Mobility-Aware Caching and Computation Offloading in 5G Ultra-Dense Cellular Networks

Chen, Min; Hao, Yixue; Qiu, Meikang; Song, Jeungeun; Wu, Di; Humar, Iztok

doi:10.3390/s16070974

Cited by 149 publications

(64 citation statements)

References 42 publications

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“…Moreover, sufficient link quality should be ensured, as stated in References [23,24], because data transmission from the on-body relay to the C-MEC suffers from high energy attenuation. Moreover, similar to one of the key research challenges in IoT offloading strategies, the issue of where to execute the computation tasks should be further investigated when designing the healthcare monitoring systems [25,26].…”

Section: Introductionmentioning

confidence: 99%

Relay-Enabled Task Offloading Management for Wireless Body Area Networks

Liao

Han

et al. 2018

Applied Sciences

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Inspired by the recent developments of the Internet of Things (IoT) relay and mobile edge computing (MEC), a hospital/home-based medical monitoring framework is proposed, in which the intensive computing tasks from the implanted sensors can be efficiently executed by on-body wearable devices or a coordinator-based MEC (C-MEC). In this paper, we first propose a wireless relay-enabled task offloading mechanism that consists of a network model and a computation model. Moreover, to manage the computation resources among all relays, a task offloading decision model and the best task offloading recipient selection function is given. The performance evaluation considers different computation schemes under the predetermined link quality condition regarding the selected vital quality of service (QoS) metrics. After demonstrating the channel characterization and network topology, the performance evaluation is implemented under different scenarios regarding the network lifetime of all relays, network residual energy status, total number of locally executed packets, path loss (PL), and service delay. The results show that data transmission without the offloading scheme outperforms the offload-based technique regarding network lifetime. Moreover, the high computation capacity scenario achieves better performance regarding PL and the total number of locally executed packets.

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Section: Introductionmentioning

confidence: 99%

Relay-Enabled Task Offloading Management for Wireless Body Area Networks

Liao

Han

et al. 2018

Applied Sciences

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“…In the future, the 5G mobile communication system in 2020 will provide higher data transmission rate, more service connections and better user experience while ensuring low cost, transmission security, reliability and stability [13]. Channel coding in 5G mobile communication involves non-binary LDPC codes [14].…”

Section: Introductionmentioning

confidence: 99%

Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection

Meng

Zhao

Tian

et al. 2018

Sensors

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In order to improve the performance of non-binary low-density parity check codes (LDPC) hard decision decoding algorithm and to reduce the complexity of decoding, a sum of the magnitude for hard decision decoding algorithm based on loop update detection is proposed. This will also ensure the reliability, stability and high transmission rate of 5G mobile communication. The algorithm is based on the hard decision decoding algorithm (HDA) and uses the soft information from the channel to calculate the reliability, while the sum of the variable nodes’ (VN) magnitude is excluded for computing the reliability of the parity checks. At the same time, the reliability information of the variable node is considered and the loop update detection algorithm is introduced. The bit corresponding to the error code word is flipped multiple times, before this is searched in the order of most likely error probability to finally find the correct code word. Simulation results show that the performance of one of the improved schemes is better than the weighted symbol flipping (WSF) algorithm under different hexadecimal numbers by about 2.2 dB and 2.35 dB at the bit error rate (BER) of 10−5 over an additive white Gaussian noise (AWGN) channel, respectively. Furthermore, the average number of decoding iterations is significantly reduced.

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“…Edge cloud and remote cloud serve as the storage and computing infrastructure of the system. The variety of terminals, such as smart terminals, humanoid robots, smart clothing and smart cars, etc., utilize various radio access technologies, forming the cognitive sensing layer (or the infrastructure layer) of the system [29][30][31][32].…”

Section: Network Architecture Of 5g-csysmentioning

confidence: 99%

A 5G Cognitive System for Healthcare

Chen

Yang

Hao

et al. 2017

BDCC

Self Cite

125

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Developments and new advances in medical technology and the improvement of people's living standards have helped to make many people healthier. However, there are still large design deficiencies due to the imbalanced distribution of medical resources, especially in developing countries. To address this issue, a video conference-based telemedicine system is deployed to break the limitations of medical resources in terms of time and space. By outsourcing medical resources from big hospitals to rural and remote ones, centralized and high quality medical resources can be shared to achieve a higher salvage rate while improving the utilization of medical resources. Though effective, existing telemedicine systems only treat patients' physiological diseases, leaving another challenging problem unsolved: How to remotely detect patients' emotional state to diagnose psychological diseases. In this paper, we propose a novel healthcare system based on a 5G Cognitive System (5G-Csys). The 5G-Csys consists of a resource cognitive engine and a data cognitive engine. Resource cognitive intelligence, based on the learning of network contexts, aims at ultra-low latency and ultra-high reliability for cognitive applications. Data cognitive intelligence, based on the analysis of healthcare big data, is used to handle a patient's health status physiologically and psychologically. In this paper, the architecture of 5G-Csys is first presented, and then the key technologies and application scenarios are discussed. To verify our proposal, we develop a prototype platform of 5G-Csys, incorporating speech emotion recognition. We present our experimental results to demonstrate the effectiveness of the proposed system. We hope this paper will attract further research in the field of healthcare based on 5G cognitive systems.

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Mobility-Aware Caching and Computation Offloading in 5G Ultra-Dense Cellular Networks

Cited by 149 publications

References 42 publications

Relay-Enabled Task Offloading Management for Wireless Body Area Networks

Relay-Enabled Task Offloading Management for Wireless Body Area Networks

Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection

A 5G Cognitive System for Healthcare

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