# Future Is Unlicensed: Private 5G Unlicensed Network for Connecting Industries of Future
## 4 Challenges and Research Directions
### Challenge 4: Adaptive Back-off Mechanism for LBT  
The FCC mandates NR-U to use the Category 4 channel access mechanism, which is considered to be less efficient than the LTE-U-based carrier sense adaptive transmission (CSAT) mechanism [55] in terms of radio resource utilization. NR-U uses an LBT in medium access control (MAC) protocols, which is a contention-based channel access mechanism. Many times, resources are wasted while contending for channel access [56]. Additionally, a blind increase and decrease of the contention window in LBT bound the performance of NR-U equipment to a limited number of contenders, thus affecting the end-user quality of experience in the presence of the dense user.  
The LBT scheme is the typical and traditional carrier sense multiple access/collision avoidance (CSMA/CA)-like mechanism, as shown in Figure 5, which was introduced in IEEE 802.11 Distributed Coordination Function (DCF) [57] for fair coexistence in the shared channel. A randomly generated back-off value for the contention procedure was used in LBT. At the first transmission attempt, the user generates a uniform random back-off value N, from the contention window interval [0, CW], where CW is initially set to the minimum value CWmin. After each unsuccessful transmission, CW is doubled until it reaches the maximum value CWmax = 2\({}^{m}\) CWmin for \(m\) maximum number of back-off stages (M), that is \(m\in(0,\) M). Once there is a successful transmission of its data frame, CW is reset to the minimum value CWmin. For a network with a heavy load, resetting \(m\) to zero and the contention window (CW) to its CWmin value after successful transmission will result in more collisions and poor network performance due to an increase in probability to select a similar back-off value \(m\) by the devices [58]. Similarly, for fewer contending UEs, the blind exponential increase of CW for collision avoidance causes an unnecessarily long delay due to the broader range for selecting CW. Besides, this blind increase/decrease of the back-off window is more inefficient in the highly-dense networks proposed for NR-U for 5G smart factories (with one million devices per square meter), because the probability of contention collision increases with the the increasing number of users. Thus, the current LBT protocol does not allow NR-U devices to achieve high efficiency in highly dense environments. Hence, to withstand this challenge, NR-U needs a more efficient and self-scrutinized back-off mechanism to promise enhanced user quality of experience (QoE) in Industry 4.0 applications.  
The performance of an NR-U system can be severely degraded with an increase in the number of contenders, as the collision in the network is directly proportional to the density of the devices. Thus, we need a channel observation-based scaled back-off mechanism to scale-up and scale-down adaptively the size of the contention window to avoid the collisions [59; 60]. The channel state information such as the estimation of channel collision probability or a number of users currently active in the shared channel is the best information for optimal selection of the CW value to utilize the resources more tightly. For the channel state estimation, detecting the time slot busy/ideal is a research challenge. Usually, the user senses the energy and compares its certain threshold to decide if the slot is busy or idle. However, it is very hard to distinguish only based on received energy. Thus, casting this question into a binary classification problem (K-means), unsupervised learning algorithms help to identify the state of a shared channel correctly and optimize the CW value.