Deep Learning-Based Cross-Layer Power Allocation for Downlink Cell-Free Massive Multiple-Input–Multiple-Output Video Communication Systems

Lin, Wen-Yen; Chang, Tin-Hao; Tseng, Shu-Ming

doi:10.3390/sym15111968

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…Also developing a model-based precoding optimizer algorithm is difficult task. Therefore, in this paper, we have designed a deep learning approach (19,20) for generating optimal matrix. In CFMM system spatial complexity increases with increase in number of APs, so with traditional precoding schemes, implementation cost of the network increases.…”

Section: Introductionmentioning

confidence: 99%

Downlink Learning-aided Precoding for Cell-Free networks Specially D esigned for beyond 5G Communication System

Deshpande,

Aggarwal,

Sabherwal

et al. 2024

IJST

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Objectives: With increase in number of access points in Cell-Free Massive Multiple Input Multiple Output (CFMM) system, spatial complexity of calculating precoding vector increases with traditional precoding schemes. So, to reduce computational complexity, network cost, and run time, a new deep learning -aided precoder is specially designed for CFMM system. The performance of downlink (DL) Cell-Free Massive Multiple Input Multiple Output (CFMM) system operating under Rayleigh fading channel model is analyze using new deep learning-based precoding scheme. Methods: We introduce new deep learning-aided precoder and an improved version of basic scalable pilot assignment algorithm which enhances system performance. We derive closed- form expression for average DL spectral efficiency (SE) for the proposed scheme, which is then compared with Minimum Mean Square Error Successive Interference Cancellation (MMSESIC), Regularized Zero Forcing (RZF), Least Square (LS) and Maximum Ratio (MR) combining techniques. We analyze the proposed scheme with perfect channel state information (CSI), instantaneous CSI, coherent transmission, non-coherent transmission, different pilot configuration. Findings: The spectral efficiency obtained with CFMM-MMSESIC and the proposed scheme is 3 bits/s/Hz, and 2.5bits/s/Hz respectively, which clearly indicates the difference of 20 % only. So, the proposed scheme gives near optimal results, and we can use simple linear combining techniques instead of complex non-linear combining techniques. Also, the proposed scheme with coherent data transmission gives performance curve, which is very close to MMSESIC scheme as desired. The performance of the proposed system is improved by reducing run time and computational complexity as compared to conventional linear precoding schemes. Novelty: The proposed precoder improves performance by reducing run time and computational complexity. Advanced pilot assignment algorithm enhances performance by reducing pilot contamination. Keywords: Cell-Free Massive Multiple Input Multiple Output, Pilot Contamination, Precoding, Minimum Mean Square Error Successive Interference Cancellation, Maximum Ratio, Regularized Zero Forcing (RZF), Least Square (LS)

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Section: Introductionmentioning

confidence: 99%

Downlink Learning-aided Precoding for Cell-Free networks Specially D esigned for beyond 5G Communication System

Deshpande,

Aggarwal,

Sabherwal

et al. 2024

IJST

View full text Add to dashboard Cite

show abstract

“…Massive multiple input multiple output (MIMO) makes a substantial contribution to augmenting the throughput of wireless communication systems and providing high-speed data services, which is essential in future mobile communication systems [1,2]. Massive MIMO systems can be categorized into two principal domains: time division duplexing (TDD) systems and frequency division duplexing (FDD) systems where orthogonal time and frequency are recognized to distinguish the uplink and downlink [3,4].…”

Section: Introductionmentioning

confidence: 99%

Multi-Head Transformer Architecture with Higher Dimensional Feature Representation for Massive MIMO CSI Feedback

Chen,

Guo,

Cui

2024

Applied Sciences

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To achieve the anticipated performance of massive multiple input multiple output (MIMO) systems in wireless communication, it is imperative that the user equipment (UE) accurately feeds the channel state information (CSI) back to the base station (BS) along the uplink. To reduce the feedback overhead, an increasing number of deep learning (DL)-based networks have emerged, aimed at compressing and subsequently recovering CSI. Various novel structures are introduced, among which Transformer architecture has enabled a new level of precision in CSI feedback. In this paper, we propose a new method named TransNet+ built upon the Transformer-based TransNet by updating the multi-head attention layer and implementing an improved training scheme. The simulation results demonstrate that TransNet+ outperforms existing methods in terms of recovery accuracy and achieves state-of-the-art.

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Machine Learning Assisted Cross-Layer Joint Optimal Subcarrier and Power Allocation for Device-to-Device Video Transmission

Tseng,

Wu,

Fang

2024

IEEE Access

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Deep Learning-Based Cross-Layer Power Allocation for Downlink Cell-Free Massive Multiple-Input–Multiple-Output Video Communication Systems

Cited by 4 publications

References 28 publications

Downlink Learning-aided Precoding for Cell-Free networks Specially D esigned for beyond 5G Communication System

Downlink Learning-aided Precoding for Cell-Free networks Specially D esigned for beyond 5G Communication System

Multi-Head Transformer Architecture with Higher Dimensional Feature Representation for Massive MIMO CSI Feedback

Machine Learning Assisted Cross-Layer Joint Optimal Subcarrier and Power Allocation for Device-to-Device Video Transmission

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