A Novel Algorithm to Report CSI in MIMO-Based Wireless Networks

Shehzad, Muhammad Karam; Rose, Luca; Assaad, Mohamad

doi:10.1109/icc42927.2021.9500480

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Note that P-1 is a convex optimization problem and can be solved using the interior point method or other convex optimization tools such as CVX [38]. Additionally, the constraint in (7) is to ensure that the resultant covariance channel matrix is positive semidefinite which is the inherent characteristic of the matrix.…”

Section: System Modelmentioning

confidence: 99%

“…In Multiple Input and Multiple Output (MIMO) wireless communication systems, with proper transmitter precoding and receiver signal processing, high diversity and multiplexing gains can be achieved to meet the increasing demands of high data rates and low latency applications [ 1 ]. To realize the full benefits of MIMO, accurate channel state information (CSI) at the transmitter is essential for various techniques including precoding, bit-loading, adaptive modulation, channel-aware scheduling, and beamforming [ 2 , 3 , 4 , 5 , 6 , 7 ]. In particular, the degree of CSI accuracy can significantly affect the performance of most MIMO-enabled wireless systems [ 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

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Enhanced MIMO CSI Estimation Using ACCPM with Limited Feedback

Al-Asadi,

Al-Saedi,

Alwane

et al. 2023

Sensors

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Multiple Input and Multiple Output (MIMO) is a promising technology to enable spatial multiplexing and improve throughput in wireless communication networks. To obtain the full benefits of MIMO systems, the Channel State Information (CSI) should be acquired correctly at the transmitter side for optimal beamforming design. The analytical centre-cutting plane method (ACCPM) has shown to be an appealing way to obtain the CSI at the transmitter side. This paper adopts ACCPM to learn down-link CSI in both single-user and multi-user scenarios. In particular, during the learning phase, it uses the null space beamforming vector of the estimated CSI to reduce the power usage, which approaches zero when the learned CSI approaches the optimal solution. Simulation results show our proposed method converges and outperforms previous studies. The effectiveness of the proposed method was corroborated by applying it to the scattering channel and winner II channel models.

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Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced MIMO CSI Estimation Using ACCPM with Limited Feedback

Al-Asadi,

Al-Saedi,

Alwane

et al. 2023

Sensors

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“…Lastly, collaboration between the BS and a UE is indispensable, posing new challenges for the standardization bodies [12]. A different approach has been proposed in [3], [7], [36], where the CSI prediction technique has been exploited to enhance CSI quality. In a nutshell, twin channel predictors (CPs) are used at the BS and UE to enhance precoding gain and reduce OTA overhead.…”

Section: A Csi Feedback: Motivation and State-of-the-artmentioning

confidence: 99%

“…In a nutshell, twin channel predictors (CPs) are used at the BS and UE to enhance precoding gain and reduce OTA overhead. One of the limitations of the works proposed in [7] and [36] is the synchronization of twin CPs. Besides, reporting massive amounts of ML-based trained weights to the BS is another crucial issue, which causes high OTA overhead [3].…”

Section: A Csi Feedback: Motivation and State-of-the-artmentioning

confidence: 99%

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Real-Time Massive MIMO Channel Prediction: A Combination of Deep Learning and NeuralProphet

Shehzad

Rose

Azam

et al. 2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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In the literature, machine learning (ML) has been implemented at the base station (BS) and user equipment (UE) to improve the precision of downlink channel state information (CSI). However, ML implementation at the UE can be infeasible for various reasons, such as UE power consumption. Motivated by this issue, we propose a CSI learning mechanism at BS, called CSILaBS, to avoid ML at UE. To this end, by exploiting channel predictor (CP) at BS, a light-weight predictor function (PF) is considered for feedback evaluation at the UE. CSILaBS reduces over-the-air feedback overhead, improves CSI quality, and lowers the computation cost of UE. Besides, in a multiuser environment, we propose various mechanisms to select the feedback by exploiting PF while aiming to improve CSI accuracy. We also address various ML-based CPs, such as NeuralProphet (NP), an ML-inspired statistical algorithm. Furthermore, inspired to use a statistical model and ML together, we propose a novel hybrid framework composed of a recurrent neural network and NP, which yields better prediction accuracy than individual models. The performance of CSILaBS is evaluated through an empirical dataset recorded at Nokia Bell-Labs. The outcomes show that ML elimination at UE can retain performance gains, for example, precoding quality.

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