## 2 Related Work  
Recent advances in dense WLANs push the researchers to solve the channel collision problem and performance anomaly in order to provide good users' experience. For the collision problem, the most literature attempts to find an optimal CW. In [14], [PERSON] uses a two-dimensional Markov chain model to analyze the performance of DCF in a saturated network (each node always has data to send). He demonstrates that the selection of CW values is a key factor affecting the system throughput and derives an optimal CW value that should be related to the nodes' number in the network. Therefore, the solutions that were discussed in many literatures are to dynamically adjust the CW value. In [4], the authors obtain an optimal CW value by estimating the number of nodes according to a collision probability mechanism. In [5], the authors propose an adaptive practical channel observation-based scaled backoff (COSB) to scale-up and scale-down the CW according to the channel observation-based conditional collision probability, which achieves higher throughout and shorter delay. Subsequently, they further propose a cognitive backoff (CB) mechanism that adaptively determines the CW to provide efficient collision avoidance with high throughput and low delay under both dense and sparse conditions in [6]. In [7], the authors obtain the number of nodes by calculating the number of idle slots. In [8], the authors achieve the maximum system throughput by adaptively adjusting the CW according to the network condition. In [9], the authors develop a mathematical model for obtaining the relationship between the CW value and the transmission delay between any two nodes, which can maximize the system throughput by an iterative algorithm. Although the above algorithms can be applied to a dynamic network environment, they need to be simultaneously executed on nodes and AP and result in extra operating cost. Therefore, [15] and [16] present two algorithms that do not require any hardware modifications and only run on nodes. In [15], all of the nodes decrease the back-off counter with a certain probability that can be calculated by some available network parameters during the idle time. In [16], the AP can calculate and assign a back-off value for nodes by running a VBA algorithm in the back-off stage after nodes successfully transmit. Alternatively, some literature attempts to tackle the transmission collision by a contention/reservation algorithm. In [17], the authors propose a Token-DCF protocol, in which each node schedules its neighbor that has the longest queue with a certain probability by its MAC header. The Token-DCF can result in some nodes' starvation when one of its neighbors always has the longest queue. In [18] and [19], the authors propose an Implicit Ordering-MAC (IO-MAC) mechanism for degrading the collision. In [18], the successful transmission node is considered as the predecessor of its following node. The successive node will take part in contenting with a random back-off value in a range from CWmin to 2 CWmin-1 when its predecessor fails to transmit or has no more data to transmit. However, the successive node can use a smaller back-off value than the back-off value of its predecessor when several nodes simultaneously follow the IO-rule, which will result in the invalidation of this algorithm. Hence, in [19], the time slot is divided into a reservation period and a contention period. The nodes select its successful transmission order in the current cycle as its back-off value while they enter successfully into the reservation period. However, it also can be inefficient due to lots of idle contention time slots being wasted, while all nodes get into a contention-free status. In [20], a mechanism carrier sense multiple access/contention queuing (CSMA/CQ) based on software defined network (SDN) is proposed to solve the collision problem, but it needs multiple channels due to its separation of operation in the contention and transmission channel.