A Resource Allocation Scheme with Fractional Frequency Reuse in Multi-cell OFDMA Systems

Pao, Wei Chen; Wang, Wen Bin; Tseng, Shu-Ming; Chen, Yung‐Fang

doi:10.1007/s11277-018-5803-5

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…Step 3. To satisfy Constraint 2, we also choose from x to 16 of Fig. 4, but skip those already chosen in Step 2 (x, z, |, and }).…”

Section: Dnn Output Layer Conversion Algorithm To Meet Constraintsmentioning

confidence: 99%

“…Additionally, orthogonal frequency division multiple access (OFDMA) is used in Wi-Fi, 4G and 5G New Radio Phase I [6] due to its increased bandwidth efficiency, inter-symbol interference tolerance, and flexible resource allocation. In [14]- [16], the resources are allocated according to the channel state information (CSI) in the physical layer. On the other hand, [17]- [21] allocate the resources according to the rate distortion (RD) function in the application layer.…”

Section: Introductionmentioning

confidence: 99%

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Deep-Learning-Aided Cross-Layer Resource Allocation of OFDMA/NOMA Video Communication Systems

et al. 2019

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In previous study, deep learning and autoencoder have been applied for data detection of NOMA systems, rather than the resource allocation of OFDMA/NOMA systems. In previous work, we proposed the use of non-deep-learning-based cross-layer resource allocation for OFDMA/NOMA video communication systems. In this paper, we apply a deep neural network and supervised learning to an OFDMA subcarrier assignment and NOMA user grouping problem in downlink video communication systems. The resource allocation results from our previous work are used as training data at the training stage. At the testing stage, we propose a conversion algorithm to map the result of the sigmoid activation function (values between [0,1]) of the output layer of the DNN to either zero (unassigned) or one (assigned), in order to meet two hard constraints. The PSNR performance is very close (within 0.2dB) to that but has lower complexity, due to the non-iterative approach used in the testing stage of the DNN.

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“…Step 3. To satisfy Constraint 2, we also choose from x to 16 of Fig. 4, but skip those already chosen in Step 2 (x, z, |, and }).…”

Section: Dnn Output Layer Conversion Algorithm To Meet Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Aided Cross-Layer Resource Allocation of OFDMA/NOMA Video Communication Systems

et al. 2019

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Cross-Layer Codebook Allocation for Uplink SCMA and PDNOMA-SCMA Video Transmission Systems and a Deep Learning-Based Approach

Tseng

Chen

2023

IEEE Systems Journal

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Coherence Bandwidth and Its Relation to Other Channel Metrics for Urban Mobile Radio Channels at 2 GHz

Clarke¹

2020

Antennas Wirel. Propag. Lett.

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A Resource Allocation Scheme with Fractional Frequency Reuse in Multi-cell OFDMA Systems

Cited by 4 publications

References 25 publications

Deep-Learning-Aided Cross-Layer Resource Allocation of OFDMA/NOMA Video Communication Systems

Deep-Learning-Aided Cross-Layer Resource Allocation of OFDMA/NOMA Video Communication Systems

Cross-Layer Codebook Allocation for Uplink SCMA and PDNOMA-SCMA Video Transmission Systems and a Deep Learning-Based Approach

Coherence Bandwidth and Its Relation to Other Channel Metrics for Urban Mobile Radio Channels at 2 GHz

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