# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 4 OFDMA in Wi-Fi 6
### Related Work
#### 4.3.1 Random Access  
CSMA/CA means that collisions may arise that negatively impact the throughput and the energy efficiency. Thus, many propositions adapt random access to reduce the number of collisions. Some approaches added an additional exponential backoff procedure. For instance, hybrid UORA [37] used a multi-carrier CSMA analytical model based on Markov chains. A station first uses a primary backoff as usual to grant a TXOP for its data transmission. Figure 11: MU EDCA procedure. However, instead of transmitting its frame immediately, the station may engage a secondary backoff procedure according to a given probability depending on the Markov model. The secondary backoff counter reduces the number of collisions among the selected stations for the transmission. The entire procedure takes place within a TXOP. CRUI [38] also relied on an extra backoff stage during which each station sends a busy signal at a given offset depending on its priority. Only the station with the largest priority, i.e., with the earliest busy signal, wins the contention and is allowed to transmit for the corresponding TXOP. A winner can also grant the rest of the TXOP to a secondary station with a lower priority when it has finished transmitting its frames. Some other approaches tried to find the optimal backoff selection parameters or applied the backoff differently. [PERSON] et al. [39] concluded that the number of stations does not increase the contention probability if the OFDMA contention window is tuned optimally. Therefore, they proposed a distributed backoff counter controller. Every station adjusts its backoff counter regarding the earlier transmission's success or failure. If the earlier transmission is successful, a station will decrease its backoff counter to allow other stations to allocate more RUs and vice versa in case of transmission failure. [PERSON] et al. [40] minimized latency and improved throughput by retransmitting without the backoff procedure. The station had a second transmission opportunity for unsuccessful transmissions with a complementary probability. This method is effective when the number of stations is less than the number of random access RUs. [PERSON] et al. [41] proposed a multi-dimensional busy-tone approach to solve contention among stations that use the same RU. More precisely, each station selects a random backoff value and sends a signal during the arbitration phase's sub-slots that corresponds to one in its backoff value. Thus, the station knows that it lost the TXOP if a signal is received during a sub-slot corresponding to a zero in its backoff value. A turnaround time is required during each sub-slot so that a station can switch its radio chipset from transmission to reception. This significantly complicates the implementation and wastes radio bandwidth. In addition, these approaches break the compatibility with other Wi-Fi 6 devices. Artificial intelligence techniques have also been applied to optimize performance. The deep reinforcement learning technique, more specifically a convolutional neural network, was exploited to propose a distributed RU selection scheme to improve throughput and delay [42]. The scheme operates with CSMA/CA to enable a station to select the right RU considering a fixed number of previous channel allocations. Likewise, ref. [43] applied two other techniques, namely deep Q-network (DQN) and deep deterministic policy gradient (DDPG). The authors who leveraged those methods predicted the optimal contention window size regarding past behaviors to achieve high network throughput. [PERSON] et al. [44] proposed a hybrid MAC to assign RUs to stations in two steps. Firstly, RUs are fairly distributed among the stations. Then, multiple stations that select the same RU contend to access the RU using the legacy CSMA/CA method. However, such an approach is static and assumes that each station generates the same amount of traffic. In addition, it just reduces the collision probability but may not function efficiently in dense networks. [PERSON] et al.