Capacity Optimization of MIMO Wireless Communication Systems Using a Hybrid Genetic-Taguchi Algorithm

Recioui, Abdelmadjid; Bentarzi, Hamid

doi:10.1007/s11277-012-0857-2

Cited by 18 publications

(14 citation statements)

References 21 publications

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“…5, in which the first letter refers to the transmitter and the second to the receiver. Each array is linear and uniformly spaced due to the fact that although it has been reported in literature better performances for non-uniform and non-linear arrays [26][27][28][29], the aim of this measurement campaign was to compare the results obtained with those deduced in [21] for 200 MHz bandwidth. The large size of the antennas made us to separate the elements of the array at least 4 cm.…”

Section: Channel Sounder Setupmentioning

confidence: 99%

Mimo Capacity in Uwb Channels in an Office Environment for Different Polarizations

Garcia-Pardo¹,

Molina‐Garcia‐Pardo²,

Rodríguez³

et al. 2013

PIER C

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Abstract-In this paper, a 4 × 4 indoor Multiple-Input MultipleOutput Ultra-Wideband (MIMO-UWB) measurement campaign in the 2-5 GHz bandwidth is presented. The main contribution of this work is the impact of radio-wave polarization as well as the effect of frequency dependence on the capacity of MIMO-UWB systems working in an office environment. To accomplish this, the capacity for different polarizations is analyzed under two different assumptions: constant or variable Signal-to-Noise Ratio.

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Section: Channel Sounder Setupmentioning

confidence: 99%

Mimo Capacity in Uwb Channels in an Office Environment for Different Polarizations

Garcia-Pardo¹,

Molina‐Garcia‐Pardo²,

Rodríguez³

et al. 2013

PIER C

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“…Such advantages motivate us to resort to the WOA algorithm to solve the problem in Eq. (9). However, it is difficult to directly deal with the above mentioned problem in Eq.…”

Section: Binary Whale Optimization Algorithm (Bwoa)mentioning

confidence: 99%

“…One is adaptive sampling implemented by the technique of antenna selection [5] or controlled parasitic elements [6,7], and the other is nonuniform sparse array arrangements [8]. Because the channel capacity of a MIMO system is closely associated with the scattering environment, antenna array system and position of elements, over the past few years, some evolutionary algorithms have also been applied to the optimization of capacity of MIMO systems, such as hybrid Genetic-Taguchi algorithm [9], Galaxy-based search algorithm [10], and spiral optimization technique [11]. Herein we optimize the antenna layout with binary whale optimization (BIWO) algorithm, based on an efficient algorithm, the whale optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Mimo System With Scattering Using Binary Whale Optimization Algorithm With Crossover Operator

Yuan¹,

Guo²,

Zheng³

2019

PIER Letters

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In a MIMO system, scattering is always an important problem since it is closely related to the channel capacity of system. In most of previous works, scattering was usually neglected so as to simplify the process of analysis. Therefore, it is really necessary to investigate and understand the scattering effect on capacity. To this end, scattering is taken into consideration in terms of channel capacity in this paper. From the antenna point of view, antenna element layout can be viewed as an optimization problem. To resolve this problem, a binary whale optimization algorithm (BWOA) is proposed. We investigate the effect of scattering environment on the capacity of a MIMO system and make comparison with an existing method in performance. The simulated results demonstrate that the nonuniform sampling method is able to efficiently improve the capacity of system even for poor scattering environment. SYSTEM MODEL AND PROBLEM FORMULATIONConsider a narrow-band MIMO system, which consists of N t transmitter and N r receiver antennas. Assume that X ∈ C Nt×1 represents the vector of transmit signals and that N ∈ C Nr×1 denotes the Algorithm 1: Pseudo code of the BWOA Input: input parameters t, t max , l, r Output: X * Initialize the whales population X i (i = 1, 2, · · · , n) 1 Calculate the fitness of each search agent 2 X * =the best search agent 3 while t < t max do 4 for each search agent do 5 Update α, A, C, l and p 6 if p < 0.5 then 7 if |A| ≤ 1 then 8 Update the position of the current search agent by D = |C · x * (t) − X(t)| 9 X(t + 1) = X * (t) − A · D else 10 Update the position of the current search agent by the D = |C · x rand − X(t)| 11 X(t + 1) = x rand − A · D else 12 Update the position of the current search agent by D = |X * (t) − X(t)| 13 X(t + 1) = D · e bl + cos(2πl) + X(t) Calculate the probabilities using a transfer function taking Eqs. (12) and (13) 14 Crossover operator between x i and x i−1 by Eqs. (10) and (11) 15 Calculate the fitness of each search agent 16 Update X * if there is a better solution 17 t = t + 1 18

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“…Specifically, the channel capacity increases with the number of antennas even with small antenna spacing. For nonuniform antenna arrays, the array configuration can be optimized to maximize the channel capacity [16].…”

Section: An 8 × 8 Terrestrial Mimo Link Using Dual Polarized Antennasmentioning

confidence: 99%

Mimo Antennas for a Terrestrial Point-to-Point Wireless Link: From the Optimum Antenna Spacing to a Compact Array

Smith²,

Yang³

2016

PIER B

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Abstract-Multiple-input-multiple-output antennas are investigated for terrestrial point-to-point wireless link. The lack of rich scatters in a terrestrial wireless channel results in an ill-conditioned channel matrix for a long range link with compact arrays, which can cause a high bit error rate. This paper demonstrates that the channel matrix can be improved by carefully selecting the antenna spacing. Unfortunately, an optimum antenna spacing that guarantees a good channel matrix is too large to implement for most long range terrestrial wireless links. On the other hand, a channel capacity study of an 8 × 8 link reveals that multiple antennas do provide more capacity even with small antenna spacing. Constellation multiplexing is then applied to the compact array configuration to solve the unreliable communication problem. In addition, a multilevel maximum ratio combining technique is introduced to improve detection efficiency.

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Capacity Optimization of MIMO Wireless Communication Systems Using a Hybrid Genetic-Taguchi Algorithm

Cited by 18 publications

References 21 publications

Mimo Capacity in Uwb Channels in an Office Environment for Different Polarizations

Mimo Capacity in Uwb Channels in an Office Environment for Different Polarizations

Synthesis of Mimo System With Scattering Using Binary Whale Optimization Algorithm With Crossover Operator

Mimo Antennas for a Terrestrial Point-to-Point Wireless Link: From the Optimum Antenna Spacing to a Compact Array

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