Why Will Computing Power Need Particular Attention in Future Wireless Devices?

Mämmelä, Aarne; Anttonen, Antti

doi:10.1109/mcas.2016.2642679

Cited by 50 publications

(39 citation statements)

References 80 publications

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“…configured according to [51], [53] and [55]. For clarity, the detail parameter settings are provided in Table I 2 , which did not change in the sequel unless otherwise specified.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…That is, more bits are required to be processed, more computation should be performed by local computing. According to [50] and [55], the value of K depends on the specific algorithms, but at high bit rate, the computing operation with linear complexity is often expected to reduce the computational complexity and power consumption. Hence, similar to [46], [50], we also adopt the linear model in (9) to characterize the local computing energy consumption in this paper.…”

Section: System Modelmentioning

confidence: 99%

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Fog-Assisted Multiuser SWIPT Networks: Local Computing or Offloading

Zheng

Xiong

Fan

et al. 2019

IEEE Internet Things J.

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This paper investigates a fog computing-assisted multi-user simultaneous wireless information and power transfer (SWIPT) network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors' required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multi-user scheduling, the time assignment, the sensors' transmit powers and the PS ratios. Since the problem is a mixed integer programming (MIP) problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions and theoretical analysis. For a scheduled sensor, the closed-form and semi-closedform solutions to achieve its minimal required energy are derived, and then an efficient multi-user scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server (FS), the fog offloading mode is the better choice; otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors.

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“…configured according to [51], [53] and [55]. For clarity, the detail parameter settings are provided in Table I 2 , which did not change in the sequel unless otherwise specified.…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Fog-Assisted Multiuser SWIPT Networks: Local Computing or Offloading

Zheng

Xiong

Fan

et al. 2019

IEEE Internet Things J.

View full text Add to dashboard Cite

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“…It is desirable to vastly increase the EE in 5G, but IMT-2020 provides no measurable targets for it, but claims that higher spectral efficiency will be sufficient. There are two main ways to improve the spectral efficiency: smaller cells [6], [7] This paper was supported by ELLIIT and grants from the Swedish Foundation for Strategic Research (SSF) and the Swedish Research Council (VR). and massive multiple-input multiple-output (MIMO) [8], [9].…”

Section: Introductionmentioning

confidence: 99%

“…The bandwidth is fixed in these prior works, but many other parameters are optimized for maximum EE. There are other non-trivial tradeoffs, such as the fact that transceiver hardware becomes more efficient with time [6], [11], so the energy consumption of a given network topology gradually reduces.…”

Section: Introductionmentioning

confidence: 99%

How Energy-Efficient Can a Wireless Communication System Become?

Björnson

Larsson

2018

2018 52nd Asilomar Conference on Signals, Systems, and Computers

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The data traffic in wireless networks is steadily growing. The long-term trend follows Cooper's law, where the traffic is doubled every two-and-a-half year, and it will likely continue for decades to come. The data transmission is tightly connected with the energy consumption in the power amplifiers, transceiver hardware, and baseband processing. The relation is captured by the energy efficiency metric, measured in bit/Joule, which describes how much energy is consumed per correctly received information bit. While the data rate is fundamentally limited by the channel capacity, there is currently no clear understanding of how energy-efficient a communication system can become. Current research papers typically present values on the order of 10 Mbit/Joule, while previous network generations seem to operate at energy efficiencies on the order of 10 kbit/Joule. Is this roughly as energy-efficient future systems (5G and beyond) can become, or are we still far from the physical limits? These questions are answered in this paper. We analyze a different cases representing potential future deployment and hardware characteristics.

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“…Related challenges need to be considered carefully both at the base station (Ge, Yang, Gharavi, & Sun, 2017) and mobile terminal side (Mämmelä & Anttonen, 2017).…”

Section: Energy Consumption Trends Related Radio Network Technologiesmentioning

confidence: 99%

Energy consumption of mobile data transfer – Increasing or decreasing? Evaluating the impact of technology development & user behavior

Pihkola¹,

Hongisto²,

Apilo³

et al.

EPiC Series in Computing

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Mobile data consumption in Finland is among the highest in the world. Increase in mobile data usage has been rapid and continuous growth is foreseen. While the energy consumed per transmitted gigabyte has substantially decreased, it seems that the absolute annual energy consumption related to mobile operators' activities has started to increase. Simultaneously, consumer behavior is changing. While new end-user devices are more and more energy-efficient, we use more and more time with mobile devices. Is increasing usage outweighing achieved energy savings? What kinds of options are available for tackling increasing energy demand? This paper discusses current and future trends related to energy consumption of mobile data transfer and mobile networks in Finland. Using a top-down approach and publicly available data, an illustrative trend (kWh/gigabyte) for the energy consumption of transmitted mobile data was constructed for the years 2010-2016. In addition, energy consumption related to mobile data transfer is discussed from a life cycle perspective, considering both direct and indirect energy use and challenges in conducting such assessments. Contributions of relevant technological and social developments (radio network technology transformations from 4G to 5G and consumer behavior) are analyzed considering possible trade-offs and pointing out aspects that require future studies.

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Why Will Computing Power Need Particular Attention in Future Wireless Devices?

Cited by 50 publications

References 80 publications

Fog-Assisted Multiuser SWIPT Networks: Local Computing or Offloading

Fog-Assisted Multiuser SWIPT Networks: Local Computing or Offloading

How Energy-Efficient Can a Wireless Communication System Become?

Energy consumption of mobile data transfer – Increasing or decreasing? Evaluating the impact of technology development & user behavior

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