Proactive Caching in D2D Assisted Multitier Cellular Network

Ahmad, Fawad; Ahmad, Ayaz; Hussain, Irshad; Muhammad, Ghulam; Uddin, Zahoor; AlQahtani, Salman A.

doi:10.3390/s22145078

Cited by 10 publications

(3 citation statements)

References 52 publications

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“…To evaluate the performance of SE methods, we have used four objective metrics including Short-time objective intelligibility (STOI) [56], extended STOI (ESTOI) [57], Perceptual evaluation of speech quality (PESQ) [58] and Source-todistortion ratio (SDR) [59].…”

Section: B Evaluation Metricsmentioning

confidence: 99%

U-Shaped Low-Complexity Type-2 Fuzzy LSTM Neural Network for Speech Enhancement

et al. 2023

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Speech enhancement (SE) aims to improve the intelligibility and perceptual quality of speech contaminated by noise signals through spectral or temporal changes. Deep learning models achieve speech enhancement and estimate the magnitude spectrum. This paper proposes a novel and computationally efficient deep learning model to enhance noisy speech. The model pre-processes the noisy speech magnitude by redistributing energy from high-energy voiced segments to low-energy unvoiced segments using an adaptive power law transformation while maintaining the total energy of the speech signals constant. A U-shaped fuzzy long short-term memory (UFLSTM) estimates the magnitude of a time-frequency (T-F) mask by using the pre-processed data. Residual connections to the similar-shaped layers are added to avoid gradient decay. Attention process is adopted by modifying the forget gate of UFLSTM. To make a causal speech enhancement system, the processing does not include any future audio frames. We compare the proposed speech enhancement to other deep learning models in different noisy environments with signal-to-noise ratios of 0 dB, 5 dB, and 10 dB. The experiments show that the proposed SE system outscores the competing deep learning models and considerably improves speech intelligibility and quality. In terms of STOI and PESQ, the LibriSpeech database improves results by (0.211) 21.1% and (0.95) 36.39%, respectively, over noisy speech in seen noisy conditions, and by (0.199) 19.9% and (0.94) 35.69% over noisy speech in unseen noisy conditions. Further, the cross-corpus analysis shows that proposed SE system performs better when trained with the DNS dataset as compared to the LibriSpeech, VoiceBank, and TIMIT datasets.

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Section: B Evaluation Metricsmentioning

confidence: 99%

U-Shaped Low-Complexity Type-2 Fuzzy LSTM Neural Network for Speech Enhancement

et al. 2023

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“…The cache-enabled D2Dassisted wireless networks can greatly reduce data traffic strain and successfully address the issue of content placement and content delivery. Therefore, in [7], the authors presented joint data delivery and content placement to improve D2D communications. A support vector machine was devised to determine data popularity profile.…”

Section: Introductionmentioning

confidence: 99%

Mobility-Aware Data Caching to Improve D2D Communications in Heterogeneous Networks

et al. 2022

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User Equipment (UE) is equipped with limited cache resources that can be utilized to offload data traffic through device-to-device (D2D) communications. Data caching at a UE level has the potential to significantly alleviate data traffic burden from the backhaul link. Moreover, in wireless networks, users exhibit mobility that poses serious challenges to successful data transmission via D2D communications due to intermittent connectivity among users. Users’ mobility can be exploited to efficiently cache contents by observing connectivity patterns among users. Therefore, it is crucial to develop an efficient data caching mechanism for UE while taking into account users’ mobility patterns. In this work, we propose a mobility-aware data caching approach to enhance data offloading via D2D communication. First, we model users’ connectivity patterns. Then, contents are cached in UE’ cache resources based on users’ data preferences. In addition, we also take into account signal-to-interference and noise ratio (SINR) requirements of the users. Hence, our proposed caching mechanism exploits connectivity patterns of users to perform data placement based on users’ own demands and neighboring users to enhance data offloading via cache resources. We performed extensive simulations to investigate the performance of our proposed mobility-aware data caching mechanism. The performance of our proposed caching mechanism is compared to most deployed data caching mechanisms, while taking into account the dynamic nature of the wireless channel and the interference experienced by the users. From the obtained results, it is evident that our proposed approach achieves 14%, 16%, and 11% higher data offloading gain than the least frequently used, the Zipf-based probabilistic, and the random caching schemes in case of an increasing number of users, cache capacity, and number of contents, respectively. Moreover, we also analyzed cache hit rates, and our proposed scheme achieves 8% and 5% higher cache hit rate than the least frequently used, the Zipf-based probabilistic, and the random caching schemes in case of an increasing number of contents and cache capacity, respectively. Hence, our proposed caching mechanism brings significant improvement in data sharing via D2D communications.

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“…Prediction problems are studies in which people speculate about the trends that will emerge in the future based on the development patterns of historical things. Research on prediction problems is developing rapidly in a wide range of fields such as epidemiology [ 11 , 12 ], network science [ 13 , 14 ], engineering management [ 15 , 16 ], and cloud computing [ 17 , 18 ]. In the field of service recommendation, current research on QoS prediction problems can be divided into two categories, static QoS prediction methods [ 19 , 20 , 21 , 22 , 23 ] and dynamic QoS prediction methods [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic QoS Prediction Algorithm Based on Kalman Filter Modification

Yan

Sun

Zhang

et al. 2022

Sensors

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With the widespread adoption of service-oriented architectures (SOA), services with the same functionality but the different Quality of Service (QoS) are proliferating, which is challenging the ability of users to build high-quality services. It is often costly for users to evaluate the QoS of all feasible services; therefore, it is necessary to investigate QoS prediction algorithms to help users find services that meet their needs. In this paper, we propose a QoS prediction algorithm called the MFDK model, which is able to fill in historical sparse QoS values by a non-negative matrix decomposition algorithm and predict future QoS values by a deep neural network. In addition, this model uses a Kalman filter algorithm to correct the model prediction values with real-time QoS observations to reduce its prediction error. Through extensive simulation experiments on the WS-DREAM dataset, we analytically validate that the MFDK model has better prediction accuracy compared to the baseline model, and it can maintain good prediction results under different tensor densities and observation densities. We further demonstrate the rationality of our proposed model and its prediction performance through model ablation experiments and parameter tuning experiments.

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Proactive Caching in D2D Assisted Multitier Cellular Network

Cited by 10 publications

References 52 publications

U-Shaped Low-Complexity Type-2 Fuzzy LSTM Neural Network for Speech Enhancement

U-Shaped Low-Complexity Type-2 Fuzzy LSTM Neural Network for Speech Enhancement

Mobility-Aware Data Caching to Improve D2D Communications in Heterogeneous Networks

Dynamic QoS Prediction Algorithm Based on Kalman Filter Modification

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