Search

In Long Term Evolution-Advanced (LTE-A) networks, different aspects of radio resource scheduling such as fairness and Quality of Service (QoS) assurance must be provided for heterogeneous traffic, having different characteristics. However, the ever-growing number of mobile devices sharing the limited radio resources leads to the high cost and difficulty for information acquisition and computations in resource scheduling process. In the present paper, we propose a proportional knapsack scheduling approach for fairness- and QoS-aware downlink transmission of all different service classes in LTE-A networks. Moreover, we assess the computational cost created by the uncertainty and lack of information on user operations, and propose a Gaussian-based approach for ranking the bearers in presence of limited information and computational capabilities. This approach is particularly suitable for emerging next generation wireless networks to support a wide range of applications with huge number of users. The results indicate that significant advantages are achieved both in terms of QoS and fairness and that it is a scalable solution for overload states of the network.

The growing variety and consumption of the mobile services throughout the cellular networks lead to various challenging issues in radio resource scheduling. To have an apparent perspective over the resource scheduling in real implementation of the next generation cellular networks, it is essential to consider sequences of alternating overload and normal states of the traffic, occurring much in the system. In this paper, we do a performance study of three overload-state schedulers by implementing such a network environment and exploiting the advantages and drawbacks of the compared algorithms. This performance study through the simulation results reveals that the existing overload-state resource scheduling schemes do not satisfy the fifth generation (5G) mobile network's requirements to be more optimized in hard real time fashion. Then, open challenges and potential research directions for resource management in future 5G mobile networks are presented at the end.

The growing variety and consumption of the mobile services throughout the Long Term Evolution (LTE) mobile networks lead to various challenging issues in radio resource scheduling. One of the main challenges in LTE resource management is how to allocate the scare available bandwidth, especially in overload states of the network, to the services which have different characteristics and quality requirements. In this paper, we study the optimal solution to LTE downlink resource allocation problem which has already been cast as the fractional knapsack optimization problem. The performance of this solution is investigated by a wide range of demands from different classes of services, over sequences of alternating overload and normal states of the network. The simulation results demonstrate that the proposed method supplies diverse quality requirements of different service classes in compromise with data rate enhancement. System performance has been evaluated in terms of loss, delay and throughput in comparison with reference algorithms.

In LTE multi-service communication system, a trade-off between QoS assurance and fairness is a challenging issue, since the QoS provisioning at the cost of starving users in low service demand classes is not favorable for the operator. In this paper, we adopt the time-domain Knapsack algorithm and fine tune it to provide fair resource allocation while support QoS requirements in LTE downlink scheduling system when the bearers are from different classes of service, having different QoS characteristics. We demonstrate that more efficient performance can be achieved in two aspects of fairness and QoS provisioning in terms of normalized throughput, and packet loss and delay rate, which are evaluated using simulation results.

In LTE systems, multicast services must be delivered efficiently in response to the need for strong QoS support. However, each class of quality services has its own requirements to be satisfied. These quality constraints limit the scheduling flexibility, and the LTE downlink resource allocating algorithms need to assimilate these constraints while trying to maximize system performance in terms of fairness and throughput. This paper addresses this fundamental problem of LTE downlink scheduling by adopting the time-domain Knapsack algorithm over the traffic overload patterns. We fine tune the Knapsack algorithm, to overcome this problem and improve system performance objectives. We demonstrate that more efficient performance can be achieved in terms of fairness index and system throughput, which is evaluated using simulation results.

The Long Term Evolution (LTE) as a mobile broadband technology supports a wide domain of communication services with different requirements. Therefore scheduling of all flows from various applications by the same strategy of resource allocation would not be efficient. Accordingly it is necessary to design new scheduling algorithms by considering the Quality of Service (QoS) requirements defined for each application. In this regard, this paper will provide a brief overview of previously reported schedulers for QoS support in LTE cellular networks by taking into account the various important QoS parameters such as delay, packet loss ratio and data rate. The resource distribution problem in LTE networks and solutions from the numerous numbers of previous resource allocation approaches are outlined and compared through a comparative table. The future direction for solving these problems will be stated. Overall this study summarises the current state of knowledge on the QoS-oriented scheduling algorithms for LTE networks.