Prediction and Measurement of Multiuser MIMO-OFDM Channel in Rural Australia

Suzuki, Hideyuki; Ratnayake, Nisal Lahiru; Ziri-Castro, Karla

doi:10.1109/vetecs.2012.6240211

Cited by 17 publications

(10 citation statements)

References 4 publications

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“…The simulation is implemented in the Matlab environment. We implement Algorithm 1 within a channel that was tested by Commonwealth Scientific and Industrial Research Organization (CSIRO) in rural Australia [34]. γ is set as 1.0 × 10 −4 , and η is set as 70%.…”

Section: Numerical Resultsmentioning

confidence: 99%

A Parallel Low Complexity Zero-Forcing Beamformer Design for Multiuser MIMO Systems Via a Regularized Dual Decomposition Method

Dam

et al. 2015

IEEE Trans. Signal Process.

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Zero-forcing beamforming under per-antenna power constraint (PAPC) is considered in this paper and the objective is to maximize the minimum user information rate. A parallel low complexity zeroforcing beamformer design is proposed in this paper for MU-MIMO systems by introducing a regularized dual decomposition method. The idea of this method is to solve the problem via solving its dual problem.Since the dual objective is not differentiable, a Tikhonov regularization is introduced. The regularized problem can be solved by using a gradient-based method in a parallel manner. Moreover, the optimal solution of the Lagrangian is in a closed form. The smoothness properties of the regularized dual function are investigated. We also estimate the error bound between the optimal function value of the primal problem and that of the regularized dual problem. Corresponding convergence analysis and convergence rate of the proposed algorithm are established. Computational complexity analysis is carried out to compare the complexity of the proposed method with that of state-of-the-art interior point method.Simulation results are provided to show the effectiveness of the proposed method.

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Section: Numerical Resultsmentioning

confidence: 99%

A Parallel Low Complexity Zero-Forcing Beamformer Design for Multiuser MIMO Systems Via a Regularized Dual Decomposition Method

Dam

et al. 2015

IEEE Trans. Signal Process.

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“…We note that a similar three dimensional geometric optics based model has been proposed for analyzing single user MIMO in rural areas in [7]. The validity of the geometric optics based model for modeling MU-MIMO channels in rural area was verified against measurement results obtained in an actual rural environment in [8].…”

Section: Introductionmentioning

confidence: 90%

Large-scale multiple antenna fixed wireless systems for rural areas

Suzuki

Collings

Hayman

et al. 2012

2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC)

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Multi-user multiple-input multiple-output (MU-MIMO) has a potential to realize cost effective high data rate internet access to the homes in rural areas. We propose the novel Ngara Access system where the central access point (AP) is equipped with a uniform circular array (UCA) installed on a high tower while each UT is equipped with a directional antenna free of clutter, providing predominantly line-of-sight (LoS) channel environment. Using a three dimensional geometric optics based channel model, we provide bit error probability simulation results which show that the spectral efficiency of the proposed system can be improved linearly as a function of the number of antenna elements at AP, without increasing the total transmitting power, provided a half wavelength antenna spacing is maintained and user groups of four or more are used to avoid ill-conditioned channel. Hardware demonstrators based on the proposed system have achieved the system spectral efficiency of 20 bits/s/Hz in an actual rural environment and of 67 bits/s/Hz in a laboratory environment at a lower UHF band.

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“…Denote by the number of times the dual cost is required to be calculated for the step size search algorithm. Then, the multiplications required for Algorithm III.2 is: (17) Hence, the total number of multiplications required for each iteration of the accelerated gradient with step size search is . …”

Section: Complexity Comparisonmentioning

confidence: 99%

“…The base station array considered is a uniform planar circular array consisting of a ring with ideal dipole antennas and interelement spacing equal to , where is the wavelength of the radio frequency carrier. The wireless channel between each base station array antenna and a user antenna is modeled as in [16] and [17]. The channel is comprised of a direct line of sight propagation path and a ground reflected propagation path.…”

Section: Design Examplesmentioning

confidence: 99%

A Fast Low Complexity Method for Optimal Zero-Forcing Beamformer MU-MIMO System

Dam

Cantoni

2015

IEEE Signal Process. Lett.

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This letter proposes a new algorithm for solving the optimal zero-forcing beamforming problem that maximizes the user achievable rate with restriction on the per-antenna element power constraints. An accelerated gradient method with step size search is proposed for solving the problem. For each iteration of the gradient approach, a quick one dimensional search is employed to obtain the step size. The advantage of the step-size search is that it is relatively fast and requires only a few calculations of the objective function. Design examples show that the proposed algorithm converges faster than the gradient approach and the accelerated gradient approach with a constant step size while achieving a low computational complexity.Index Terms-Accelerated gradient method, beamformer, MU-MIMO, optimal step size, optimum zero-forcing, per-antenna power constraints.

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Prediction and Measurement of Multiuser MIMO-OFDM Channel in Rural Australia

Cited by 17 publications

References 4 publications

A Parallel Low Complexity Zero-Forcing Beamformer Design for Multiuser MIMO Systems Via a Regularized Dual Decomposition Method

A Parallel Low Complexity Zero-Forcing Beamformer Design for Multiuser MIMO Systems Via a Regularized Dual Decomposition Method

Large-scale multiple antenna fixed wireless systems for rural areas

A Fast Low Complexity Method for Optimal Zero-Forcing Beamformer MU-MIMO System

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