireless nodes as well as by the increase in communication distance resulting in increased path diversity. Results from our analysis reveal that the same level of resilience can be achieved with reduced number of radio interfaces per node while tolerating an order of magnitude higher node failure rate. Moreover, such CTC-enabled networks are more resilient to advanced technology-specific jamming attacks.

LiFi has become an interesting and now more mature WiFi complement. To overcome the coverage limitations of LiFi and to create robustness against signal blockage, we use analog relays that cooperate with each other in both transmissions and signal reception. We present an ultra low-cost cascaded PLC-LiFi communication system comprising Power-Line Communication (PLC) for backhauling and Light Fidelity (LiFi) for wireless access. The interworking between the two technologies is achieved through low-complexity analog Amplify-and-Forward Relaying (AFR), i.e., the luminaries act as simple media converter. Results reveal the impact of amplification of AFR on the SNR as well as the relationship between the PLC and the optical wireless link length. Moreover, even so the gain from cooperative communication is substantial it is limited due to noise propagation in AFR.

Appropriate data rate selection at the physical layer is crucial for Wi-Fi network performance: too high rates lead to loss of data frames, while too low rates cause increased latency and inefficient channel use. Most existing methods adopt a probing approach and empirically assess the transmission success probability for each available rate. However, a transmission failure can also be caused by frame collisions. Thus, each collision leads to an unnecessary decrease in the data rate. We avoid this issue by resorting to the fine timing measurement (FTM) procedure, part of IEEE 802.11, which allows stations to perform ranging, i.e., measure their spatial distance to the AP. Since distance is not affected by sporadic distortions such as internal and external channel interference, we use this knowledge for data rate selection. Specifically, we propose FTMRate, which applies statistical learning (a form of machine learning) to estimate the distance based on measurements, predicts channel quality from the distance, and selects data rates based on channel quality. We define three distinct estimation approaches: exponential smoothing, Kalman filter, and particle filter. Then, with a thorough performance evaluation using simulations and an experimental validation with real-world devices, we show that our approach has several positive features: it is resilient to collisions, provides near-instantaneous convergence, is compatible with commercial-off-the-shelf devices, and supports pedestrian mobility. Thanks to these features, FTMRate outperforms existing solutions in a variety of line-of-sight scenarios, providing close to optimal results. Additionally, we introduce Hybrid FTMRate, which can intelligently fall back to a probing-based approach to cover non-line-of-sight cases. Finally, we discuss the applicability of the method and its usefulness in various scenarios.

The recent move of the LoRa wireless technology towards the wider 2.4 GHz ISM spectrum band creates multiple opportunities for its broader usage in new IoT applications requiring larger data rates. However, this ISM band is already crowded with other wireless technologies like WiFi or Bluetooth, and cross-technology interference issues need to be considered. Moreover, the less favorable radio propagation properties in the 2.4 GHz band shorten the communication distance of LoRa transmissions and therefore make relaying necessary. Unfortunately, the direct usage of LoRa technology in a wireless mesh network is challenging due to the non-existent generic frame preamble and header, i.e. LoRa frames are not self-contained. This makes the dynamic adaptation of the modulation used by LoRa per link or even packet transmission challenging. We show in this paper that such limitation can be partially overcome by utilizing the macro spacial diversity provided by opportunistic routing (OR) protocols. In contrast to traditional routing OR selects a candidate set of nodes per hop along the path in the mesh network. Thus any temporal channel fading and local interference conditions will not result in a failed transmissions due to utilization of the spatial diversity provided by OR. The usage of OR achieves a much higher E2E packet delivery rate as well as lower E2E delay as compared to traditional routing while the overhead is reasonable. Moreover, with OR the same signal modulation can be used network-wide and its adjustment can be made on a long-term basis.

We presentHy-Fi, a system that aggregates light fidelity (LiFi) and radio frequency (RF)-based communication on the 802.11 (WiFi) physical layer by utilizing the MIMO capabilities in IEEE 802.11-compliant commodity WiFi chips.Hy-Fiis based on two key ideas. First, we use inexpensive commodity hardware to facilitate direct transmission of WiFi waveforms over the optical wireless channel, as this is proposed in the IEEE P802.11bb task group. Second, we use the MIMO signal processing from WiFi to aggregate LiFi and radio signals directly at the physical layer.Hy-Fiwas implemented as a prototype and evaluated in a small testbed. Experimental results reveal that our approach offers excellent robustness against signal fading, blockage and external interference in both, the optical and radio channels making it suitable for applications with very strict requirements to the packet delay and loss ratio. Moreover, the two channels, LiFi and RF, can be aggregated to double the link capacity in the best case. Finally, we demonstrate howHy-Ficould be used as wireless access technology in next-generation indoor enterprise networks providing both high capacity and seamless mobility.

The recent move of LoRa towards the unlicensed 2.4GHz ISM spectrum band creates multiple opportunities for its broader usage in new IoT applications. However, the ISM band is already highly populated with diverse wireless systems, and interference issues are expected. We believe that cross-technology communication (CTC) can help to speed up LoRa adaptation and mitigate the coexistence problems. Therefore, in this paper, we introduce Wi-Lo, a novel signal emulation-based CTC approach, which allows a WiFi device to generate a valid LoRa waveform. Our scheme requires no hardware modifications but depends only on the careful selection of WiFi frame payload bits. We build our Wi-Lo prototype with commodity hardware. Our evaluation reveals that Wi-Lo enables reliable communication from WiFi to LoRa devices, which is comparable to the configurations using native LoRa nodes. Moreover, by leveraging the high link budget of LoRa, a long distance CTC can be established.

Listen-Before-Talk (LBT) is an essential function of many MAC protocols and a key mechanism of sharing spectrum in an uncoordinated manner. However, this simple but effective concept remains a major challenge for Software-Defined Radios (SDRs). If performing the protocol stack on a host PC, due to their structure, SDRs have inherent latencies built in. These latencies increase their reaction time, the so-called turnaround time, significantly, compared to their conventional radio counterparts. Unfortunately, this means that they cannot comply with the LBT channel access procedures used in modern protocols like IEEE 802.11 or LTE Licensed-Assisted Access (LTE-LAA). Given the flexibility and rapid-prototyping capabilities of SDRs, these protocols could clearly benefit from such SDR-based implementations. In this paper, we fill this gap and present a design approach for SDRs supporting LBT. We particularly focus on the rather complicated timing issues. As a proof-of-concept, we showcase our LBT-enabled implementation of the srsLTE software stack for LTE.

We present Hy-Fi, a system which combines light fidelity (LiFi) and radio based on the WiFi physical layer waveform by using the MIMO features available in IEEE 802.11-compliant commodity chip sets. Hy-Fi is based on two key ideas. First, we use inexpensive COTS hardware to facilitate direct transmission of WiFi waveforms over the optical wireless channel, as this is proposed in the IEEE P802.11bb task group. Second, we use the MIMO signal processing to aggregate LiFi and radio signals at the physical layer. The system was implemented as a prototype and evaluated in a small testbed. Experimental results show that our approach offers robustness against signal blockage (Shadowing) and external interference in both, the optical and RF channels. Moreover, the two media (LiFi and WiFi) can be aggregated to double the capacity in the best case.

Visible light communications (VLC) has been introduced as a complementary wireless technology that can be widely used in industrial indoor environments where automated guided vehicles aim to ease and accelerate logistics. Despite its advantages, there is one significant drawback of using an indoor vehicular VLC (V-VLC) network that is there is a high handover outage duration. In line-of-sight VLC links, such handovers are frequently due to mobility, shadowing, and obstacles. In this paper, we propose a frequency diversity and link aggregation solution, which is a novel technique in Data link layer to tackle handover challenge in indoor V-VLC networks. We have developed a small-scale prototype and experimentally evaluated its performance for a variety of scenarios and compared the results with other handover techniques. We also assessed the configuration options in more detail, in particular focusing on different network traffic types and various address resolution protocol intervals. The measurement results demonstrate the advantages of our approach for low-outage duration handovers in V-VLC. The proposed idea is able to decrease the handover outage duration in a two-dimensional network to about 0.2 s, which is considerably lower compared to previous solutions.

As the radio spectrum has become the bottleneck resource with increasing volume of mobile data and ultra-dense network deployments, it is crucial to use spectrum more flexibly in time, space, and frequency dimensions. However, higher efficiency in spectrum usage facilitated by flexible spectrum allocation comes with a cost, namely the increased complexity of spectrum monitoring and management. Identifying the transmitters is at the interest of particularly spectrum enforcement authorities to ensure that spectrum is used as intended by the legitimate users of the spectrum. For a scalable, efficient, and highly-accurate operation, we propose a crowd-sensing based solution where sensing devices report their measured receive power levels to a central entity which later fuses the collected information for localizing an unknown number of transmitters. Our solution, referred to as DeepTxFinder, leverages deep learning to handle many sources of uncertainty in the operation environment: namely number of transmitters, their transmission power levels, and channel conditions (shadowing). Using deep-learning, DeepTxFinder distinguishes itself from the prior state-of-the art which requires knowledge of the number and transmission power of transmitters or require the transmitters to be well separated in space by tens to hundreds of meters making them ill-suited for application in expected ultra-dense deployment of small-cells. Moreover, we propose a tiling-based approach to increase the scalability of our proposal by reducing the computational complexity. Our simulation studies show that DeepTxFinder can provide a high detection accuracy even only by collecting data from a very small number of sensors. More specifically, with 1 %-2 % sensor density DeepTxFinder can estimate the number of transmitters and their locations with high probability which proves that sparse sensing is feasible.

Commercial off-the-shelf (COTS) IEEE 802.11ac WiFi systems only use a low number of antennas. This limits the multi-user MIMO (MU-MIMO) performance and, hence, the throughput of such systems, especially in dense environments. We present a unique solution based on hybrid digital-analog (HDA) beamforming to overcome the limitation of COTS WiFi hardware and effectively increase the MU-MIMO gain and to unlock the potential of MU-MIMO in WiFi. We use COTS WiFi hardware in combination with a self-developed beamforming module and novel algorithms for the HDA approach, leveraging the MU-MIMO precoding of the WiFi system. The implemented control and signal processing software is fully transparent to the WiFi part, i.e., no protocol changes are needed. We demonstrate the increase in MU-MIMO gain using unmodified COTS end-user terminals.

Despite exhibiting very high theoretical data rates, in practice, the performance of LTE-U/LAA and WiFi networks is severely limited under cross-technology coexistence scenarios in the unlicensed 5GHz band. As a remedy, recent research shows the need for collaboration and coordination among colocated networks. However, enabling such collaboration requires an information exchange that is hard to realize due to completely incompatible network protocol stacks. We propose OfdmFi, the first cross-technology communication scheme that enables direct bidirectional over-the-air communication between LTE-U/LAA and WiFi with minimal overhead to their legacy transmissions. Requiring neither hardware nor firmware changes in commodity technologies, OfdmFi leverages the standard-compliant possibility of generating message-bearing power patterns, similar to punched cards from the early days of computers, in the time-frequency resource grid of an OFDM transmitter which can be cross-observed and decoded by a heterogeneous OFDM receiver. As a proof-of-concept, we have designed and implemented a prototype using commodity devices and SDR platforms. Our comprehensive evaluation reveals that OfdmFi achieves robust bidirectional CTC between both systems with a data rate of up to 84kbps, which is more than 125× faster than state-of-the-art.

Despite the planned operation of enterprise wireless local area networks (WLANs), they still experience unsatisfactory performance due to several inefficiencies. One of the major issues is the so-called sticky user problem, in which users remain connected to an access point (AP) until the signal quality becomes too weak. In this paper, we leverage software-defined networking (SDN) to propose a user association solution for WLANs aiming to mitigate such inefficiencies, thus improving resource utilization. As it is a computationally hard problem, we also design various low-complexity user-AP association schemes that consider not only signal quality but also AP loads and minimum quality requirements for user traffic. Moreover, to provide simultaneous content distribution in a sustainable mode, we propose exploiting link-layer multicasting to decide on user-AP associations. Our analysis via simulations and experimentation on an open-source testbed shows that considering user-AP association jointly with multicast delivery leads to a significant performance increase over the default client-driven approach: the median throughput is 11× higher when all users request the same content and the achieved improvement decreases to 68% for 100 contents. Moreover, due to more efficient use of the airtime, unicast users achieve higher throughput if multicast delivery is exploited.

Recently, we have seen a boom of attempts to improve the operation of networking protocols using machine learning techniques. The proposed reinforcement learning (RL) based control solutions very often overtake traditionally designed ones in terms of performance and efficiency. However, in order to reach such a superb level, an RL control agent requires a lot of interactions with an environment to learn the best policies. Similarly, the recent advancements in image recognition area were enabled by the rise of large labeled datasets (e.g. ImageNet). This paper presents the ns3-gym - the first framework for RL research in networking. It is based on OpenAI Gym, a toolkit for RL research and ns-3 network simulator. Specifically, it allows representing an ns-3 simulation as an environment in Gym framework and exposing state and control knobs of entities from the simulation for the agent's learning purposes. Our framework is generic and can be used in various networking problems. Here, we present an illustrative example from the cognitive radio area, where a wireless node learns the channel access pattern of a periodic interferer in order to avoid collisions with it. The toolkit is provided to the community as open-source under a GPL license.

LTE in Unlicensed (LTE-U) constitutes a new source of interference in the 5 GHz ISM band with a potentially strong impact on WiFi performance. Cross-technology interference and radio resource management are the best ways to assure efficient coexistence but require proper signaling channels. We present LtFi, a system which enables to set-up a cross-technology communication between nodes of co-located LTE-U and WiFi networks. LtFi follows a two-step approach: using an innovative side channel on their air-interface LTE-U BSs are broadcasting connection and identification information to adjacent WiFi nodes, which is used in a subsequent step to create a bi-directional control channel over the wired backhaul. The simple LtFi is fully compliant with LTE-U and works with COTS WiFi hardware. The achievable data rate on the air-interface based broadcast side channel (up to 665 bps) is sufficient for this and multiple other purposes. Experimental evaluation of a fully operational prototype has demonstrated reliable data transmission even in crowded wireless environments for LTE-U receive power levels down to -92 dBm. Moreover, system-level simulations demonstrate accurate recognition of the complete set of interfering LTE-U BSs in a typical LTE-U multi-cell environment.

Smart antennas can unlock the potential of unlicensed spectrum by letting the coexisting networks transmit concurrently without harmful interference. This is possible by strategically allocating the antenna degrees-of-freedom for both beamforming toward the intended receiver and interference nulling toward the victim receiver(s). Our solution, named Xzero, achieves this goal for the particular case of LTE-unlicensed (LTE-U) and WiFi by overcoming the challenges of cross-technology interference nulling by a null search at the LTE-U BS with assistance from the WiFi network. Our demo shows a running prototype of Xzero implemented using USRP SDR platform running srsLTE and commodity WiFi hardware. We illustrate the change in the airtime of colocated WiFi and LTE-U networks upon activation of Xzero and fast reconfiguration of the null beam upon a change in WiFi node's location.

To avoid the foreseeable spectrum crunch, LTE operators have started to explore the option to directly use the underutilized unlicensed spectrum in 5 GHz UNII bands being mainly used by IEEE 802.11 (WiFi). However, as LTE is not designed with shared spectrum access in mind, it has a potential to seriously harm Wi-Fi, Currently suggested solutions focus on forcing LTE-U to introduce coexistence gaps in either frequency, time, or space domain, and are addressing the coexistence only indirectly due to the lack of coordination among the coexisting WiFi and LTE-U networks. Contrary to these schemes, our proposal introduces explicit cooperation between neighboring LTE-U and WiFi networks. We suggest that LTE-U BSs equipped with multiple antennas can create coexistence gaps in space domain in addition to the time domain gaps by means of cross-technology interference nulling towards WiFi nodes in the interference range. In return, LTE-U can increase its own airtime utilization while trading off slightly its antenna diversity. We demonstrate that such cooperation offers benefits to both WiFi and LTE-U in terms of improved throughput and decreased channel access delay. More specifically, system-level simulations reveal a throughput gain up to 221% for LTE-U network and up to 44% for WiFi network depending on the setting, e.g., distance between the two cells, number of LTE antennas, and WiFi nodes in the LTE-U BS neighborhood. Our approach provides significant benefits especially for moderate separation distances between LTE-U/WiFi cells where interference from a neighboring network might be severe due to the hidden network problem.

Classical control and management plane for computer networks is addressing individual parameters of protocol layers within an individual wireless network device. We argue that this is not sufficient in phase of increasing deployment of highly re-configurable systems, as well as heterogeneous wireless devices co-existing in the same radio spectrum. They demand harmonized, frequently even coordinated adaptation of multiple parameters in different protocol layers (cross-layer) in multiple network devices (cross-node). We propose UniFlex, a framework enabling unified and flexible radio and network control. It provides an API enabling coordinated cross-layer control and management operation over multiple wireless network nodes. The controller logic may be implemented either in a centralized or distributed manner. This allows to place time-sensitive control functions close to the controlled device (i.e., local control application), off-load more resource hungry control application to compute servers and make them work together to control entire network. The UniFlex framework was prototypically implemented and provided to the research community as open-source. We evaluated the framework in a number of use-cases, what proved its usability.

Dense Small Cell networks are considered the most effective way to cope with the exponential increase in mobile traffic demand expected for the upcoming years and are one of the foundations of the future 5G. However, novel architectures are required to enable cost-efficient deployments of very dense outdoor Small Cell networks, complementing the coverage layer provided by macro-cells. In this regard, two important challenges need to be solved to make this vision a reality: i) increased traffic dynamics, which are translated into more frequent handovers, and ii) cost-efficient deployment of large number of Small Cells. In this paper we propose and evaluate SENSEFUL, an novel architecture addressing the two problems highlighted above: Software-Defined Networking (SDN) as the key technology to promote adaptability to a varying environment and provide efficient mobility solutions in the dense access layer, and novel wireless backhauling technologies where traditional wired connectivity does not meet cost/efficiency restrictions.

Ubiquitous Wi-Fi access points (APs) constantly send out beacon and probe response frames to inform potential stations (STAs) about their existence. Beacon and probe responses can be extended by adding additional information (so-called beacon stuffing) making it possible to deliver this information to mobile devices (e.g. smart-phones equipped with 802.11 interfaces) without the need for association with the local infrastructure. This feature can be used to support location-based information services (LBS) related for e.g. advertising local opportunities, temporary obstacles and traffic disturbances or emergency notifications. In this paper we introduce a demonstrator for Location-based Wi-Fi Services (LoWS) based on beacon stuffing. The LoWS system can be easily installed within an existent 802.11 infrastructure while the receiver application can be installed on nearly all up-to-date commercial off-the-shelf smartphones, e.g. based on Android or iOS.

Efficient resource allocation in enterprise wireless local area networks(WLAN) has become more paramount with the shift of traffic toward WLANs and increasing share of the video traffic. Unfortunately, current practise of client-driven association to APs has several shortcomings, e.g., sticky client problem. As a remedy, we propose to move the AP association decision to a periodically-running central controller which aims to maximize the proportionally-fair network throughput. After formulating the optimal mapping problem, we devise several heuristics requiring various degrees of knowledge, e.g., pairwise user-AP link rates, throughput demand of each user. Our analysis via simulations on realistic scenarios (conference, office, and shopping mall) shows the superior performance of our proposals in terms of aggregate logarithmic throughput. While the utility gain over the conventional client-driven approach is modest, up to 18%, the resulting increase in the weakest user's throughput is significant (71-120%) as well as that of AP load balance and fairness of user throughputs. Moreover, our evaluations reveal a very small optimality gap (between 0.1-5%). The highest gain is observed in the conference setting where the users are unevenly distributed in the network and hence there is a huge load imbalance among the APs. While schemes requiring more knowledge, i.e., on handover-cost and traffic demands, perform the best, a naive approach which runs periodically and assigns each user to the AP providing the highest signal level to that user maintains up to 41% gain in the weakest user's throughput over the client-driven handover approach.

Individual localization services seldom satisfy the requirements for accurate, robust, punctual, and seamless location information. To enhance the provisioning, a set of challenges has to be addressed, pertaining to handover, fusion, and integration of individual localization services. In the following, we advertise the Standardized Localization Service (SLSR), a middleware architecture for achieving those goals. We instantiate the SLSR in a office-like indoor environment and evaluate its performance in a specifically designed testbed for evaluation of indoor localization approaches. Our results typify the contributions of different functional components envisioned in the SLSR on its overall performance.

Device-to-Device (D2D) communication has a high potential in reducing the amount of network traffic and improving the latency and energy efficiency of communication. Currently, D2D link establishment decisions are based on active probing between devices that wish to establish a D2D link. The main drawback of such approaches is a large overhead as during active probing no data communication can take place. We leverage physical locations of the devices that wish to establish a D2D link in order to estimate the probability of success in establishing the link before making an attempt to communicate. The probability of success is given as a closed form equation that takes into account the imperfections of location information of the devices and intrinsic randomness of wireless environments. We experimentally evaluate the proposed location-based decision- making mechanism for D2D link establishment in a complex office-like indoor environment. We show that setting the Signal-to-Noise Ratio (SNR) threshold of the proposed mechanism to a value that is 5 dB higher than the nominal SNR required for communication results in reliable link establishment with false positive rate of less than 2%. Furthermore, we show a relatively small loss of link establishment potential due to an increase in the inaccuracies of location information.

The increased popularity of IEEE 802.11 LANs (WLANs) in residential (home) environments leads to dense, unplanned and thus chaotic deployments. Access Points (APs) are frequently deployed unfavorably in terms of radio coverage and use interfering frequency/power settings. In fact, in some parts of a one's apartment the usage of the neighbor's AP might be preferred as it might provide better signal quality than the own AP. Moreover, the network performance could be dramatically improved by balancing the network load over spatially co-located neighbor APs operating on different radio channels or having asymmetric workloads. We address these problems by presenting NxWLAN (Neighborhood extensible WLAN) which enables the secure virtual extension of user's home WLANs in residential environments through enabling the usage of neighboring APs operating on the same or on different radio channels. NxWLAN requires no additional software to be installed on the client STAs while providing the same level of security as the home AP, e.g. WPA2, without revealing any kind of security credentials to untrusted neighboring APs. In this paper we prove, by prototyping and simulating the NxWLAN solution, that it is operational, performant and easy to deploy using off-the-shelf hardware. Moreover, we provide the full source code of our prototype to the community as open-source.

Nowadays, the usage of multiple virtual wireless networks on top of one physical AP is very common in home Wi-Fi networks. Be it Hotspots of Internet Service Providers (ISPs) or Mobile Network Operators (MNOs) used for offloading their traffic, community networks or plain so-called "Guest-Networks". Whereas the home user (AP owner) in the best case should not even be aware that his network connection is shared. Most of the ISPs and MNOs are now trying to convince their customers to install an additional virtual wireless hotspot network on their home AP and offer them in return the free usage of all other available hotspots. But, currently most costumers are skeptical as the providers cannot guarantee a downlink slice of air-time or real separation in time on the wireless access network. In this paper we demonstrate by using a novel downlink slicing scheme applied on commercial off-the-shelf hardware, how slicing on MAC level can be applied to truly guarantee a fixed amount of air-time for the home user and provide complete traffic separation in time between the home and the hotspot network. Moreover, our demonstrator shows the benefits of the approach by comparing the quality of a high definition video stream with and without our MAC slicing approach.

The efficient management of radio resources in today's home or residential Wi-Fi networks is still an open research question. Due to the chaotic and unplanned deployment of Wi-Fi Access Points (APs) and the fact that all APs are managed individually by their owners, home Wi-Fi networks suffer from performance degradation due to contention and interference. In this paper we present and showcase a distributed radio channel assignment scheme, implemented using the ResFi platform, in which neighboring home Wi-Fi APs cooperate with each other in order to negotiate radio channel selection by taking into account the instantaneous network load of neighboring APs (two hop neighborhood). While the information about the network load can be directly exchanged between ResFi-enabled APs using the ResFi out-of-band Internet control channel, the information about the network load of co-located non-ResFi APs first needs to be estimated passively by monitoring the data traffic on the radio channel. The presented demonstrator of the approach is implemented using commodity hardware and enables the audience to observe the distributed channel adaptation in real-time.

We demonstrate BIGAP, a novel architecture providing both high network performance as well as seamless handover in Enterprise IEEE 802.11 networks. The former is achieved by assigning different channels to co-located APs to fully utilize the available radio spectrum. The latter is achieved by providing a mechanism for below MAC-layer handover through exploiting the Dynamic Frequency Selection (DFS) capability in IEEE 802.11. In essence BigAP forces clients to change AP whilst they `believe' they are simply changing channel. BIGAP is fully compatible with 802.11 and requires no modifications to the wireless clients.

Enterprise IEEE 802.11 networks need to provide high network performance to operate a large number of diverse clients like laptops, smartphones and tablets as well as capacity hungry and delay sensitive novel applications like mobile HD video & cloud storage efficiently. Moreover, such devices and applications require much better mobility support and higher QoS/QoE. Existing solutions can either provide high network performance or seamless mobility but not both. We present BIGAP, a novel architecture achieving both of the above goals. The former is achieved by assigning different channels to co-located APs in order to fully utilize the available radio spectrum. The latter is achieved by providing a mechanism for below MAC-layer handover through exploiting the Dynamic Frequency Selection capability in 802.11. In essence BIGAP forces clients to change AP whilst they `believe' they are simply changing channel. BIGAP is fully compatible with 802.11 and requires no modifications to the wireless clients. Testbed results demonstrate a significant improvement in terms of network outage duration (which is 32 x smaller as compared to state-of-the-art solutions) and negligible throughput degradation during handover operation. In this way frequent and seamless handover operations can take place thus supporting both seamless mobility and efficient load balancing.

In dense deployments of residential WiFi networks individual users suffer performance degradation due to both contention and interference. While Radio Resource Management (RRM) is known to mitigate this effects its application in residential WiFi networks being by nature unplanned and individually managed creates a big challenge. We propose ResFi - a framework supporting creation of RRM functionality in legacy deployments. The radio interfaces are used for efficient discovery of adjacent APs and as a side-channel to establish a secure communication among the individual Access Point Management Applications within a neighborhood over the wired Internet backbone. We have implemented a prototype of ResFi and studied its performance in our testbed. As a showcase we have implemented various RRM applications among others a distributed channel assignment algorithm using ResFi. ResFi is provided to the community as open source.

The new IEEE 802.11ac standard offers improvements like the use of large bandwidth and advanced Multiple-Input and Multiple-Output (MIMO) signal processing. However, the use of wide channels in a dense 802.11 network increases co-channel interference and contention. Fortunately, MIMO signal processing enables interference management where Access Points (AP) on the same channel can cancel their interference at each other's Stations (STA), while beamforming their signal to their own STA. We focus on the Downlink (DL) of 802.11ac-based infrastructure networks with unequal network load at the APs and show that a combination of the two physical layer MIMO techniques, namely DL multi- user MIMO and interference nulling, can significantly improve the DL performance. The proposed algorithm performs in two steps. First, it identifies in each cell the spatial compatible STA groups which can be served by MU-MIMO in the DL. Second, the unused degree of freedom of lightly loaded APs is utilized to perform interference management by means of null steering towards STAs in highly loaded adjacent cells. The algorithm takes the overhead due to 802.11ac compliant channel sounding into account when computing the set of STAs to be nulled. Our simulation results from an indoor hotspot environment conclude that the proposed scheme outperforms state-of-the-art protocols especially in high density AP deployments. Moreover, the proposed method is fully compatible with commodity 802.11ac network cards that implement MU-MIMO and requires only slight modifications to the 802.11 MAC layer.

Cognitive Radio (CR) will enhance the efficiency in spectrum usage by re-using temporally unused licensed spectrum. A promising transmission scheme to utilize even very fragmented and fast changing spectrum is Non-Contiguous OFDM (NCOFDM). Unfortunately, NC-OFDM requires tight synchronization between sender and receiver, i.e. the receiver needs perfect up-to-date knowledge about the set of subcarriers being used by the NC-OFDM sender. Therefore, a reliable and always available Control Channel (CC) is crucial for signaling the spectrum allocation information. Although, underlay Impulse-Radio Ultra-WideBand (IR-UWB) meets the theoretical requirements for such a CC in CR networks, i.e. communication range of up to 1km on a sufficient high data rate, there is a lack of practical studies of IR-UWB in real world environments, especially with respect to co-existence with NC-OFDM as in the envisioned CR multi-technology station. In this paper we present results of measurements in our state-of-the-art IR-UWB testbed. We show that IR-UWB can reach the required communication range of a few hundreds of meters in unobstructed as well as slightly obstructed Lineof- Sight propagation only. Although, IR-UWB is a wideband technology we show that it is severely affected by narrowband interference from close proximity sources which is typically the case in the envisioned multi-technology stations. Such a mutual disturbance can be mitigated by increasing the spatial separation between both air interfaces, orthogonalization in time or using only those parts of the radio spectrum for NC-OFDM which are outside the main IR-UWB transmission mask.

It is hard to experiment with test-beds for communication networks: data produced in the network has to be retrieved and analyzed, networks must be reconfigured before and between experiments, data is often little structured (log-files) and analysis methods and tools are generic. Even though many problems of experimentation are the same for all experiments, re-use is sparse and even simple experiments require large efforts. We present a framework that attempts to solve these problems: we define a set of requirements for experimenting with network test-beds, we describe the principles and inner workings of our framework, demonstrate it with a typical example experiment, and present measurement results that illustrate the feasibility and scalability of our approach. Some qualitative and quantitative aspects of ClickWatch are compared to the commonly used logfile based approach to experimentation.

In this paper we analyze the adverse effects of Adjacent Channel Interference (ACI) on 802.11 with a focus on new 802.11n standard. ACI is causing problems that are related to the carrier sensing mechanism in 802.11. On the one hand, the carrier sensing is sometimes too restrictive thus preventing concurrent transmissions which leads to a variant of the exposed terminal problem. On the other hand, the carrier sensing is sometimes too optimistic thus causing packet collisions which is a form of the hidden node problem. Both problems are especially severe in multi-radio systems, where the radios are very closely spaced. Such problems already investigated in 802.11a/b/g still remain with 802.11n. Our results show that the number of available orthogonal channels in IEEE 802.11n depends on the spatial spacing between the radios, channel width (20MHz vs. 40 MHz), RF band (2.4 vs. 5GHz) and traffic pattern. In a multi-radio system the situation is worst, e.g. in the 2.4 GHz we were not able to find 2 orthogonal channels. The adverse effect of ACI can be reduced in two ways. First, by increasing the spatial separation between the radios; a spacing of less than 1 meter already improves the situation significantly, e.g. 40 cm are sufficient to get 2-3 orthogonal 20 MHz channels in the 2.4 GHz band with reduced transmission power. Furthermore, a distance of 90 cm is also sufficient so that a 40 and a 20MHz channel can be used simultaneously without any interference. However, in a multi-radio system the spatial spacing between the radios cannot be increased due to space limitations. The only option to overcome ACI related problems is to reduce the transmit power making power control essential. Finally, our analysis revealed that 802.11 is an inappropriate protocol for multi-channel MAC/routing protocols based on multi-radio systems where an explicit MAC layer link-scheduling is more promising.

In this paper, we present a configurable and inexpensive MIMO mesh network research platform based on IEEE 802.11n and open source software. There are two requirements for such a research testbed. First configurability: the researcher needs the ability to modify hard- and software on each ISO/OSI layer. Secondly, the testbed has to comprise hundreds of nodes to make sound conclusions on network performance. Therefore, a single mesh node has to be cheap. Both requirements have contradicting implications and a tradeoff is necessary. We propose a solution based on off-the-shelf 802.11n hardware with Atheros WiFi chips, the open source WiFi driver ath9k, and the Click Router API. This represents a good tradeoff. Procurement costs for a single network node are below 100$. The solution yet allows a variety of adjustments based on the open-source driver and a highly configurable WiFi hardware. We evaluated our platform and present measurement results.

During the last few years, there has been a tremendous increase in the deployment of IEEE 802.11 wireless networks. However, a possible drawback is the proliferation of dense WiFi deployments leading to mutual interference among WiFi nodes were users will experience low QoS levels. The contribution of this work is to present an architecture for dense WiFi networks that combines Rf channel assignment, distributed antennas and network load distribution to enhance QoS. Simulation results showed that channel assignment and distributed antenna are complementary. The former one increases the capacity of the network by adding additional spectrum and decreasing co-channel interference. The latter one helps to decrease the packet error rate especially of weak links, which also results in higher network capacity. In addition, by balancing the network load among several Access Points the capacity of the uplink of the wired broadband connection (e.g. DSL) can be more efficiently utilized. Simulations show that the proposed method increases the average throughput by up to 14% in infrastructure networks where the wireless capacity is the limiting factor. In scenarios where the backbone is the limiting factor, the average throughput can be increased by up to 56% by using distributed antennas and network load distribution.

Opportunistic routing (OR) relies on links of intermediate quality, i.e. packet losses are common. However, the reasons for packet losses are manifold, e.g. a received packet may contain corrupted bits. According to traditional approaches, the receiver discards the whole frame in such a case. In this paper, we present measurements from an indoor IEEE 802.11 wireless mesh network (WMN), which indicate that corrupted frames still contain a significant amount of correct data, which can be utilized. In particular, corrupted frames are common for intermediate quality links. Bit errors tend to occur in proximity, i.e. they are bursty. Furthermore, bit errors are uncorrelated across different receivers in most cases. Based on our observations, we propose a HARQ scheme for OR calledHybridARQwithLimitedFragmentation(HALF). It operates on a hop-by-hop manner and requires only local knowledge. Due to the bursty nature of bit errors, we are dividing frames into fragments with additional error detection. Using random linear network codes, the sender transmits incremental redundancy until one of its receivers is able to decode all fragments and therefore sends an acknowledgement packet. However, the partial information at all other receivers is not lost. Instead, to increase the throughput further, it is also used in subsequent forwarding rounds along the multi-hop route. We implemented a prototype of our protocol to evaluate its performance. With the help of detailed simulations, we analyzed the reasons why HALF significantly outperforms traditional approaches like DSR.

In recent years wireless mesh networks (WMN) gained lots of attention in research and industry. Especially, the use of WMNs for community networks proved their ability to provide a high performance Internet access while demanding little deployment planning. However, one problem became apparent. Network congestion, especially around gateway nodes (GN), can dramatically decrease the overall performance of the entire network. We present a novel approach to efficiently decrease network congestion while maintaining fairness. To achieve that goal, we combine two modern techniques, namely uplink superposition-coding and multi-user diversity to create a MAC protocol that can dramatically improve the network throughput around GN, hence extending the capabilities of the entire WMN. We will present two distributed protocols, which are suitable for ad-hoc networks without any centralized infrastructure. Extensive analysis and simulations will be presented, that show a network throughput increase of up to 88% over the use of 802.11 with the OAR rate selection algorithm. In a fading environment an additional gain of 5-13% from multi-user diversity was observed.

WiMAX is one of the most promising technologies to provide broadband wireless access in the near future. In this paper we identify a key element for the performance of a WiMAX network, the DL-MAP packing algorithm, which mainly determines the usage efficiency of the available radio resources and investigate potential differences that could appear between WiMAX equipment vendors in the maximum capacity of the system due to the packing approach used. Our results show that the performance of simple DL-MAP packing algorithms might be significantly outperformed by more complex ones resulting in a clear differentiation factor among manufacturers.

We propose a new high performance forwarding scheme, denoted transmit diversity based cooperative opportunistic routing (TDiCOR), that efficiently exploits multiuser and transmit diversity to improve the overall throughput in wireless multi-hop networks. Neighboring nodes cooperate through simultaneous transmissions in a MISO fashion in order to overcome the destructive effects of fading. TDiCOR uses distributed transmit diversity to increase the robustness of acknowledgements as well as data transmissions while preserving the opportunistic nature by using multiple candidates for packet relaying. We present measurements from a wireless testbed which suggest that cooperation using transmit diversity is feasible even with today's off-the-shelf hardware. At the instance of TDiCOR, we demonstrate how to realize the idea of cooperative opportunistic routing as an operational protocol and present very promising simulation results. TDiCOR outperforms traditional routing (i.e. DSR) in typical outdoor scenarios in terms of throughput by 30% and by 50% in indoor scenarios with high shadow fading, without consuming additional bandwidth or additional hardware resources.

In this paper, we propose a new multi-hop forwarding scheme for wireless multi-hop networks, denoted Multi-Channel Extremely Opportunistic Routing (MCExOR), that efficiently exploits radio channel characteristics and makes use of multiple RF channels. MCExOR reduces the overall number of transmissions in wireless multi-hop networks by opportunistically skipping nodes in a packet's forwarding path. The use of multiple non overlapping RF channels contributes to the reduction of overall interference. In contrast to other approaches MCExOR only needs one RF transceiver per device. We present algorithms for route discovery and packet forwarding. In contrast to other multi-channel protocols the packet forwarding is decoupled from route discovery. Therefore, both protocols have low complexity and can be addressed separately. With the help of simulations we show that MCExOR significantly outperforms traditional protocols like AODV through the simultaneous use of multiple RF channels and its opportunistic behaviour. With the increasing number of RF channels the overall throughput increases superproportionally. MCExOR with 2 RF channels surpasses AODV by an average of 140%. Unlike other multi channel approaches even a single packet flow can benefit from the existence of multiple channels.

Several routing protocols for IEEE 802.11 mesh networks that operate at multiple RF channels have been described before [1][2][3]. However, only few facts about link-level characteristics in multi-channel environments have been published. This paper presents observations, made in an indoor testbed, about the impact of channel-assignment on the quality of links.We argue that the assumption 'all radio channels are equal' does not hold in almost all indoor scenarios. Hence, great care must be taken when assigning radio channels to individual links, in order not to spoil network performance. We found that for any given environment the quality and symmetry of a wireless link (quantified by delivery probability) varies significantly depending on the radio channel used. The delivery probability between the best and the worst case could easily exceed a factor of two.However, existing IEEE 802.11 multi-channel protocols assume that all channels are equal, which does not reflect real-world conditions. To remedy to this shortcoming we present the Multi Channel Extremely Opportunistic Routing MCExOR protocol [3].