Joint Sub-Channel Assignment and Power Allocation in Heterogeneous Networks: An Efficient Optimization Method

Khamidehi, Behzad; Rahmati, Ali; Sabbaghian, Maryam

doi:10.1109/lcomm.2016.2597147

Cited by 40 publications

(56 citation statements)

References 10 publications

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“…Detailed explanation of how these weights are determined and updated will be given in Section 4.2. As a result, following the approach presented in the work of Khamidehi et al, we define our optimization problem as

\begin{matrix} alignleft \\ align-1 \underset{X, P}{maximize} & align-2 R^{normald} + R^{normalu} & (P 1) \\ align-1 subject to: & align-2 \sum_{k \in K} \sum_{n \in N} p_{k, j, n}^{normald} \leq P_{j}^{normalB}, \forall j \in J & (C 1) \\ align-1 & align-2 \sum_{j \in J} \sum_{n \in N} p_{k, j, n}^{normalu} \leq P_{k}^{normalU}, \forall k \in K & (C 2) \\ align-1 & align-2 0 \leq p_{k, j, n}^{normald} \leq x_{k, j, n}^{normald} P_{j}^{normalB} \forall k \in K, \forall j \in J, \forall n \in N & (C 3) \\ align-1 & align-2 0 \leq p_{k, j, n}^{normalu} \leq x_{k, j, n}^{normalu} P_{k}^{normalU} \forall k \in K, \forall j \in J, \forall n \in N & (C 4) \\ align-1 & align-2 \sum_{k \in K} x_{k, j, n}^{normald} \leq 1, \sum_{k \in K} x_{<...>} \end{matrix}

…”

Section: Problem Formulationmentioning

confidence: 99%

“…Constraints C1 and C 2 place a limit on the maximum transmit powers of the BSs and users, respectively, where

P_{j}^{normalB}

is the maximum transmit power of the j th BS and

P_{k}^{normalU}

is the maximum transmit power of the k th user.

x_{k, j, n}^{normald}

and

x_{k, j, n}^{normalu}

are defined as

x_{k, j, n}^{d} = \{\begin{matrix} 1, if subchannel n in the j th BS is assigned to the k th user in downlink direction \\ 0, otherwise, \end{matrix}

and

x_{k, j, n}^{u} = \{\begin{matrix} 1, if subchannel n in the j th BS is assigned to the k th user in uplink direction \\ 0, otherwise. \end{matrix}

In order to account for subchannel assignment variables in the objective function, we add constraints C 3 and C 4 as described in the work of Khamidehi et al To elaborate on this point, we note that, when

x_{k, j, n}

…”

Section: Problem Formulationmentioning

confidence: 99%

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Joint user association, subchannel assignment, and power allocation in full‐duplex OFDMA heterogeneous networks

Firouzabadi

Rabiei

2018

Trans Emerging Tel Tech

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In‐band full‐duplex (FD) radio is regarded as a promising solution to enhance the spectral efficiency in the next‐generation wireless networks. In this paper, the problem of joint user association, subchannel assignment, and power allocation in FD orthogonal frequency‐division multiple access heterogeneous networks is considered. The weighted downlink and uplink sum rate of the network is maximized in a way that the transmit power of base stations and users remains below a given level. The problem is formulated as a nonconvex mixed‐integer nonlinear programming optimization problem. Then, it is converted into a suboptimal problem, which can be solved iteratively using difference of convex programming algorithm. Numerical results show that the proposed iterative algorithm converges quickly in few numbers of iterations with different initialization points. The results also show that the sum rate of an FD heterogeneous network can be as much as 66 % higher than the sum rate of a half‐duplex network.

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\begin{matrix} alignleft \\ align-1 \underset{X, P}{maximize} & align-2 R^{normald} + R^{normalu} & (P 1) \\ align-1 subject to: & align-2 \sum_{k \in K} \sum_{n \in N} p_{k, j, n}^{normald} \leq P_{j}^{normalB}, \forall j \in J & (C 1) \\ align-1 & align-2 \sum_{j \in J} \sum_{n \in N} p_{k, j, n}^{normalu} \leq P_{k}^{normalU}, \forall k \in K & (C 2) \\ align-1 & align-2 0 \leq p_{k, j, n}^{normald} \leq x_{k, j, n}^{normald} P_{j}^{normalB} \forall k \in K, \forall j \in J, \forall n \in N & (C 3) \\ align-1 & align-2 0 \leq p_{k, j, n}^{normalu} \leq x_{k, j, n}^{normalu} P_{k}^{normalU} \forall k \in K, \forall j \in J, \forall n \in N & (C 4) \\ align-1 & align-2 \sum_{k \in K} x_{k, j, n}^{normald} \leq 1, \sum_{k \in K} x_{<...>} \end{matrix}

…”

Section: Problem Formulationmentioning

confidence: 99%

“…Constraints C1 and C 2 place a limit on the maximum transmit powers of the BSs and users, respectively, where

P_{j}^{normalB}

is the maximum transmit power of the j th BS and

P_{k}^{normalU}

is the maximum transmit power of the k th user.

x_{k, j, n}^{normald}

and

x_{k, j, n}^{normalu}

are defined as

x_{k, j, n}^{d} = \{\begin{matrix} 1, if subchannel n in the j th BS is assigned to the k th user in downlink direction \\ 0, otherwise, \end{matrix}

and

x_{k, j, n}^{u} = \{\begin{matrix} 1, if subchannel n in the j th BS is assigned to the k th user in uplink direction \\ 0, otherwise. \end{matrix}

x_{k, j, n}

…”

Section: Problem Formulationmentioning

confidence: 99%

Joint user association, subchannel assignment, and power allocation in full‐duplex OFDMA heterogeneous networks

Firouzabadi

Rabiei

2018

Trans Emerging Tel Tech

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“…The joint subchannel and power allocation problem in the OFDMA‐based HetNets is extensively studied in the literature . Some works studied the joint subchannel and power allocation problem in the downlink of HetNets .…”

Section: Introductionmentioning

confidence: 99%

“…The macro‐tier problem is addressed by a suboptimal distributed power control algorithm and the femto‐tier problem is addressed by a suboptimal joint subchannel and power allocation algorithm. Khamidehi et al formulated the joint subchannel and power allocation problem in the uplink of femto‐tier as an optimization problem to maximize the weighted sum rate of the femto‐tier under the macrocell's cross‐tier interference constraint and the minimum data rate requirement constraint for the high data rate FUEs. To address this problem, first the problem is reformulated and then the integer variable is relaxed and the objective function is rewritten as the difference of two concave (DC) functions.…”

Section: Introductionmentioning

confidence: 99%

“…Our key contributions in this paper can be summarized as follows: We propose a tier‐aware joint subchannel and power allocation model for the uplink of a two‐tier OFDMA‐based HetNet, where the interaction between the macro‐tier and femto‐tier is considered (in contrast to the works of Ngo et al, Zhang et al, and Khamidehi et al). We formally state the problem of uplink joint subchannel and power allocation for macro‐tier to maximize the sum of tolerable interference of the MUEs, where it is assumed that each MUE may be allocated more than one subchannel. This problem has not been studied for the uplink of macro‐tier yet and to address it, we reformulate and transform this problem to an MILP problem, which is solved optimally.…”

Section: Introductionmentioning

confidence: 99%

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Tier‐aware joint subchannel and power allocation in uplink OFDMA heterogeneous networks

Rajabi

Rasti

Pedram

et al. 2018

Trans Emerging Tel Tech

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We study the problem of tier‐aware subchannel and power allocation in the uplink of a two‐tier orthogonal frequency‐division multiple access heterogeneous network. We formulate the joint subchannel and power allocation problem in the macro‐tier as an optimization problem that is aware of the existence of the femto‐tier and aims to maximize the sum of tolerable interference caused by femto‐tier on its allocated subchannel(s) subject to the minimum data rate requirements of the macrocell user equipments (MUEs). The resource allocation problem for the macro‐tier is an NP‐hard mixed‐integer nonlinear programming (MINLP) problem. To address it, we reformulate and transform it to a tractable mixed‐integer linear programming (MILP) problem, which is optimally addressed with polynomial‐time complexity. We formulate the joint subchannel and power allocation problem in the femto‐tier as an optimization problem that is aware of the existence of the macro‐tier and aims to maximize the sum rate of the femtocell user equipments subject to the maximum tolerable interference caused to the MUEs. To address this MINLP problem, we transform it to a MILP problem through a linear approximation, which is solved optimally by IBM CPLEX solver. In addition, we develop a distributed and efficient algorithm that addresses this optimization problem suboptimally with a lower computational complexity. Numerical results show that our proposed algorithms outperform the existing algorithms in terms of network sum rate.

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Efficient Transmission of Multi Satellites-Multi Terrestrial Nodes Under Large-Scale Deployment of LEO

Sun

Cao

2020

Communications in Computer and Information Science

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Joint Sub-Channel Assignment and Power Allocation in Heterogeneous Networks: An Efficient Optimization Method

Cited by 40 publications

References 10 publications

Joint user association, subchannel assignment, and power allocation in full‐duplex OFDMA heterogeneous networks

Joint user association, subchannel assignment, and power allocation in full‐duplex OFDMA heterogeneous networks

Tier‐aware joint subchannel and power allocation in uplink OFDMA heterogeneous networks

Efficient Transmission of Multi Satellites-Multi Terrestrial Nodes Under Large-Scale Deployment of LEO

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