In unlicensed (shared) bands, wireless technologies typically operate without central coordination, which can lead to unwanted transmission interruptions, collisions, and resource wastage. We focus on Wi-Fi and NR-U coexistence in shared bands and solve the aforementioned problem with a deterministic backoff approach. The proposed scheme allows active transmitters to learn the number of nearby interferers in a distributed manner and, as a result, implement an ordered round-robin transmission schedule to minimize collisions. We show analytically that the scheme converges not only in equilibrium (when collisions are negligible), but also in a general case (when collisions are frequent at the beginning or in the middle of the proposed scheme's operation). Furthermore, extensive simulations prove that the proposed scheme guarantees fairness between contenting cells and technologies (both in terms of throughput and delay) as well as optimizes channel efficiency. We also study the impact of imperfect readings of the number of contending nodes (which may be the result of, e.g., asymmetric channel conditions) on the performance of the proposed scheme. In most cases, the deterministic backoff approach outperforms legacy channel access schemes. Additionally, our proposal does not add additional signaling overhead and is backward compatible with standard Wi-Fi and NR-U operation.

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.

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.

Data rate selection algorithms for Wi-Fi devices are an important area of research because they directly impact performance. Most of the proposals are based on measuring the transmission success probability for a given data rate. In dense scenarios, however, this probing approach will fail because frame collisions are misinterpreted as erroneous data rate selection. We propose FTMRate which uses the fine timing measurement (FTM) feature, recently introduced in IEEE 802.11. FTM allows stations to measure their distance from the AP. We argue that knowledge of the distance from the receiver can be useful in determining which data rate to use. We apply statistical learning (a form of machine learning) to estimate the distance based on measurements, estimate channel quality from the distance, and select data rates based on channel quality. We evaluate three distinct estimation approaches: exponential smoothing, Kalman filter, and particle filter. We present a performance evaluation of the three variants of FTMRate and show, in several dense and mobile (though line-of-sight only) scenarios, that it can outperform two benchmarks and provide close to optimal results in IEEE 802.11ax networks.

The legacy approach to solve coexistence problems between multiple wireless networks operating in the same frequency bands is through network planning. However, this approach is often unfeasible in unlicensed (shared) bands, where different network owners and technologies work without any coordination. In this paper, we adapt an existing channel access scheme for fair resource sharing between Wi-Fi and NR-U (the unlicensed version of 5G), in a completely distributed manner. The idea is to find an ordered schedule of transmissions granted to the active transmitters (regardless of their technology) and repeat this schedule in a round-robin fashion until the set of active transmitters changes. The mechanism works as a special extension of a random access scheme with deterministic backoff counters. Simulation-based results prove that the scheme guarantees airtime fairness between network cells and technologies while optimizing channel efficiency and minimizing channel access delays. Unlike other coexistence solutions, the scheme does not require the exchange of information between the coexisting cells; moreover, it is backward compatible with legacy access schemes.

Wireless networks operating in unlicensed bands generally use one of two channel access paradigms: random access (e.g., Wi-Fi) or scheduled access (e.g., LTE License Assisted Access, LTE LAA and New Radio-Unlicensed, NR-U). The coexistence between these two paradigms is based on listen before talk (LBT), which was, however, designed for random access. Meanwhile, scheduled systems require that their transmissions start at the beginning of a slot boundary. Synchronizing this boundary to the end of LBT usually requires transmitting a reservation signal (RS) to block the channel. Since the RS is a waste of channel resources, we investigate an alternative self-deferral approach (gap-based access) using analytical and simulation models. We put forth a proposal to employ only self-deferral, treat the gap mechanism as a partial backoff, and adjust the contention window (CW) settings to the number of coexisting nodes. We demonstrate that this approach not only ensures fairness in Wi-Fi/NR-U coexistence but also avoids wasting radio channel resources and improves aggregate network throughput. Furthermore, we show that the proposed approach outperforms RS-based access and provides significant throughput and fairness gains. Finally, we implement a long short-term memory-based (LSTM) regression model to predict those Wi-Fi/NR-U CW settings which lead to coexistence fairness.

Multihop wireless networks with autonomous nodes are susceptible to selfish traffic remapping attacks (TRAs). Nodes launching TRAs leverage the underlying channel access function to receive an unduly high Quality of Service (QoS) for packet flows traversing source-to-destination routes. TRAs are easy to execute, impossible to prevent, difficult to detect, and harmful to the QoS of honest nodes. Recognizing the need for providing QoS security, we use a novel network-oriented QoS metric to propose a self-enforcing game-theoretic mitigation approach. By switching between TRA and honest behavior, selfish nodes engage in a noncooperative multistage game in pursuit of high QoS. We analyze feasible node strategies and design a distributed signaling mechanism called DISTRESS, under which, given certain conditions, the game produces a desirable outcome: after an upper bounded play time, honesty tends to become a selfish node’s best reply behavior, while yielding acceptable QoS to most or all nodes. We verify these findings by Monte Carlo and ns-3 simulations of static and mobile nodes.

Wireless technologies coexisting in unlicensed bands should receive a fair share of the available channel resources, even when they use different access methods. We consider the problem of coexistence between Wi-Fi and New Radio Unlicensed (NR-U) nodes, which employ, respectively, a random and scheduled access scheme. The latter typically resorts to reservation signals (RSs), which allow keeping the control of the channel until the start of the next synchronized slot. This mechanism, although effective for increasing the channel access opportunities of scheduled-based nodes, is also a waste of channel resources. We investigate alternative solutions, based on self-deferral only. We built analytical and simulations models for a Wi-Fi and NR-U coexistence scenario and found that (a) airtime fairness can be achieved with proper contention window (CW) settings and (b) this solution can be exploited for optimizing network performance for both Wi-Fi and NR-U. Additionally, we demonstrate how an artificial recurrent neural network-based regression model can be applied to predict such proper CW settings. Our research confirms that by embedding contending devices with machine learning intelligence in CW selection, scheduled-based systems such as NR-U do not have to resort to RSs.

The proper setting of contention window (CW) values has a significant impact on the efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is not scalable enough to maintain stable throughput for an increasing number of stations, yet it remains the default method of channel access for 802.11ax single-user transmissions. Therefore, we propose a new method of CW control, which leverages deep reinforcement learning (DRL) principles to learn the correct settings under different network conditions. Our method, called centralized contention window optimization with DRL (CCOD), supports two trainable control algorithms: deep Q-network (DQN) and deep deterministic policy gradient (DDPG). We demonstrate through simulations that it offers efficiency close to optimal (even in dynamic topologies) while keeping computational cost low.

Coexistence between cellular systems and Wi-Fi gained the attention of the research community when LTE License Assisted Access (LAA) entered the unlicensed band. The recent introduction of NR-U as part of 5G introduces new coexistence opportunities because it implements scalable numerology (flexible subcarrier spacing and OFDM symbol lengths), and non-slot based scheduling (mini-slots), which considerably impact channel access. This paper analyzes the impact of NR-U settings on its coexistence with Wi-Fi networks and compares it with LAA operation using simulations and experiments. First, we propose a downlink channel access simulation model, which addresses the problem of the dependency and non-uniformity of transmission attempts of different nodes, as a result of the synchronization mechanism introduced by NR-U. Second, we validate the accuracy of the proposed model using FPGA-based LAA, NR-U, and Wi-Fi prototypes with over-the-air transmissions. Additionally, we show that replacing LAA with NR-U would not only allow to overcome the problem of bandwidth wastage caused by reservation signals but also, in some cases, to preserve fairness in channel access for both scheduled and random-access systems. Finally, we conclude that fair coexistence of the aforementioned systems in unlicensed bands is not guaranteed in general, and novel mechanisms are necessary for improving the sharing of resources between scheduled and contention-based technologies.

In wireless networks, stations can improve their received quality of service (QoS) by handling packets of source flows with higher priority. Additionally, in cooperative relay networks, the relays can handle transit flows with lower priority. We use game theory to model a two-hop relay network where each of the two involved stations can commit such selfish QoS manipulation. We design and evaluate a reputation-based incentive scheme called RISC2WIN, whereby a trusted third party (e.g., an access point) can limit selfish behavior and preserve appropriate QoS for both stations.

In multi-hop ad hoc networks, selfish nodes may unduly acquire high quality of service (QoS) by assigning higher priority to source packets and lower priority to transit packets. Such traffic remapping attacks (TRAs) are cheap to launch, impossible to prevent, hard to detect, and harmful to non-selfish nodes. While studied mostly in single-hop wireless network settings, TRAs have resisted analysis in multi-hop settings. In his paper we offer a game-theoretic approach: we derive a formal model of opportunistic TRAs, define a TRA game with a heuristic rank-based payoff function, and propose a boundedly rational multistage attack strategy that both selfish and non-selfish nodes are free to use. Thus non- selfish nodes are allowed to respond in kind to selfish ones. We characterize the form of equilibrium that the multistage play reaches and verify via simulation that it often coincides with a Nash equilibrium in which harmful TRAs are curbed in the first place, whereas harmless ones need not be.

The 802.11 standard is prone to selfish attacks performed by insiders, i.e., correctly authenticated stations. The recently released 802.11aa amendment is likewise prone to such attacks because the mechanisms which it provides can be selfishly configured by insiders to raise their QoS. In this paper, we present the first security analysis of 802.11aa by investigating selfish insider attacks against the intra-AC prioritization feature. Our analysis shows that 802.11aa is susceptible to already existing attacks as well as attacks previously not considered. Furthermore, our research shows the extent to which an attacker can benefit from these types of selfish behaviors. Therefore, we can identify which mechanisms should be augmented with countermeasures to protect 802.11aa networks from selfish attackers.

Quality of Service (QoS) is usually provided in ad hoc networks using a class-based approach which, without dedicated security measures in place, paves the way to various abuses by selfish stations. Such actions include traffic remapping attacks (TRAs), which consist in claiming a higher traffic priority, i.e., false designation of the intrinsic traffic class so that it can be mapped onto a higher-priority class. In practice, TRAs can be executed in IEEE 802.11 ad hoc networks using the Enhanced Distributed Channel Access (EDCA) function. This attack is easy to perform yet hard to prevent. We propose a distributed discouragement scheme based on the threat of TRA detection and punishment. The scheme does not rely on station identities or a trusted third party, nor does it require tampering with the MAC protocol. We analyze an arising non-cooperative TRA game and find that under certain realistic assumptions it only incentivizes TRAs if they are harmless to other stations; otherwise the selfish stations are induced to learn that TRAs are counterproductive.

ABSTRACT Selfish users are known to be a severe security threat for wireless ad hoc networks. In particular, they can exploit mechanisms designed to assure quality of service (QoS) in the network. In this paper, the problem of backoff misbehavior in IEEE 802.11 enhanced distributed channel access (EDCA) networks is studied using a game‐theoretic approach. First, it is shown how this selfish behavior can disrupt traffic differentiation. Then, a solution is proposed, which encourages standard‐compliant behavior and thus proper QoS provisioning. This solution is based on punishing selfish nodes by degrading their throughput proportionally to the degree of misbehavior. A practical application of the solution is proposed, which is verified through simulations. Results show that the suggested mechanism considerably improves QoS provisioning in IEEE 802.11 EDCA ad hoc networks in the presence of selfish nodes. Furthermore, it is shown that the mechanism is adaptive, does not have a negative impact on the throughput of well‐behaving nodes, and provides legacy node support. Copyright © 2012 John Wiley & Sons, Ltd.

This paper presents a work in progress which deals with the important and unresolved problem of node misbehavior. A realistic approach is used to determine the impact of contention window manipulation on IEEE 802.11e ad-hoc networks. It is explained why such networks are more prone to misbehavior. Novel results pertaining to the 802.11e standard are presented. Simulation analysis is done for several scenarios with a distinction made for uplink and downlink traffic. It is shown that a misbehaving node can jeopardize network performance, therefore, countermeasures to this problem need to be developed.