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.

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.

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.

Ensuring QoS guarantees for real-time multimedia traffic in WLANs has recently attracted a lot of attention from standardization bodies and researchers. In order to increase the granularity of traffic prioritization and QoS support provided by EDCA, the intra-access category prioritization mechanism has been proposed in the recent IEEE 802.11aa amendment. In order to improve real time transmission over WLANs, two standard transmission selection algorithms can be used to prioritize traffic within the voice and video access categories of EDCA: the strict priority algorithm and the credit-based shaper algorithm (CBSA). This paper focuses on the improvement of video transmission over WLANs using two video transmission queues (primary and alternate) and the two traffic selection algorithms and compares the behavior of IEEE 802.11aa with legacy EDCA. Additionally, it comments on the possible ways of CBSA implementations. Finally, it proposes an adjustment of CBSA to wireless environment (WCBSA) which gives the most promising results.

Audio-video streaming in Wi-Fi (IEEE 802.11) wireless local area networks (WLANs) has recently attracted a lot of attention. In order to increase the granularity of traffic prioritization and quality of service (QoS) support defined by EDCA, intra-access category prioritization has recently been introduced in the IEEE 802.11aa amendment. In this paper, we present novel results which show how the new mechanism impacts the performance of an access point (AP). Additionally, we propose a wireless credit-based shaper algorithm (WCBSA), a version of CBSA adjusted to the wireless environment. We also analyze how the different settings of WCBSA impact the prioritization of traffic streams. Finally, we compare the operation of 802.11aa intra-access category prioritization with the legacy inter-access category prioritization defined by EDCA.

Analytical models of the Distributed Coordination Function (DCF) have numerous applications in the performance analysis of IEEE 802.11 networks. The model proposed in this paper distinguishes itself from existing approaches in that it allows throughput, delay, and frame loss ratio analysis under both saturation and non-saturation network conditions. It combines the Markov chain and the queuing theory to comprehensively and efficiently analyze the DCF channel access procedure assuming finite (M/M/1/K) queues. Additionally, it incorporates advantages of models previously proposed in the literature: correct backoff countdown, proper handling of frames and distinction of the wireless channel blocking probability from the frame blocking probability during the backoff countdown. Furthermore, it is kept reasonably simple to attract network designers. Simulation results validate the correctness of the new model for a variable number of nodes and under different network loads. Additionally, we demonstrate that the new model outperforms three previous models known from the literature.

This article presents three versions of a novel MAC protocol for IEEE 802.11 ad-hoc networks called Busy Signal-based Mechanism turned On (BusySiMOn) (This is an extended version of our conference paper: [15]). The key idea of the proposed solution is based on an intelligent two-step reservation procedure combined with the advantages of EDCA service differentiation. The former alleviates the hidden node problem while the latter ensures compatibility with the IEEE 802.11 standard. Simulation results obtained for saturated and non-saturated network conditions emphasize the advantages of the new protocol over the currently used four-way handshake mechanism in terms of fairness, throughput, and average frame delay.

This article presents a new MAC layer protocol for IEEE 802.11 EDCA-based ad-hoc networks with hidden nodes. The key idea of the proposed solution is based on an intelligent two-step reservation procedure which is combined with the advantages of EDCA service differentiation. The new protocol achieves significant performance improvement for high priority traffic (e.g., Voice) in terms of fairness, throughput and average frame delay. It is also compatible with the IEEE 802.11 standard. The obtained results emphasize the advantages of the new protocol over the currently used four-way handshake mechanism.

The paper presents a preliminary study of a revised analysis of IEEE 802.11e performance. One of many possible topologies is analyzed in order to emphasize the severe problem of the incapability to prioritize traffic in networks with hidden nodes. The article also provides some innovative conclusions.