Antenna location design for distributed antenna systems with selective transmission

Qian, Yunzhi; Chen, Ming; Wang, Xinzheng; Zhu, Pengcheng

doi:10.1109/wcsp.2009.5371490

Cited by 29 publications

(22 citation statements)

References 12 publications

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“…We assume the user is uniformly distributed in the circular cell centered at (0, 0) with radius R. According to [48] and [49], if the number of RAUs I is smaller than 6, the location of the j-th RAU is (r cos(2π(j − 1)/I), r sin(2π(j − 1)/I)) for j = 1, · · · , I, where r = 2R sin(π/I)/(3π/I). Otherwise, the first RAU is located at the cell center (0, 0), and the other I − 1 RAUs are located at (r cos(2π(j − 2)/(I − 1)), r sin(2π(j − 2)/(I − 1))) with j = 2, · · · , I.…”

Section: Simulation Resultsmentioning

confidence: 99%

Energy Efficiency Optimization for MIMO Distributed Antenna Systems

Ren

Liu

Pan

et al. 2017

IEEE Trans. Veh. Technol.

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In this paper, we propose a transmit covariance optimization method to maximize the energy efficiency (EE) for a single-user distributed antenna system, where both the remote access units (RAUs) and the user are equipped with multiple antennas. Unlike previous related work, both the rate requirement and RAU selection are taken into consideration. Here, the total circuit power consumption is related to the number of active RAUs. Given this setup, we first propose an optimal transmit covariance optimization method to solve the EE optimization problem under a fixed set of active RAUs. More specifically, we split this problem into three subproblems, i.e., the rate maximization problem, the EE maximization problem without rate constraint, and the power minimization problem, and each subproblem can be efficiently solved. Then, a novel distance-based RAU selection method is proposed to determine the optimal set of active RAUs. Simulation results show that the performance of the proposed RAU selection is almost identical to the optimal exhaustive search method with significantly reduced computational complexity, and the performance of the proposed algorithm significantly outperforms the existing EE optimization methods.

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Section: Simulation Resultsmentioning

confidence: 99%

Energy Efficiency Optimization for MIMO Distributed Antenna Systems

Ren

Liu

Pan

et al. 2017

IEEE Trans. Veh. Technol.

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“…As a result, the * Correspondence: zekeriya.uykan@aum.edu.kw SDC [6] received much attention within the DAS academic community, and following in the footsteps of the SDC and the analysis in [6], several other papers further investigated the SDC for different DAS scenarios. In [12], the SDC is applied to the downlink DAS with selection transmission (ST) in a single cell. The squareddistance-divided-power-criterion is proposed in [13] for linear DAS, which similarly maximizes a lower bound to the ergodic capacity.…”

Section: Introductionmentioning

confidence: 99%

Composite vector quantization for optimizing antenna locations

Uykan

Jäntti²

2018

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, we study the location optimization problem of remote antenna units (RAUs) in generalized distributed antenna systems (GDASs). We propose a composite vector quantization (CVQ) algorithm that consists of unsupervised and supervised terms for RAU location optimization. We show that the CVQ can be used i) to minimize an upper bound to the cell-averaged SNR error for a desired/demanded location-specific SNR function, and ii) to maximize the cell-averaged effective SNR. The CVQ-DAS includes the standard VQ, and thus the well-known squared distance criterion (SDC) as a special case. Computer simulations confirm the findings and suggest that the proposed CVQ-DAS outperforms the SDC in terms of cell-averaged "effective SNR".

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“…Recent research concerning antenna location of GDAS mainly focus on coverage performance analysis and antenna location design criteria [6]- [12]. A multi-cell scenario where 6 distributed antennas are placed along a circle in each cell is considered in [6].…”

Section: Introductionmentioning

confidence: 99%

Impact of Path Loss Exponents on Antenna Location Design for GDAS

Zhang

Diao

Zhao

et al. 2012

2012 IEEE 75th Vehicular Technology Conference (VTC Spring)

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In this paper, the impact of path loss exponents on antenna location design in generalized distributed antenna system (GDAS) is investigated, with the goal of maximizing a lower bound of the average uplink capacity. A simple case when antenna ports (AP) are uniformly placed along a circle is studied first. Both the upper and lower bounds for the optimal placement radius are given. An approximate expression of the optimal radius with sufficient accuracy is derived using Newton-Cotes integration formula with a degree of 5. The given expression indicates that with different path loss exponents the optimal locations of APs differ. The interesting thing is that the optimal radius decreases as the path loss exponent grows when the number of APs is large, while it is the opposite when the number of APs is small. Analytical results are then extended to more general scenarios and a new criterion is proposed for antenna location design. Simulation results show that the given criterion outperforms those in previous literature, and the achieved capacity gain can be as much as 10%.

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Antenna location design for distributed antenna systems with selective transmission

Cited by 29 publications

References 12 publications

Energy Efficiency Optimization for MIMO Distributed Antenna Systems

Energy Efficiency Optimization for MIMO Distributed Antenna Systems

Composite vector quantization for optimizing antenna locations

Impact of Path Loss Exponents on Antenna Location Design for GDAS

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