Sum-rate maximization in the multicell MIMO broadcast channel with interference coordination

Nguyen, Duy H. N.; Le‐Ngoc, Tho

doi:10.1109/icc.2013.6655348

Cited by 2 publications

(21 citation statements)

References 32 publications

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“…Also assume that

{scriptI}_{k, i}^{-}

to be the set of indexes of the users that are out of the set

I_{k, i}

except [ k , i ]. Then Equation can be written as follows:

\begin{matrix} {\hat{boldx}}^{[k, i]} & = {boldU}^{{[k, i]}^{H}} \sum_{[ℓ, j] \in {scriptI}_{k, i}} {boldH}_{j}^{[k, i]} {boldV}^{[ℓ, j]} {boldx}^{[ℓ, j]} \\ + {boldU}^{{[k, i]}^{H}} {boldn}^{[k, i]} . \end{matrix}

Now, let Γ [ k , i ] represents the interference plus the noise covariance matrix for the [ k , i ]‐th user:

Γ^{false[k, i false]} = \sum_{false[ℓ, j false] \in I_{k, i}} {boldH}_{j}^{false[k, i false]} V^{false[ℓ, j false]} {boldV}^{{false[ℓ, j false]}^{H}} {boldH}_{j}^{{false[k, i false]}^{H}} + σ_{n}^{2} boldI,

then the minimum mean squared error (MMSE) receive filter for the [ k , i ]‐th user is obtained by

{boldU}_{MMSE}^{false[k, i false]} = Γ^{false[k, i false]}

…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…Although the optimization problem (Equation ) is not jointly convex in V , U , and W , it is marginally convex in each one and can be solved iteratively by fixing 2 of them and updating the third sequentially . For the case of using DPC, the WMMSE minimization problem subject to per‐user power constraints can be solved via a computationally efficient algorithm as proposed in the work of Lee et al Following the work of Sun and de Carvalho, by introducing a slack variable β [ k , i ] , the optimization problem can be written as

\begin{matrix} \underset{V, β^{[k, i]}}{minimize} \sum_{i = 1}^{C} \sum_{k = 1}^{K_{i}} α^{[k, i]} Tr ({boldW}^{[k, i]} E [({β^{[k, i]}}^{- 1} {\overset{x}{true}}^{[k, i]} - \\ {boldx}^{[k, i]}) {({β^{[k, i]}}^{- 1} {\overset{x}{true}}^{[k, i]} - {boldx}^{[k, i]})}^{H}]) \\ subject to Tr ({boldV}^{[k, i]} {boldV}^{{[k, i]}^{H}}) ⩽ P^{[k, i]}, \forall i, k . \end{matrix}

Using the slack varia...…”

Section: Wsr Maximization With Per‐user Power Constraintsmentioning

confidence: 99%

“…Assuming M i = M , K i = K , N i = d i = N ; ∀ i and that κ denotes the total number of users in the system, the complexity of each iteration of the algorithm is the sum complexity of calculating V [ k , i ] , U [ k , i ] , and W [ k , i ] . The number of complex multiplications for calculating V [ k , i ] in Equation is

((), false(C - 1 false) K + k) false(2 N^{3} + M^{2} N + M N^{2} false) + N^{3} + M N^{2} .

Similarly, the number of complex multiplications for calculating U [ k , i ] in Equation is

((), false(C - 1 false) K + false(K - k false)) false(N^{3} + M^{2} N false) + N^{3} + M N^{2},

and eventually for W [ k , i ] we have

2 N^{2} M + N^{3} .

Finally, the overall computational complexity of the proposed algorithm in each iteration is no more than

scriptO ((), \frac{κ}{2} false[K + 3 false] false(3 M N^{2} + M^{2} N + M^{3} false) + 3 κ N^{3}),

where κ is the total number of users in the system. The total computational complexity is the number of iterations multiplied by Equation , where as mentioned before the number of iterations is 10 to 15 for the WBPU and 15 to 20 for the GBPU.…”

Section: Optimizing Per‐user Power Constraintsmentioning

confidence: 99%

“…Partially coordinated networks form an interfering multiple‐access channel in the uplink transmission and an interfering broadcast channel (IF‐BC) in the downlink transmission . These two types of operations are called the full coordination and interference coordination modes in the LTE‐Advanced standard . A review of theoretical and practical issues relating to fully coordinated networks can be found in the work of Gesbert et al…”

Section: Introductionmentioning

confidence: 99%

“…The performance of the proposed algorithms is evaluated using computer simulations and is compared to the performance of the recently proposed algorithms in the works of Nguyen and Le‐Ngoc, and Lee et al under 2 different channel settings. The results show that when the channel entries are independent circularly symmetric complex‐Gaussian random variables, the proposed algorithms outperform the current algorithms significantly at high SNRs.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Weighted sum rate maximization in MIMO interfering broadcast channels exploiting virtual per‐user power constraints

Masoumzadeh

Sabahi

Forouzan

2016

Trans. Emerging Tel. Tech.

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In this paper, we consider the weighted sum rate (WSR) maximization problem in a partially coordinated multiple‐input multiple‐output multicell broadcast channel with constraints on base stations' transmit powers where the communication is based on dirty paper coding. To be able to obtain a computationally efficient solution for this non‐convex optimization problem, we solve it by a fast algorithm for WSR maximization under per‐user power constraints. The main idea is to define virtual per‐user power constraints that add up to the per‐base station power budgets in each cell and optimize the per‐user power constraints to maximize the WSR. We propose two computationally efficient power allocation algorithms to find the per‐user power constraints, namely, the waterfilling‐based power update and the gradient descent–based power update algorithms. In the latter one, we find a closed‐form expression for the derivative of the maximum of WSR wrt the per‐user power constraints resulting in a considerable reduction in the complexity. The resulting algorithms are computationally efficient, and our simulation results show that they achieve significantly higher bit rates than the current algorithms at the same signal‐to‐noise power ratio.

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“…Also assume that