Transfer Learning for Channel Quality Prediction

Parera, Claudia; Redondi, Alessandro E. C.; Cesana, Matteo; Liao, Qi; Malanchini, Ilaria

doi:10.1109/iwmn.2019.8805017

Cited by 21 publications

(9 citation statements)

References 13 publications

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“…The authors in [86] focus on evaluating different TL approaches for traffic prediction in wireless networks. Specifi-cally, the authors develop and compare a CNN and an LSTM to predict channel quality indicator (CQI) in two scenarios, i.e., different frequency bands of the same 4G cell and different cells using the same frequency band.…”

Section: Channel Estimation and Predictionmentioning

confidence: 99%

“…The reason is that the surrounding environments of the source and the target are familiar, and thus their Qvalues might be similar. Alternatively, TL approaches such as [86] and [87] transfer knowledge of the same task in a different frequency band, whereas some other approaches transfer knowledge from previous experiences. Moreover, many of the surveyed approaches do not clarify why the sources are chosen.…”

Section: Challenges Open Issues and Future Research Directionsmentioning

confidence: 99%

“…Despite its importance, there are few works that attempt to address this challenge. In [86] and [71], the authors determine the number of layers to transfer through simulations, i.e., testing different numbers of layers and choose the one with the best result.…”

Section: Challenges Open Issues and Future Research Directionsmentioning

confidence: 99%

“…Generally, the more layers we fine-tune, the better the model performs because all the weights are adjusted to the target's domain. However, this also necessitates more training time as shown in [86] and [71]. Moreover, in the case where the target data is limited, fine-tuning all the layers may even lead to overfitting (as shown in the results of [169] and [168]) since the available labeled data do not represent the domain well.…”

Section: Challenges Open Issues and Future Research Directionsmentioning

confidence: 99%

See 3 more Smart Citations

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Nguyen,

Van Huynh,

Chu

et al. 2021

Preprint

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With outstanding features, Machine Learning (ML) has been the backbone of numerous applications in wireless networks. However, the conventional ML approaches have been facing many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, will impede the effectiveness and applicability of ML in future wireless networks. To address these problems, Transfer Learning (TL) has recently emerged to be a very promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks as well as from valuable experiences accumulated from the past to facilitate the learning of new problems. Doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods' robustness to different wireless environments. This article aims to provide a comprehensive survey on applications of TL in wireless networks. Particularly, we first provide an overview of TL including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, localization, signal recognition, security, human activity recognition and caching, which are all important to nextgeneration networks such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.

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Section: Channel Estimation and Predictionmentioning

confidence: 99%

Section: Challenges Open Issues and Future Research Directionsmentioning

confidence: 99%

Section: Challenges Open Issues and Future Research Directionsmentioning

confidence: 99%

Section: Challenges Open Issues and Future Research Directionsmentioning

confidence: 99%

See 2 more Smart Citations

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Nguyen,

Van Huynh,

Chu

et al. 2021

Preprint

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“…Previous applications of transfer learning to communication systems include beamforming for multi-user, multiple-input, single-output (MISO) downlink [ 25 ] and for intelligent reflecting surfaces (IRS)-assisted MISO downlink [ 26 ], and downlink channel prediction [ 27 , 28 ] (see also [ 25 , 27 ]). Meta-learning has been applied to communication systems, including demodulation [ 29 , 30 , 31 , 32 ], decoding [ 33 ], end-to-end design of encoding and decoding with and without a channel model [ 34 , 35 ]; MIMO detection [ 36 ], beamforming for multiuser MISO downlink systems via [ 37 ], layered division multiplexing for ultra-reliable communications [ 38 ], UAV trajectory design [ 39 ], and resource allocation [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Speeding up Training of Linear Predictors for Multi-Antenna Frequency-Selective Channels via Meta-Learning

Park

Simeone

2022

Entropy

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An efficient data-driven prediction strategy for multi-antenna frequency-selective channels must operate based on a small number of pilot symbols. This paper proposes novel channel-prediction algorithms that address this goal by integrating transfer and meta-learning with a reduced-rank parametrization of the channel. The proposed methods optimize linear predictors by utilizing data from previous frames, which are generally characterized by distinct propagation characteristics, in order to enable fast training on the time slots of the current frame. The proposed predictors rely on a novel long short-term decomposition (LSTD) of the linear prediction model that leverages the disaggregation of the channel into long-term space-time signatures and fading amplitudes. We first develop predictors for single-antenna frequency-flat channels based on transfer/meta-learned quadratic regularization. Then, we introduce transfer and meta-learning algorithms for LSTD-based prediction models that build on equilibrium propagation (EP) and alternating least squares (ALS). Numerical results under the 3GPP 5G standard channel model demonstrate the impact of transfer and meta-learning on reducing the number of pilots for channel prediction, as well as the merits of the proposed LSTD parametrization.

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ADAPTIVE6G: Adaptive Resource Management for Network Slicing Architectures in Current 5G and Future 6G Systems

Thantharate

Beard²

2022

J Netw Syst Manage

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Transfer Learning for Channel Quality Prediction

Cited by 21 publications

References 13 publications

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Speeding up Training of Linear Predictors for Multi-Antenna Frequency-Selective Channels via Meta-Learning

ADAPTIVE6G: Adaptive Resource Management for Network Slicing Architectures in Current 5G and Future 6G Systems

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