Fair Resource Allocation for the Gaussian Broadcast Channel with ISI

Sung, Chi Wan; Shum, Kenneth W.; Ng, Cho Yiu

doi:10.1109/tcomm.2009.070311

Cited by 10 publications

(10 citation statements)

References 20 publications

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“…It is shown in [18] that (6) is NP-hard. Therefore, both (4) and (5) are NP-hard because a special case of the two problems is NP-hard.…”

Section: Problem Formulationmentioning

confidence: 99%

“…As a result, (18) is a concave optimization problem, and it can be solved by using standard convex optimization solvers. Let the matrix X Relaxed be the solution to (18), where its (u, b)-th entry, X Relaxed u,b , gives the fraction of the b-th PRB that is allocated to user u. To get an allocation pattern that satisfies the constraints of MAXPFUTILITY, we need to quantize X Relaxed .…”

Section: Convex Schedulermentioning

confidence: 99%

“…We use the fact that the solution to (18) (ν 1 , ν 2 , ..., ν N B ) is called an equilibrium price vector and the nonnegative real vectors { ( x u,1 , x u,2 , ..., x u,N …”

Section: Cake-cutting Schedulermentioning

confidence: 99%

“…Let {x * u,b |∀u, b} be the solution to (18). It is proved in [18] that a price equilibrium of PRICEEQUILIBRIUM gives an optimal solution to (18).…”

Section: Cake-cutting Schedulermentioning

confidence: 99%

See 3 more Smart Citations

A Game Theoretic Distributed Algorithm for FeICIC Optimization in LTE-A HetNets

Liu

Chen

Sung

et al. 2017

IEEE/ACM Trans. Networking

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“…It is shown in [18] that (6) is NP-hard. Therefore, both (4) and (5) are NP-hard because a special case of the two problems is NP-hard.…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Convex Schedulermentioning

confidence: 99%

“…We use the fact that the solution to (18) (ν 1 , ν 2 , ..., ν N B ) is called an equilibrium price vector and the nonnegative real vectors { ( x u,1 , x u,2 , ..., x u,N …”

Section: Cake-cutting Schedulermentioning

confidence: 99%

“…Let {x * u,b |∀u, b} be the solution to (18). It is proved in [18] that a price equilibrium of PRICEEQUILIBRIUM gives an optimal solution to (18).…”

Section: Cake-cutting Schedulermentioning

confidence: 99%

See 2 more Smart Citations

A Game Theoretic Distributed Algorithm for FeICIC Optimization in LTE-A HetNets

Liu

Chen

Sung

et al. 2017

IEEE/ACM Trans. Networking

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“…In this paper, we assume that The User Allocator block at the medium access control (MAC) layer allocates the scheduled users optimally over bands with index of = 1, ..., , based on their related CSI. Therefore, each user occupies one band in order to reduce the cell interference and guarantee fair services for all users [7], [8]. The optimal user allocation can increase the average system throughput by exploiting the channel diversity that allows the physical layer to select high MCS levels for the utilized bands.…”

Section: The Proposed Amc-ofdma System Modelmentioning

confidence: 99%

Optimal user scheduling and allocation for WiMAX OFDMA systems

Al-Janabi

Tsimenidis

Sharif

et al. 2010

2010 7th International Symposium on Wireless Communication Systems

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We propose a strategy that optimally schedules and allocates users for adaptive modulation and coding based orthogonal frequency multiple access systems (AMC-OFDMA). This strategy aims to maximize the average system throughput as a function of bit error rate (BER) and spectral efficiency by exploiting the user diversity. It sorts the available users of an assigned base station (BS) in queues based on their priority levels and optimally selects a user from each queue, in which the average system throughout is maximized. Moreover, the proposed strategy guarantees fair services for all users in distinct priority levels. The scheduled users are allocated over the utilized bands in an optimal manner in order to improve the performance of the investigated system. Each user selects a suitable modulation and coding scheme (MCS) based on the corresponding minimum signal-to-noise ratio (SNR). The sub-carriers of the transmitted OFDMA frame are grouped in different bands depending on the number of utilized priority levels in a BS. Simulation results show the performance improvement obtained by the proposed strategy over other compared schemes.

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