# Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network +
## 1 Introduction  
To meet the key requirements for the next generation wireless network, cellular operators around the world provide seamless Quality of Service (QoS) for converged mobile and multimedia services. This is possible due to the deployment of 4G networks based on the Long-Term Evolution (LTE) standard. The growing demand for network services, such as video streaming, video telephony, Voice Over Internet Protocol (VOIP) with constraints on delay and bandwidth requirements, poses new challenges for researchers in the design of the future generation cellular network. LTE and System Architecture Evolution (SAE) were formulated by the 3 rd Generation Partnership Project (3 GPP) in 2004 [1]. LTE introduces a new air interface and radio access network, which provides much higher throughput and low latency, greatly improved system capacity and coverage compared to those of the Wideband Code Division Multiple Access (WCDMA) systems. These improvements lead to increased expectations on the end-user Quality of Experience (QoE) over LTE as compared to existing 2G/3G systems [2]. For this reason, both research and industrial communities are making a considerable effort in the study of LTE systems, proposing new and innovative solutions in order to analyze and improve their performance. To date, there has been much research on LTE, not only on the physical layer but also on higher layers, e.g., the Packet Data Convergence Protocol (PDCP) and Radio Link Control (RLC) layer. The main data buffers of the user interface are located at the RLC layer where data is held before its transmission to the destination User Equipment (UE). However, if there is congestion or overflow in buffers of RLC entities, low latency will not be guaranteed [3]. As a high speed broadband network, LTE promises uninterrupted connectivity to the packet data network. Congestion may take place at the radio interface due to various reasons. One such reason for congestion is communication quality in the channel that varies with the distance of the UE from the base station (Evolved Node B, eNB) that leads eNB to select a low bit rate modulation scheme in the case of poor communication channel quality and may segment the packet. Therefore, if the channel condition is not good enough to transmit RLC Service Data Unit (SDUs) in time, they will be in buffers of RLC entities waiting for the instruction for segmentation or concatenation from the Medium Access Control (MAC) layer. Also, when a user is moving from one cell to another cell, handover occurs. During the handover, the source eNB forwards the incoming data and the data that is already in the RLC buffer for the user to the target eNB to alleviate the service disruption. However, the forwarding of the user data may cause increased delay of forwarded data. Moreover, there is a time interval immediately after the handover when the data on both the direct path and the forwarded path may arrive in parallel to the target eNB [4]. Hence, there is a high probability of congestion or overflow in RLC buffers due to the large volume of traffic in a short period of time, leading to a high delay that results in poor end-to-end application performance [5]. To guarantee high throughput and low delay when congestion occurs, numerous studies on Active Queue Management (AQM) based congestion control schemes in the network have been proposed in the past, especially for transport-layer protocols. Among the proposed AQM schemes, the Random Early Detection (RED) [6] algorithm is one of the most famous algorithms. The work presented in this paper is an improvement and extension of our preliminary version [7] with the following new contributions:  
* In this paper, we have extended our idea Smart RED (SmRED) [7] to SmRED-i, where packet dropping probability function is different in accordance with the value of i = \(2,3,4\dots\) 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.