Uplink power control with MMSE receiver in multi-cell MU-massive-MIMO systems

Guo, Kunkun; Guo, Yan; Fodor, Gábor; Ascheid, Gerd

doi:10.1109/icc.2014.6884144

Cited by 86 publications

(64 citation statements)

References 10 publications

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“…For example, the authors in [18] formulated the DL energy efficiency optimization problem for the single cell Massive MIMO systems that takes both the transmit and circuit powers into account. The paper [19] considered optimized user-specific pilot and data powers for given QoS constraints, while [20] optimized the max-min SE and sum SE. None of these papers have considered the BS-user association problem.…”

Section: Introductionmentioning

confidence: 99%

Joint Power Allocation and User Association Optimization for Massive MIMO Systems

Chien

Björnson

Larsson

2016

IEEE Trans. Wireless Commun.

165

144

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This paper investigates the joint power allocationand user association problem in multi-cell Massive MIMO(multiple-input multiple-output) downlink (DL) systems. Thetarget is to minimize the total transmit power consumptionwhen each user is served by an optimized subset of the basestations (BSs), using non-coherent joint transmission. We ﬁrstderive a lower bound on the ergodic spectral efﬁciency (SE),which is applicable for any channel distribution and precodingscheme. Closed-form expressions are obtained for Rayleigh fadingchannels with either maximum ratio transmission (MRT) or zeroforcing (ZF) precoding. From these bounds, we further formulatethe DL power minimization problems with ﬁxed SE constraintsfor the users. These problems are proved to be solvable aslinear programs, giving the optimal power allocation and BS-user association with low complexity. Furthermore, we formulatea max-min fairness problem which maximizes the worst SEamong the users, and we show that it can be solved as aquasi-linear program. Simulations manifest that the proposedmethods provide good SE for the users using less transmit powerthan in small-scale systems and the optimal user associationcan effectively balance the load between BSs when needed.Even though our framework allows the joint transmission frommultiple BSs, there is an overwhelming probability that only oneBS is associated with each user at the optimal solution

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Section: Introductionmentioning

confidence: 99%

Joint Power Allocation and User Association Optimization for Massive MIMO Systems

Chien

Björnson

Larsson

2016

IEEE Trans. Wireless Commun.

165

144

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“…In [20], a resource allocation scheme was proposed to maximize the sum spectral efficiency (SE), which determines the optimal values of the pilot sequence length, the pilot signal power, and the data signal power. A joint pilot and data transmit power control was provided in [21] to minimize the total power consumption of all users with the constraints of per user signal to interference plus noise ratio (SINR) and per user power. In [22], a PPA policy was developed to maximize the minimum asymptotic SINR in each cell, by adopting a pilot scheme where all users in each cell share the same pilot sequence and keep the pilot sequences orthogonal for different cells.…”

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confidence: 99%

Pilot Power Allocation Through User Grouping in Multi-Cell Massive MIMO Systems

Liu

Jin

Jiang

et al. 2017

IEEE Trans. Commun.

108

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Abstract-In this paper, we propose a relative channel estimation error (RCEE) metric, and derive closed-form expressions for its expectation Exp rcee and the achievable uplink rate holding for any number of base station antennas M , with the least squares (LS) and minimum mean squared error (MMSE) estimation methods. It is found that RCEE and Exp rcee converge to the same constant value when M → ∞, which renders the pilot power allocation (PPA) substantially simplified and a PPA algorithm is proposed to minimize the average Exp rcee per user with a total pilot power budget P in multi-cell massive multiple-input multiple-output systems. Numerical results show that the PPA algorithm brings considerable gains for the LS estimation compared with equal PPA (EPPA), while the gains are significant only with large frequency reuse factor (FRF) for the MMSE estimation. Moreover, for large FRF and large P , the performance of the LS approaches to the MMSE. Besides, a scheduling strategy is proposed to allocate pilot power whole system, which can approach the optimal performance. For the achievable uplink rate, the PPA scheme delivers almost the same average achievable uplink rate and improves the minimum achievable uplink rate compared with the EPPA scheme.Index Terms-Massive multiple-input multiple-output (MI-MO), pilot power allocation, relative channel estimation error, achievable uplink rate.

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“…Linear detectors can be a trade-off between the performance and the complexity. Typical linear detectors such as zero force (ZF) and minimum mean square error (MMSE) can achieve the suboptimal performance, but they still suffer from the unfavorable matrix inversion with relatively high complexity [10]. Therefore, a low complexity detection algorithm for massive MIMO systems is in urgent need.…”

Section: Introductionmentioning

confidence: 99%

A low-complexity detector for uplink massive MIMO systems based on Gaussian approximate belief propagation

Ling

Song

et al. 2015

2015 International Conference on Wireless Communications &Amp; Signal Processing (WCSP)

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In massive multiple-input multiple-output (MIMO) systems, the number of base station (BS) antennas could reach tens or even hundreds, therefore the hardware cost of adopting conventional signal detectors can be unaffordable due to their high complexity which increases quickly with the number of the transceiver antennas. To solve this problem, this paper proposes a low-complex iterative detector based on Gaussian approximate belief propagation (GABP). The proposed algorithm calculates the first and second order of statistical properties of the messages sent between the transceiver nodes, and substantially reduces the complexity with just matrix-vector multiplication. The complexity of the proposed scheme is proved to be one order of magnitude smaller than that of conventional minimum mean square error (MMSE). The rapid convergence property is evaluated by mean square error (MSE). Simulation results demonstrate that the proposed algorithm could achieve better bit error rate (BER) performance than MMSE with a small number of iterations.

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Uplink power control with MMSE receiver in multi-cell MU-massive-MIMO systems

Cited by 86 publications

References 10 publications

Joint Power Allocation and User Association Optimization for Massive MIMO Systems

Joint Power Allocation and User Association Optimization for Massive MIMO Systems

Pilot Power Allocation Through User Grouping in Multi-Cell Massive MIMO Systems

A low-complexity detector for uplink massive MIMO systems based on Gaussian approximate belief propagation

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