# Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network +
## 4 Active Queue Management and Proposed Approach  
RED [6], proposed by [PERSON] and [PERSON], is the prominent solution for global synchronization and this opens a vast research area in AQM. The RED algorithm functions by using four parameters according to which the queue size is managed. These are queue length, minimum threshold (\(Min_{th}\)), maximum threshold (\(Max_{th}\)), and maximum probability (\(P_{max}\)). Queue length is the maximum buffer size of the queue. Briefly, the algorithm functions by maintaining an average queue size. The packet dropping probability (\(P_{d}\)) linearly changes between zero and \(P_{max}\) with the change of average queue size between the \(Min_{th}\) and \(Max_{th}\). If the average queue size exceeds the \(Max_{th}\), all arriving packets are dropped. RED can control the transient congestion (due to bursty traffic) by absorbing the arrival rate fluctuations because the packet dropping mechanism is based on the moving average of the past values. Although RED is a prominent technique to properly maintain the queue size--compared to simple Droptail [38] that drops a packet when the queue reaches its limit--RED is particularly sensitive to the parameters of the scheme itself and the dynamic traffic load [39]. The goal of a proper AQM scheme is to maintain the average queue size between \(Min_{th}\) and \(Max_{th}\) of the queue at low oscillations and in turn help avoid forced drops. It is inappropriate for the average queue size and the original RED packet dropping probability to be linearly related [10]. It has been found in [40] that with a small average delay in the low-load scenario, the link bandwidth is not fully utilized; thus, in order to improve the link utilization, a smaller packet dropping probability should be used. The link bandwidth is fully utilized with a large average delay in the high-load scenario; thus, a larger packet dropping probability should be used in order to reduce the average delay. Considering the above requirements, we modified the packet dropping probability of RED according to the traffic-loads which we call Smart RED (SmRED), in which we divided the packet dropping probability function into two regions according to the setting of the \(Min_{th}\) and \(Max_{th}\) to distinguish between low and high traffic-load conditions. By doing this, we want to achieve a trade-off between the throughput and the delay. RED's original packet dropping probability can be defined as  
\[P_{d}=P_{max}\times\frac{avg-Min_{th}}{Max_{th}-Min_{th}} \tag{1}\]  
where \(avg\) is the average queue length, \(Min_{th}\) is the minimum threshold that the average queue length must exceed before any packet marking or dropping is done, and \(Max_{th}\) is the maximum threshold that the average queue length must exceed before all packets are marked and dropped. We first decided one target value of the eNB buffer size below which we treat the traffic volume as low and above which we treat the traffic volume is high. We define the target value as the middle of the minimum threshold and the maximum threshold and it can be defined mathematically as:  
\[Target=Min_{th}+\frac{Max_{th}-Min_{th}}{2} \tag{2}\]  
If the \(avg\) is below the target value, we set the packet dropping probability as  
\[P_{d}=P_{max}\times\left(\frac{avg-Min_{th}}{Max_{th}-Min_{th}}\right)^{i} \tag{3}\]  
On the other hand, when the traffic volume starts to become high, i.e., if the \(avg\) is between _Target_ and \(Max_{th}\), then the packet dropping probability function is defined as  
\[P_{d}=P_{max}\times\left(\frac{avg-Min_{th}}{Max_{th}-Min_{th}}\right)^{1/i} \tag{4}\]where, \(i=2\), \(3\), \(4\), \(5\), \(\dots\) Increasing the value of \(i\) will lead to lower dropping probability during low traffic-load condition and high dropping probability during high traffic-load condition. We can tune the parameter \(i\) to obtain different dropping functions as shown in Figure 4, where \(Min_{th}=20\), \(Max_{th}=80\), and \(P_{max}=0.5\).