Fairness‐aware user selection and resource allocation in MISO‐OFDMA

Papoutsis, Vasileios D.; Fraimis, Ioannis G.; Kotsopoulos, Stavros

doi:10.1002/ett.1447

Cited by 7 publications

(9 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In particular, the (inverse) square root will require 340 3 n 3 flops. The determinant jAj of an n n matrix is calculated by first performing an LU decomposition (ADLU), with a complexity of 8 3 n 3 flops [24]. The determinant is then the product of the n diagonal entries of U.…”

Section: Complexity Of Various Matrix Operationsmentioning

confidence: 99%

See 1 more Smart Citation

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

Elliott

Sigdel

Krzymien

2012

Trans. Emerging Tel. Tech.

View full text Add to dashboard Cite

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.

show abstract

Section: Complexity Of Various Matrix Operationsmentioning

confidence: 99%

“…The determinant is then the product of the n diagonal entries of U. Thus, jAj has a total complexity of 8 3 n 3 C 6n flops. A QR decomposition of an m n matrix, m>n, to find R2C m n and Q2C m m requires a total of 16m 2 n 8mn 2 C 8 3 n 3 flops [24].…”

Section: Complexity Of Various Matrix Operationsmentioning

confidence: 99%

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

Elliott

Sigdel

Krzymien

2012

Trans. Emerging Tel. Tech.

View full text Add to dashboard Cite

show abstract

“…Our objective is to maximize the average sum rate while keeping fairness between the users by optimizing both the cluster and beam allocation [3,22],…”

Section: System Modelmentioning

confidence: 99%

Feedback channel designs for fair scheduling in MISO–OFDMA systems

Özbek

Ruyet

2012

J Wireless Com Network

View full text Add to dashboard Cite

In the next generation of wireless communication, adaptive resource allocation techniques will play an important role to improve quality of service and spectral efficiency. In order to employ adaptive fairness scheduling for multiple-input single-output (MISO) orthogonal frequency-division multiple access (OFDMA), the channel state information (CSI) belonging to all users is required at the base station. However, the amount of feedback increases with the number of users, transmit antennas and subchannels. Therefore, it is important to perform a user selection at the receiver side without cooperation among the users and to quantize the CSI. In this article, the reduced feedback channel designs are examined for MISO-OFDMA systems while providing fairness between the users. In order to reduce the feedback rate, we choose the users considering their norm and orthogonality properties as well as their location in the cell. In order to limit the feedback rate, channel direction information is quantized by designing a specific codebook thanks to the proposed criterion. We obtain an expression to determine the amount of required feedback information for MISO-OFDMA systems to support more than one beam per subchannel for a given number of users, subchannels and transmit antennas. The performance results of the reduced feedback channel designs are evaluated for fair scheduling in wireless channels.

show abstract

“…For MIMO‐OFDMA, strong duality holds only when the constraints are monotonically increasing in each argument, which is true at high signal‐to‐interference‐and‐noise ratio (SINR) values ; otherwise, the problems remain combinatorial and nonconvex in nature. The authors in use different optimisation and/or heuristic tools to develop suboptimal DLBF and resource optimisation algorithms for single‐cell MIMO‐OFDMA systems. The proposed algorithms show tremendous performance gains and highlight the benefits of combined use of MIMO and OFDMA in wireless systems.…”

Section: Introductionmentioning

confidence: 99%

Downlink beamforming and resource allocation in multicell MISO‐OFDMA systems

Hassan

Assaad

2012

Trans. Emerging Tel. Tech.

View full text Add to dashboard Cite

International audienceWe study the downlink beamforming (DLBF) and resource optimisation problem in multicell multi-input single-output orthogonal frequency division multiple access (MISO-OFDMA) systems. This is a mixed combinatorial, nonlinear and nonconvex optimisation problem. By using uplink downlink duality, we propose an efficient suboptimal algorithm. This algorithm provides beamformers and allocates subcarriers and power to different users in the network with minimum message passing between the transmitting base stations. DLBF and resource optimisation algorithm for single-cell MISO-OFDMA systems can be obtained as a special case of the multicell algorithm. Simulation results reveal the performance gains provided by the proposed algorithm

show abstract

Fairness‐aware user selection and resource allocation in MISO‐OFDMA

Cited by 7 publications

References 26 publications

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

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

Feedback channel designs for fair scheduling in MISO–OFDMA systems

Downlink beamforming and resource allocation in multicell MISO‐OFDMA systems

Contact Info

Product

Resources

About