Wi-Fi

Using ranging for collision-immune IEEE 802.11 rate selection with statistical learning

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 …

Reinforced-lib: Rapid prototyping of reinforcement learning solutions

Reinforcement learning (RL) is emerging as a promising framework for training intelligent agents to solve complex problems. However, developing RL solutions involves a complex process that requires experimenting with different models, agents, and …

FTMRate: Collision-Immune Distance-based Data Rate Selection for IEEE 802.11 Networks

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, …

Improving IEEE 802.11ax UORA Performance: Comparison of Reinforcement Learning and Heuristic Approaches

Machine learning (ML) has gained attention from the network research community because it can help solve difficult problems and potentially lead to groundbreaking achievements. In the Wi-Fi domain, ML is applied to solve challenges such as efficient …

DB-LBT: Deterministic Backoff with Listen Before Talk for Wi-Fi/NR-U Coexistence in Shared Bands

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 …

Using self-deferral to achieve fairness between Wi-Fi and NR-U in downlink and uplink scenarios

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 …

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as the existence of affordable and highly interoperable …

No Reservations Required: Achieving Fairness between Wi-Fi and NR-U with Self-Deferral Only

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) …

Downlink channel access performance of NR-U: Impact of numerology and mini-slots on coexistence with Wi-Fi in the 5 GHz band

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 …

Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement Learning

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 …