Resource Allocation Algorithms for Minimum Rates Scheduling in MIMO—OFDM Systems

In this paper, we consider the problem of downlink resource allocation in the IEEE 802.16 standard based on the orthogonal frequency‐division multiple access physical layer. The objective is to allocate the downlink frequency‐time slots and transmission power to different users such that the total throughput is maximized. The allocation is subject to constraints on the total transmission power and satisfying the quality of service requirements of different users expressed in terms of proportional rates. A three‐step heuristic scheme that incorporates a linear programming technique is proposed. The problem is modelled as an unbalanced transportation problem that is solved to obtain the users' slot assignment. The Vogel approximation method is used to efficiently solve the transportation problem with a slight degradation relative to the optimal solution. The Vogel approximation method gain is shown by replacing it in the three‐step heuristic by simple round‐robin or greedy slot assignment. In terms of total throughput and fairness, it is shown that the proposed scheme is a good compromise between the greedy‐oriented schemes, which aspire to maximize the total system throughput but sacrificing fairness, and a proportional rate constraints scheme introduced in the literature that achieved short‐term fairness but penalizes the total throughput. The study also shows that the proposed scheme consumes a fraction of the time needed by the proportional rate constraints scheme. Copyright © 2011 John Wiley & Sons, Ltd.

Section: Resultsmentioning

confidence: 99%

“…Further work on multiple-input multiple-output (MIMO)-OFDM resource allocation was reported by Klotz et al [24] where they consider a MIMO-OFDM downlink scenario, where each receiving mobile station has QoS requirements, namely minimum rate requirements. They proposed three schemes that can be used in a MIMO context only.…”

Section: Related Workmentioning

confidence: 99%

A dynamic radio resource management scheme for the IEEE 802.16 band‐adaptive modulation and coding mode with proportional rate constraints

Sokar

Elsayed

2011

“…Traditionally, resource management in communication networks is performed based on algorithms that focus on satisfying QoS requirements of applications [2][3][4][5][6]. The users are classified into different classes according to their needs to the network such as minimum data rate and maximum packet latency.…”

Section: Related Workmentioning

confidence: 99%

“…To optimise the use of transmission resources and the quality of the network, there is a shift from Quality of Service (QoS) resource management [2][3][4][5][6] to quality of experience (QoE)-based resource management [7][8][9][10][11]. QoS resource management maintains network-level parameters such as packet loss to provide good network quality.…”

Section: Introductionmentioning

confidence: 99%

Using buffered playtime for QoE‐oriented resource management of YouTube video streaming

Wamser

Hock

Seufert

et al. 2013

YouTube is the most important online platform for streaming video clips. The popularity and the continuously increasing number of users pose new challenges for Internet service providers. In particular, in access networks where the transmission resources are limited and the providers are interested in reducing their operational expenditure, it is worth to efficiently optimise the network for popular services such as YouTube. In this paper, we propose different resource management mechanisms to improve the quality of experience (QoE) of YouTube users. In particular, we investigate the benefit of cross-layer resource management actions at the client and in the access network for YouTube video streaming. The proposed algorithms are evaluated in a wireless mesh testbed. The results show how to improve the YouTube QoE for the users with the help of client-based or network-based control actions

“…Thus, various heuristic scheduling methods have been proposed to simplify the scheduling process [3,6,7], which perform close to the optimal ES for their respective precoding schemes. Further examples include [8] for ZFB, which also incorporates some fairness measures into the scheduling, [9] that considers orthogonal random beamforming, [10] with clustered eigenbeamforming and [11] that considers eigenbeamforming and zero-forcing DPC [12] and that attempts to provide users with minimum data rates.…”

Section: Introductionmentioning

confidence: 99%

Low complexity greedy, genetic and hybrid user scheduling algorithms for multiuser MIMO systems with successive zero‐forcing

Elliott

Sigdel

Krzymien

2012

In this paper, we consider low complexity user scheduling algorithms for multiuser multiple‐input multiple‐output systems employing successive zero‐forcing precoding. Optimal scheduling involves an exhaustive search (ES), which is prohibitively complex. Greedy algorithms (GrAs) with heuristic scheduling metrics achieve performance close to that of the ES. Meanwhile, genetic algorithms (GAs) are a rapid suboptimal option of optimising utility (e.g. scheduling) metrics. Herein, we evaluate the performance and complexity of greedy and genetic scheduling algorithms for successive zero‐forcing. We also propose and evaluate two hybrid algorithms combining the traits of the GrA and GA. The algorithms' performance is assessed through a series of computer simulations. We demonstrate both the GrA and GA achieve a near‐optimal sum rate with low complexity, whereas the hybrid algorithms further enhance the GrA and GA performance without an increase in the order of complexity.Copyright © 2012 John Wiley & Sons, Ltd.