Massive MIMO interference coordination for 5G broadband access: Integration and system level study

Grassi, Alessandro; Piro, Giuseppe; Boggia, Gennaro; Kurras, Martin; Zirwas, Wolfgang; Ganesan, Rakash SivaSiva; Pedersen, Klaus I.; Thiele, Lars

doi:10.1016/j.comnet.2018.10.012

Cited by 13 publications

(9 citation statements)

References 29 publications

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“…Since every user of the multiuser system should track the interference beam, this constraint simplifies the problem notably. Moreover, since a single phase bit of F rf can be modified in every iteration, mitigating the impact of flashlight interference in adjacent cells [12]. The Adam optimization algorithm has been used to adjust neural networks, reading mini-batches [13] of size equal to the number of coefficients in the first layer of the neural network.…”

Section: Simulation Scenario and Analysis Of Resultsmentioning

confidence: 99%

Deep reinforcement learning for hybrid beamforming in multi-user millimeter wave wireless systems

Lizárraga

Maggio

Dowhuszko

2021

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)

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This paper proposes a Machine Learning (ML) algorithm for hybrid beamforming in millimeter-wave wireless systems with multiple users. The time-varying nature of the wireless channels is taken into account when training the ML agent, which identifies the most convenient hybrid beamforming matrix with the aid of an algorithm that keeps the amount of signaling information low, avoids sudden changes in the analog beamformers radiation patterns when scheduling different users (flashlight interference), and simplifies the hybrid beamformer update decisions by adjusting the phases of specific analog beamforming vectors. The proposed hybrid beamforming algorithm relies on Deep Reinforcement Learning (DRL), which represents a practical approach to embed the online adaptation feature of the hybrid beamforming matrix into the channel states of continuous nature in which the multiuser MIMO system can be. Achievable data rate curves are used to analyze performance results, which validate the advantages of DRL algorithms with respect to solutions relying on conventional/deterministic optimization tools.

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Section: Simulation Scenario and Analysis Of Resultsmentioning

confidence: 99%

Deep reinforcement learning for hybrid beamforming in multi-user millimeter wave wireless systems

Lizárraga

Maggio

Dowhuszko

2021

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)

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“…Authors (Grassi et al, 2018) concentrated on the interference coordination for MIMO systems. In this, the channel state information (CSI) of the different users was considered to reduce the interference during the DT.…”

Section: Interference Mitigationmentioning

confidence: 99%

3D MIMO Beamforming Using Spatial Distance SVM Algorithm and Interference Mitigation for 5G Wireless Communication Network

Yadav

Tripathi

2022

Journal of Cases on Information Technology

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In recent decades, Multiple Input Multiple Output beamforming is deliberated as the vital technology enablers for 5G mobile radio services. Since, it provides noticeable improvement regarding throughput and coverage measures in 5G networks. Primarily, executed 3D MIMO beamforming using the modified Support Vector Machine algorithm which forms beam effectually to the users. The interference is mitigated in two stages that are small cell interference and macro cell interference by measuring the interference power from the cells. To provide better security to the data transmitted over Device-to-Device communication, Advanced Encryption Standard algorithm is used. The results attained from the simulations are auspicious in terms of metrics including throughput, Signal to Interference plus Noise Ratio (SINR) and Signal to Noise Ratio (SNR). From the simulation results, we prove that our ML-3DIM method increases throughput, SINR, SNR by up to 20%, 30% and 35% respectively compared to the existing methods including PABM, ULABM, and NOMA.

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“…The authors in [28] suggested a new novel idea that changes the CRE area and the ABS dynamically based on the cell traffics of each cell in the Heterogeneous network [28]. Some researchers tried to get a better performance for the user in the 5G system like in [29] where they investigated the interference coordination in massive MIMO and how that will be affected by the transfer from 4G to 5G network. Also the authors in [30] suggested many techniques to mitigate the inter cell interference: ICIC of pilot sequence configuration for cell-edge users, coordinated small cell DTX for the downlink and on-demand power boosting and coordinated muting.…”

Section: Related Work In Icicmentioning

confidence: 99%

Inter-cell interference mitigation using adaptive reduced power subframes in heterogeneous networks

Aal-Nouman¹,

Abdullah

Shaikhli³

2021

IJECE

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With the remarkable impact and fast growth of the mobile networks, the mobile base stations have been increased too, especially in the high population areas. These base stations will be overloaded by users, for that reason the small cells (like pico cells) were introduced. However, the inter-cell interference will be high in this type of Heterogeneous networks. There are many solutions to mitigate this interference like the inter-cell interference coordination (ICIC), and then the further enhanced ICIC (Fe-ICIC) where the almost blank subframes are used to give priority to the (victim users). But it could be a waste of bandwidth due to the unused subframes. For that reason, in this paper, we proposed an adaptive reduced power subframe that reduces its power ratio according to the user’s signal-to-interference-plus-noise ratio (SINR) in order to get a better throughput and to mitigate the intercell interference. When the user is far from the cell, the case will be considered as an edge user and will get a higher priority to be served first. The results show that the throughput of all users in the macro cells and pico cell will be improved when applying the proposed scheme in term of throughput for the users and the cells.

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Massive MIMO interference coordination for 5G broadband access: Integration and system level study

Cited by 13 publications

References 29 publications

Deep reinforcement learning for hybrid beamforming in multi-user millimeter wave wireless systems

Deep reinforcement learning for hybrid beamforming in multi-user millimeter wave wireless systems

3D MIMO Beamforming Using Spatial Distance SVM Algorithm and Interference Mitigation for 5G Wireless Communication Network

Inter-cell interference mitigation using adaptive reduced power subframes in heterogeneous networks

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