# Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network +
## 2 Related Work  
Many AQM schemes have been designed based on heuristic, control theoretic and optimization approaches [10]. Examples of heuristic schemes are RED [6], gentle RED (GreD) [11], adaptive RED(ARED) [12], parabola RED [13], hyperbola RED (HRED) [14], dynamic RED [15], flow RED [16], stabilized RED [17], balanced RED [18], BLUE [19], Yellow [20] etc. These algorithms aimed to improve some or all of the features, such as fairness, stability, network utilization, packet loss and adaptability for different traffic loads. In GRED, the packet dropping probability from the maximum packet dropping probability (\(P_{max}\)) to 1 is replaced by a gentle slope. The author suggested that, by doing so, the stability would increase. However, the main problem of GRED is parameter tuning. Different parameters should be tuned according to different network conditions. ARED adaptively adjusts the maximum packet dropping probability \(P_{max}\) using the multiplicative increase-multiplicative decrease approach. However, ARED is also sensitive to different parameter configuration and its performance is not superior to RED when the network environment is complex [10]. BLUE uses link idle events and packet loss to model the congestion control scheme in a simple way and its packet loss is low. However, the long delay causes congestion in certain situations [10]. In the control theoretic approach, the main focus is to model TCP with RED dynamics. This approach then provides a stable and faster responding system [15]. In this approach, the main thrust is to interpret the structural problems to analytically determine various RED parameters [21]. Proportional Integral (PI) controller [22], Proportional Derivative (PD) controller [23], PI-Derivative (PID) controller [24] and fuzzy logic controller [25] are examples of control theoretic schemes. The low-pass filter design of RED leads to low frequency oscillations in the regulated output from a control standpoint. To overcome this limitation, a PI controller is proposed that uses instantaneous samples instead of changing the average queue size of the samples. A PID controller is proposed to provide a faster response time than a PI controller. On the other hand, a PD controller is proposed to adapt the RED parameter \(P_{max}\) to further improve the queue length stabilization compared to ARED. In a fuzzy logic controller, the scheme can provide better control of the non-linear time varying system where traffic loads, round trip times, etc., vary over time. This scheme dynamically adapts its parameters to cope with the situation. An optimization scheme such as [26] deals with the congestion control problem by maximizing the aggregate source utility via an approximate gradient algorithm [27]. For example, an adaptive virtual queue [28] attempts to maintain the queue length by utilizing the arrival rate of the traffic. On the other hand, a stabilized virtual buffer [29] utilizes both the arrival rate of the traffic and the queue length as the target value to achieve a stabilized output. Traditional AQM research discussed previously focused on a wired network. Recently, there has been much AQM research focused on wireless networks. In an infrastructure type wireless network, the base station acts as the router between the wired network part and the wireless access network part and becomes congested because of the speed mismatch between these two parts [30]. A RED-based discard strategy [3], PDCP discard timer-based strategy in the LTE network [2], efficient and fair AQM [31], slope-based discard [32] and wireless delay-based queue [33] are examples of this category. On the other hand, in an infrastructureless type of wireless network, mobile nodes themselves act as the router, which can become congested. Examples of such schemes in this scenario are predictive AQM [34], adhoc hazard RED [35] etc. Although many studies, as described previously, have contributed many insights into the AQM-based congestion control, they have their own technical drawbacks.