Contribution of multiplexing and diversity to ergodic capacity of spatial multiplexing MIMO channels at finite SNR

Arar, Maher; Yongaçoğlu, Abbas

doi:10.1109/cwit.2011.5872146

Cited by 1 publication

(2 citation statements)

References 7 publications

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“…Dynamic adjustment of the antenna downtilt angle at the base station (BS) transmitter can provide multiple possibilities for transmitting 3D beamforming signals to the mobile station (MS), significantly improving system performance. Therefore, extending the existing 2D channel model to a 3D channel needs to consider the elevation angle of the propagation path and introduce the parameter into the channel model instead of being assumed to be fixed [22][23][24][25]. According to the antenna configuration in [5], the effective 3D channel matrix [H] su between the sth antenna of the base station (BS) transmitter and the uth antenna of the mobile station (MS) receiver can be written as…”

Section: Mimo System Architecturementioning

confidence: 99%

“…A factor of α = 0 makes the agent learn nothing (fully exploiting previous knowledge), while a factor of α = 1 makes the agent only consider the latest information (ignoring previous knowledge to explore future possibilities). In a fully deterministic environment, the learning rate of α = 1 is optimal; then, Equation ( 24) will be reduced to Equation (25) as follows:…”

Section: Simulation Procedures With the Q-learning Algorithmmentioning

confidence: 99%

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Maximizing Channel Capacity of 3D MIMO System via Antenna Downtilt Angle Adaptation Using a Q-Learning Algorithm

Lee¹,

Shi²,

Tan

et al. 2022

Electronics

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3D MIMO introduces the vertical dimension of the antenna downtilt angle to make the direction of signal transmission more accurate to improve system capacity. In this paper, we verify the effect of antenna downtilt angle on channel capacity through simulations of four fixed antenna downtilt angles, 90, 96, 99, and 102 degrees under the conditions that the distance between mobile station (MS) and base station (BS) is 250 m, and the heights of antenna in BS and MS are 25 m and 1.5 m, respectively. The simulation results show that the antenna downtilt angle of 96 degrees has a larger channel capacity than the others. In addition, we proposed an adaptive optimization method by applying the Q-learning algorithm to adaptively optimize the antenna downtilt angles to maximize system capacity. The performance of the proposed method is to investigate the Q-learning algorithm with three different discount rates at 0.9, 0.5, and 0.1, and four different propagation distances on 20 × 1 and 60 × 4 MIMO. We demonstrate that there is only a 1% difference between the adaptively optimized antenna downtilt angle and the ideal optimal antenna downtilt angle when the discount rate of Q-learning algorithm is 0.9, and its channel capacity performance can reach more than 99.72% of the ideal optimal one.

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