A Review of MAC Scheduling Algorithms in LTE System

Abstract

The recent wireless communication networks rely on the new technology named Long Term Evolution (LTE) to offer high data rate real-time (RT) traffic with better Quality of Service (QoS) for the increasing demand of customer requirement. LTE provide low latency for real-time services with high throughput, with the help of two-level packet retransmission. Hybrid Automatic Repeat Request (HARQ) retransmission at the Medium Access Control (MAC) layer of LTE networks achieves error-free data transmission. The performance of the LTE networks mainly depends on how effectively this HARQ adopted in the latest communication standard, Universal Mobile Telecommunication System (UMTS). The major challenge in LTE is to balance QoS and fairness among the users. Hence, it is very essential to design a down link scheduling scheme to get the expected service quality to the customers and to utilize the system resources efficiently. This paper provides a comprehensive literature review of LTE MAC layer and six types of QoS/Channel-aware downlink scheduling algorithms designed for this purpose. The contributions of this paper are to identify the gap of knowledge in the downlink scheduling procedure and to point out the future research direction. Based on the comparative study of algorithms taken for the review, this paper is concluded that the EXP Rule scheduler is most suited for LTE networks due to its characteristics of less Packet Loss Ratio (PLR), less Packet Delay (PD), high throughput, fairness and spectral efficiency.

RAN-aware Proxy-based Flow Control for High Throughput and Low Delay eMBB

“…This information allows RAPID to estimate the bandwidth allocated to each UE by the base station in units of PRBs. However, the number of allocated PRBs does not always reflect the achievable bandwidth since the MAC scheduler at the base station may also take into account the RLC buffer state [23] or the incoming data rate [31] when allocating resources to users. Therefore, in order to estimate the achievable bandwidth, we first calculate the number of unused PRBs, 𝑃𝑢 i (𝑡), by using the total number of PRBs in the cell 𝑁 PRB , the number of connected UE 𝑀 and the number of PRBs allocated to each UE 𝑃𝑎 i (𝑡):…”

QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning

“…This is shown in Fig 4 with a round robin heuristic, i.e. data flows are given a weight proportional to their timesince-last-scheduled [1], [3]. We compare the downlink network throughput and the VoIP user QoS satisfaction rate achieved using QADRA to those achieved using the round-robin and proportional fair heuristics [3].…”

“…data flows are given a weight proportional to their timesince-last-scheduled [1], [3]. We compare the downlink network throughput and the VoIP user QoS satisfaction rate achieved using QADRA to those achieved using the round-robin and proportional fair heuristics [3]. The proportional fair heuristic is defined as w j = u ū , where u and ū denote the expected throughput and mean throughput, respectively.…”

A New Channel-Aware Downlink Scheduling Algorithm for LTE-A and 5G HetNets

“…The amount of RBs and the order of served UEs over time are the result of the scheduling strategy or policy adopted. Therefore, the scheduling strategy is a decision-making process, carried out by the MAC layer in eNB, based on input parameters such as channel state, Quality of Service (QoS) requirements, among others [ 4 ], as shown in Fig. 1 .…”