# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 4 OFDMA in Wi-Fi 6
### Related Work
#### 4.3.4 Optimization  
Interactive flows are sensitive to latency. [PERSON] et al. [PERSON] et al. (2019) proposed, e.g., to minimize the upload time in uplink OFDMA. They greedily picked different stations according to their remaining upload time and assign them to different RUs. The authors of [PERSON] et al. (2019) applied a queue-based drift plus a penalty algorithm [PERSON] et al. (2019) to select the transmit power and resource allocation per timeslot to minimize the queue length. It divides the period between two consecutive TFs to multiple timeslots and allocates the RUs per timeslot, i.e., it is large enough for transmission. [PERSON] et al. [PERSON] et al. (2019) doubled the OFDMA contention window in the case of a station's failure. More precisely, this happens when the number of the unsuccessful transmissions exceeds the retransmission limit or when the number of active stations exceeds the number of available random access RUs [PERSON] et al. (2019) clustered stations with similar expected transmission delays to transmit their data simultaneously. This enhanced the uplink channel usage. The transmission scheduling was based on a proportional fair-based approach. Throughput optimization was the main objective of the following works. The authors in ([PERSON] et al., 2019) concentrated on optimizing the network's capacity when mixing scheduled and random access. Primarily, they defined the capacity entropy to measure the capacity of the network. Then, they modeled UORA mode with a Markov chain to derive the access probability and thus the capacity of the random access mode. Finally, they proposed a hybrid mode that relied on a greedy algorithm. The formulation relied on a set of constraints, defining the capacity and the channel quality perceived by each station. [PERSON] et al. [PERSON] et al. (2019) determined the contention window size utilizing deep reinforcement learning to optimize the throughput. It monitored the network under different circumstances and set the window size accordingly. A Markov decision process solved the optimization problem by taking into account the current window size, status of the nodes, and network throughput. The following papers aimed to improve the throughput with a constraint on queue length. A scheduling mechanism for downlink OFDMA with minimum throughput requirements was introduced in [81]. It was modeled with a Lyapunov optimization, considering a weighted Max-Min fairness. The authors derived a near-optimal scheduling policy to maximize the throughput. The OFDMA scheduler in [82] aimed to maximize the throughput and minimize the end-to-end latency in a multi-user scenario for both downlink and uplink. This optimization problem considered the queue length, MCS, and traffic priority of stations to assign optimal RUs to the clients. Likewise, [PERSON] et al. [83] considered the priority and fairness jointly while allocating RUs for stations. Queue length and data priorities served as inputs to a closed-loop feedback controller. Since Wi-Fi 6 exploits the unlicensed band, some approaches tried to maximize the throughput with co-existing networks. The authors of [84] maximized the total network throughput by proposing a probabilistic channel aggregation mechanism and applying a deep reinforcement learning technique to tune the probabilities. The objective was to maximize the sum of the throughput of the Wi-Fi 6 and legacy stations. If an RU is partially utilized, the station should sense it with a higher probability for aggregation. The authors of [85] investigated the coexistence of WLAN and LTE systems. They maximized the network throughput by grouping the nodes in Wi-Fi and LTE networks. They solved an optimization problem using a genetic algorithm to find the optimal grouping with the constraint on packet delay. The authors of [86] improved the throughput and reduced the delay by determining the contention window to avoid collisions.