Fairness-Aware Resource Allocation for the SISO Downlink over Frequency-Selective Channels

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

doi:10.1109/wcnc.2010.5506415

Cited by 15 publications

(34 citation statements)

References 22 publications

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“…For each topology, multicast groups are assigned a set of proportional constants

{γ_{g}}_{g MathClass-rel= 1}^{G}

. It is assumed that these constants follow the probability mass function

p_{γ_{g}} MathClass-rel= ({, \begin{matrix} falsefalsearray \\ arraycenter 1 with probability 0.5 \\ arraycenter 2 with probability 0.3 \\ arraycenter 4 with probability 0.2 \end{matrix}) MathClass-punc.

…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In addition, it has a similar complexity to and smaller than . Notice that if each chunk consists of one subcarrier, as in conventional OFDMA systems , complexity becomes larger because in the aforementioned complexities, L is replaced by N > L . Also, complexity becomes larger when the systems are unicast as in that case when G is replaced by K > G .…”

Section: Complexity Analysismentioning

confidence: 99%

“…To reduce complexity, the resource allocation unit is the chunk; and as in , the algorithm is based on ZF and on spatial correlation. Also, it imposes proportional maximum attainable data rate constraints among multicast groups of users as they are important quality of service parameters for multimedia services. Complexity and simulation analyses are provided to further enhance the contribution of our suggestion.…”

Section: Introductionmentioning

confidence: 99%

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Chunk‐based and fairness‐based resource allocation in multicast distributed MISO‐OFDMA systems

Papoutsis

Stamouli

2013

Int J Communication

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SUMMARYThe resource allocation problem for the downlink of orthogonal frequency‐division multiple access (OFDMA) wireless multicast systems is investigated. It is assumed that the base station consists of multiple antennas in a distributed antenna system (DAS), whereas each user is equipped with a single antenna. The multicasting technology is able to support several groups of users with flexible quality of service (QoS) requirements. The general mathematical formulation is provided, but achieving the optimal solution has a high computational cost. In our approach, the allocation unit is not the subcarrier, as in conventional OFDMA systems, but a set of contiguous subcarriers, which is called ‘chunk’. For practical implementation, a suboptimal but efficient algorithm is proposed in order to maximize the sum of the maximum attainable data rates of multicast groups of users, subject to total available power and proportional maximum attainable data rate constraints among multicast groups of users. Simulation and complexity analyses are provided to support the benefits of chunk‐based resource allocation to multicast OFDMA DASs, supporting that the proposed algorithm can be applied to latest‐generation wireless systems that provide QoS guarantees. Copyright © 2013 John Wiley & Sons, Ltd.

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“…For each topology, multicast groups are assigned a set of proportional constants

{γ_{g}}_{g MathClass-rel= 1}^{G}

. It is assumed that these constants follow the probability mass function

p_{γ_{g}} MathClass-rel= ({, \begin{matrix} falsefalsearray \\ arraycenter 1 with probability 0.5 \\ arraycenter 2 with probability 0.3 \\ arraycenter 4 with probability 0.2 \end{matrix}) MathClass-punc.

…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Complexity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Chunk‐based and fairness‐based resource allocation in multicast distributed MISO‐OFDMA systems

Papoutsis

Stamouli

2013

Int J Communication

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“…In , a proportional fairness constraint, which is determined by the system QoS guarantees, is employed. In , a joint subcarrier and power allocation algorithm is proposed with proportional fairness.…”

Section: Introductionmentioning

confidence: 99%

“…Because the resource allocation problem is an NP‐hard (Non deterministic Polynomial) optimization problem with nonlinear constraints , the optimal resource allocation is computationally complex for practical applications Hence, a lot of suboptimal methods are presented to reduce computational complexity . It is shown that the problem can be solved suboptimally by separating subcarrier allocation and power allocation which can obtain close to the capacity as the optimal algorithm does but with a lower computational complexity .…”

Section: Introductionmentioning

confidence: 99%

On the use of immune clonal optimization for joint subcarrier and power allocation in OFDMA with proportional fairness rate

Chai¹,

Liu²,

Qi³

et al. 2012

Int J Communication

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SUMMARY Resource allocation in orthogonal frequency division multiple access is a constraint optimization problem. In this paper, we concentrate on maximizing both the sum capacity and proportional fairness rate among users. The optimization model of resource allocation is formulated and then an immune clonalbased algorithm is proposed for it. The resource allocation is solved by separating the subcarrier and power allocation in two steps. Suitable immune operators are designed, such as clonal, mutation, Baldwin learning, selection and so on. Experiments show that, compared with the previous methods, the proposed algorithm obtains higher sum capacity with comparable computational complexity, and keeps the proportional rate more fairly among users. Copyright © 2012 John Wiley & Sons, Ltd.

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

Papoutsis¹,

Fraimis²,

Kotsopoulos³

2010

Trans Emerging Tel Tech

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SUMMARYThe problem of user selection and resource allocation for the downlink of wireless systems operating over a frequency-selective channel is investigated. It is assumed that the Base Station (BS) uses many antennas, whereas a single antenna is available to each user (Multiple Input Single Output-MISO case). To relieve heavy computational burden, a suboptimal, but efficient algorithm is devised that is based on Zero Forcing (ZF) beamforming and is less complex than other approaches. The algorithm maximises the sum of the users' data rates subject to constraints on total available power and proportional fairness among users' data rates. Simulation results are provided to indicate that the algorithm can satisfy the fairness criterion. Thus, the algorithm can be applied to latest-generation wireless systems that provide Quality-of-Service (QoS) guarantees.

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Fairness-Aware Resource Allocation for the SISO Downlink over Frequency-Selective Channels

Cited by 15 publications

References 22 publications

Chunk‐based and fairness‐based resource allocation in multicast distributed MISO‐OFDMA systems

Chunk‐based and fairness‐based resource allocation in multicast distributed MISO‐OFDMA systems

On the use of immune clonal optimization for joint subcarrier and power allocation in OFDMA with proportional fairness rate

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

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