Bit-level Optimized Neural Network for Multi-antenna Channel Quantization

Quantized channel state information (CSI) plays a critical role in precoding design which helps reap the merits of multiple-input multiple-output (MIMO) technology. In order to reduce the overhead of CSI feedback, we propose a deep learning based CSI quantization method by developing a joint convolutional residual network (JC-ResNet) which benefits MIMO channel feature extraction and recovery from the perspective of bit-level quantization performance. Experiments show that our proposed method substantially improves the performance

Dynamic resource scheduling in cloud radio access network with mobile cloud computing

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile cloud computing (MCC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile users' devices to provide better quality of service (QoS). But the power consumption has become skyrocketing for MSP as it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MCC separately while less work had considered the integration of C-RAN with MCC. In this paper, we present a unifying framework for optimizing the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MCC to minimize the power consumption of MSP while still guaranteeing the QoS for mobile users. Our objective is to maximize the profit of MSP. To achieve this objective, we first formulate the resource scheduling issue as a stochastic problem and then propose a Resource onlIne sCHeduling (RICH) algorithm using Lyapunov optimization technique to approach a time average profit that is close to the optimum with a diminishing gap (1/V) for MSP while still maintaining strong system stability and low congestion to guarantee the QoS for mobile users. With extensive simulations, we demonstrate that the profit of RICH algorithm is 3.3× (18.4×) higher than that of active (random) algorithm

Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm

Workplace Social Capital and Mental Health among Chinese Employees: A Multi-Level, Cross-Sectional Study

Background: Whereas the majority of previous research on social capital and health has been on residential neighborhoods and communities, the evidence remains sparse on workplace social capital. To address this gap in the literature, we examined the association between workplace social capital and health status among Chinese employees in a large, multilevel, cross-sectional study. Methods: By employing a two-stage stratified random sampling procedure, 2,796 employees were identified from 35 workplaces in Shanghai during March to November 2012. Workplace social capital was assessed using a validated and psychometrically tested eight-item measure, and the Chinese language version of the WHO-Five Well-Being Index (WHO-5) was used to assess mental health. Control variables included sex, age, marital status, education level, occupation status, smoking status, physical activity, and job stress. Multilevel logistic regression analysis was conducted to explore whether individual- and workplace-level social capital was associated with mental health status. Results: In total, 34.9% of workers reported poor mental health (WHO-5,13). After controlling for individual-level sociodemographic and lifestyle variables, compared to workers with the highest quartile of personal social capital, workers with the third, second, and lowest quartiles exhibited 1.39 to 3.54 times greater odds of poor mental health, 1.39 (95% CI: 1.10– 1.75), 1.85 (95% CI: 1.38–2.46) and 3.54 (95% CI: 2.73–4.59), respectively. Corresponding odds ratios for workplace-level social capital were 0.95 (95% CI: 0.61–1.49), 1.14 (95% CI: 0.72–1.81) and 1.63 (95% CI: 1.05–2.53) for the third, second, and lowest quartiles, respectively. Conclusions: Higher workplace social capital is associated with lower odds of poor mental health among Chinese employees. Promoting social capital at the workplace may contribute to enhancing employees’ mental health in China

Maximizing the Profit of Cloud Broker with Priority Aware Pricing

A practical problem facing Infrastructure-as-a-Service (IaaS) cloud users is how to minimize their costs by choosing different pricing options based on their own demands. Recently, cloud brokerage service is introduced to tackle this problem. But due to the perishability of cloud resources, there still exists a large amount of idle resource waste during the reservation period of reserved instances. This idle resource waste problem is challenging cloud broker when buying reserved instances to accommodate users' job requests. To solve this challenge, we find that cloud users always have low priority jobs (e.g., non latency-sensitive jobs) which can be delayed to utilize these idle resources. With considering the priority of jobs, two problems need to be solved. First, how can cloud broker leverage jobs' priorities to reserve resources for profit maximization? Second, how to fairly price users' job requests with different priorities when previous studies either adopt pricing schemes from IaaS clouds or just ignore the pricing issue. To solve these problems, we first design a fair and priority aware pricing scheme, PriorityPricing, for the broker which charges users with different prices based on priorities. Then we propose three dynamic algorithms for the broker to make resource reservations with the objective of maximizing its profit. Experiments show that the broker's profit can be increased up to 2.5× than that without considering priority for offline algorithm, and 3.7× for online algorithm

Disease burden of low back pain attributable to ergonomic risk factors in selected Chinese occupational groups

BackgroundAs traditional chemical and physical hazards as well as associated adverse health outcomes in workplace were wildly controlled in the past half century, the prevalence and disease burden of low back pain (LBP) have drawn more and more attention and become one of the important public health problems in the world. ObjectiveTo analyze the health loss and attributable disease burden of ergonomic risk factors for LBP in two major categories of occupations in China, aiming to provide evidence for formulating effective prevention and control policies of LBP in the workplace. MethodsBased on the methodological framework of the Global Burden of Disease Study (GBD), a meta-analysis was firstly applied to summarize relevant literature results and estimate the prevalence of LBP in two occupational groups (including technicians and associate professionals and machine operators and assemblers) by different age groups in China. Then important epidemiologic parameters (including disability weight, remission rate, and incidence) from GBD 2019 were used to estimate mean duration of disease and age at onset using DisMod II software, and to calculate health loss indexes in the selected occupational groups in China in 2013, such as years lived with disability (YLD) and disability-adjusted life year (DALY) of LBP and its attributable fractions by ergonomic risk factors, which were compared to the outcome of GBD 2013. ResultsAfter the adjustment by DisMod II, the prevalence rate of LBP was 13.00% in technicians and associate professionals (11.25% for males and 14.84% for females) and 14.80% in machine operators and assemblers (13.56% for males and 16.10% for females) in 2013, which increased with age. The DALY rate of LBP was 8.02‰ in technicians and associate professionals (7.68‰ for males and 8.33‰ for females) and 10.34‰ in machine operators and assemblers (10.30‰ for males and 10.44‰ for females), which also showed an overall increasing trend with age. In 2013, the population attributable fraction (PAF) of ergonomic risk factors to LBP was 11.42% in technicians and associate professionals and 29.17% in machine operators and assemblers. The DALY of LBP attributable to ergonomics risk factors was 4498 person-years (2108 person-years for males), with the highest DALY in the 45-49 year group (951 person-years), and the attributable DALY rate was 0.92‰ in technicians and associate professionals. The DALY of LBP attributable to ergonomics risk factors was 48529 person-years (33046 person-years for males), with the highest DALY in the 40-44 year group (10852 person-years), and the attributable DALY rate was 3.02‰ in machine operators and assemblers. Regarding LBP-associated DALY rate, in the 20 years of age and above group, both occupational groups (technicians and associate professionals: 8.06‰, machine operators and assemblers: 10.66‰) showed higher values than the general population (3.55‰). In the 20 years of age and above group, the DALY rates attributable to ergonomic risk factors with the order from high to low were machine operators and assemblers (3.11‰), general population (1.10‰) and technicians and associate professionals (0.92‰).ConclusionThe LBP-associated disease burden is heavier in the two Chinese occupational groups than in general population. Reducing the disease burden of LBP by interventions targeting ergonomic risk factors in machine operators and assemblers is more effective than that in technicians and associate professionals as the results of attributable burden of disease suggest

Task number maximization offloading strategy seamlessly adapted to UAV scenario

Mobile edge computing (MEC) has been proposed in recent years to process resource-intensive and delay-sensitive applications at the edge of mobile networks, which can break the hardware limitations and resource constraints at user equipment (UE). In order to fully use the MEC server resource, how to maximize the number of offloaded tasks is meaningful especially for crowded place or disaster area. In this paper, an optimal partial offloading scheme POSMU (Partial Offloading Strategy Maximizing the User task number) is proposed to obtain the optimal offloading ratio, local computing frequency, transmission power and MEC server computing frequency for each UE. The problem is formulated as a mixed integer nonlinear programming problem (MINLP), which is NP-hard and challenging to solve. As such, we convert the problem into multiple nonlinear programming problems (NLPs) and propose an efficient algorithm to solve them by applying the block coordinate descent (BCD) as well as convex optimization techniques. Besides, we can seamlessly apply POSMU to UAV (Unmanned Aerial Vehicle) enabled MEC system by analyzing the 3D communication model. The optimality of POSMU is illustrated in numerical results, and POSMU can approximately maximize the number of offloaded tasks compared to other schemes

Special issue on communication and computation cooperation (3C): Principles, algorithms and systems

Editorial piece for the Special Issue of the International Journal of Communication Systems