Performance Study of Proportional Fair Scheduling Algorithm with Transmit Diversity Multi-Antenna Technique for Lte Network

Long Term Evolution (LTE) access network is based on Orthogonal Frequency Division Multiple Access (OFDMA) which provides multi user diversity gain to enhance the system throughput. However, fading of a radio channel causes inter channel interference and reduces overall system throughput. This deteriorating effect of wireless channel fading is higher for mobile users which can be reduced by channel aware scheduling algorithm and transmit diversity multi-antenna technique. Hence in this paper, an attempt has been made to evaluate the effect of mobility on the performance of Proportional Fair (PF) channel aware scheduling algorithm in conjunction with transmit diversity multi-antenna technique by considering throughput, delay and jitter as performance metrics.

Performance Evaluation of Scheduling Algorithms with Different MIMO Techniques in LTE Systems

MIMO techniques are used in Wireless Broadband Access (BWA) networks to maximize spectrum efficiency and minimize the bit error rate. LTE is one such BWA network which has adopted MIMO techniques in both the uplink and downlink along with Radio Resource Management (RRM) aspects like scheduling to improve the data rate. Scheduling is mainly concerned with allocating the available radio resources among the users depending upon the metrics such as Quality of Service (QoS) requirements of users, channel conditions etc. Hence in this paper, an attempt is made to study and compare the performance of scheduling algorithms (RR, PF, MT and BET) with MIMO techniques such as SISO, SIMO, SFBC and OLSM for Constant Bit Rate (CBR) traffic scenario. The performance metrics used are average throughput and average delay

MAC Scheduling Strategies in LTE Advanced

An Efficient scheduling algorithm at the data link layer is needed in multiuser systems to efficiently exploit the benefits of multiuser multiple input multiple output (MIMO). The 3G partnership programme (3GPP) does not specify any specific scheduling for Long Term Evolution (LTE) Advanced; we can have any one of the scheduling strategies applicable for LTE Advanced. There is substantial amount of literature on scheduling algorithms for multiuser wireless systems. In this paper, we are presenting various types of scheduling schemes of LTE Advanced, their advantages, and inefficiencies.Keywords – Scheduling, MIMO, LTE Advanced, Channel state information (CSI) , Adaptive modulation and coding (AMC)

QoS and energy efficient resource allocation in downlink OFDMA systems

In this paper we present and evaluate the performance of a resource allocation algorithm to enhance the Quality of Service (QoS) provision and energy efficiency of downlink Orthogonal Frequency Division Multiple Access (OFDMA) systems. The proposed algorithm performs resource allocation using information on the downlink packet delay, the average delay and data rate of past allocations, as well as the downlink users' buffer status in order to minimize packet segmentation. Based on simulation results, the proposed algorithm achieves significant performance improvement in terms of packet timeout rate, goodput, fairness, and average delay. Moreover, the effect of poor QoS provision on energy efficiency is demonstrated through the evaluation of the performance in terms of energy consumption per successfully received bit

Downlink Resource Scheduling in an LTE System

The problem of allocating resources to multiple users on the downlink of a Long Term Evolution (LTE) cellular communication system is discussed. An optimal (maximum throughput) multiuser scheduler is proposed and its performance is evaluated. Numerical results show that the system performance improves with increasing correlation among OFDMA subcarriers. It is found that a limited amount of feedback information can provide a relatively good performance. A sub-optimal scheduler with a lower computational complexity is also proposed, and shown to provide good performance. The sub-optimal scheme is especially attractive when the number of users is large, as the complexity of the optimal scheme may then be unacceptably high in many practical situations. The performance of a scheduler which addresses fairness among users is also presented

Reinforcement Learning for Joint Optimization of Multiple Rewards

Reinforcement Learning (RL) algorithms such as DQN owe their success to Markov Decision Processes, and the fact that maximizing the sum of rewards allows using backward induction and reduce to the Bellman optimality equation. However, many real-world problems require optimization of an objective that is non-linear in cumulative rewards for which dynamic programming cannot be applied directly. For example, in a resource allocation problem, one of the objectives is to maximize long-term fairness among the users. We notice that when the function of the sum of rewards is considered, the problem loses its Markov nature. This paper addresses and formalizes the problem of optimizing a non-linear function of the long term average of rewards. We propose model-based and model-free algorithms to learn the policy, where the model-based policy is shown to achieve a regret of \Tilde{O}\left(KDSA\sqrt{\frac{A}{T}}\right) for $K$ users. Further, using the fairness in cellular base-station scheduling, and queueing system scheduling as examples, the proposed algorithm is shown to significantly outperform the conventional RL approaches

Customized Packet Scheduling Algorithm for LTE Network

Advanced mobile networks are expected to provide omnipresent broadband access to a continuously growing number of mobile users. LTE system represents 4G mobile network. The key feature thereof is the adoption of advanced Radio Resource Management procedures in order to increase the system performance up to Shannon’s limit. Packet scheduling mechanisms, in particular, play a fundamental role, because they are responsible for choosing, with fine time and frequency resolutions, how to distribute scarce radio resources among different mobile stations, taking into account channel conditions and QoS requirements. This objective should be accomplished by providing an optimal trade-off between spectral efficiency and fairness. In this context, this paper proposes customized packet scheduling algorithm designed to adaptively alter scheduling schemes considering multiple input variables in order to maximize spectral efficiency as well as overall system performance

Survey On Scheduling And Radio Resources Allocation In Lte

ABSTRACT This paper will center mainly on the PS part of the RRM task, which performs the radio resource allocation in both uplink and downlink directions. Several approaches and algorithms have been proposed in the literature to address this need (allocate resources efficiently), the diversity and multitude of algorithms is related to the factors considered for the optimal management of radio resource, specifically, the traffic type and the QoS (Quality of Service) requested by the UE. In this article, an art's state of the radio resource allocation strategies and a detailed study of several scheduling algorithms proposed for LTE (uplink and downlink) are made. Therefore, we offer our evaluation and criticism