Self-Learning RF Receiver Systems: Process Aware Real-Time Adaptation to Channel Conditions for Low Power Operation

Banerjee, Debashis; Muldrey, Barry; Xian, Wang; Sen, Shreyas; Chatterjee, Abhijit

doi:10.1109/tcsi.2016.2608962

Cited by 24 publications

(13 citation statements)

References 45 publications

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“…Most energy-efficient techniques are leveraging network intelligence to achieve a more efficient result. With a lot of cognitive network-based EE applications proposed in literature, artificial intelligence is expected to play a crucial role in EE for future networks [74], including for efficient adaptive resource allocation, discontinuous reception [75], channel learning for power management [76,77], traffic offloading for energy efficiency in small cells [78], node device authentication for security [79], and intermittent energy management for energy-harvested applications [80,81]. We present a list of prediction-based techniques for WCNs in Table 3.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

Ogbebor

Imoize

Atayero

2020

Wireless Communications and Mobile Computing

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The projected rise in wireless communication traffic has necessitated the advancement of energy-efficient (EE) techniques for the design of wireless communication systems, given the high operating costs of conventional wireless cellular networks, and the scarcity of energy resources in low-power applications. The objective of this paper is to examine the paradigm shifts in EE approaches in recent times by reviewing traditional approaches to EE, analyzing recent trends, and identifying future challenges and opportunities. Considering the current energy concerns, nodes in emerging wireless networks range from limited-energy nodes (LENs) to high-energy nodes (HENs) with entirely different constraints in either case. In view of these extremes, this paper examines the principles behind energy-efficient wireless communication network design. We then present a broad taxonomy that tracks the areas of impact of these techniques in the network. We specifically discuss the preponderance of prediction-based energy-efficient techniques and their limits, and then discuss the trends in renewable energy supply systems for future networks. Finally, we recommend more context-specific energy-efficient research efforts and cross-vendor collaborations to push the frontiers of energy efficiency in the design of wireless communication networks.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

Ogbebor

Imoize

Atayero

2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Depending on the complexity of the system, this network can process information by adjusting the interconnection between several internal node. An ANN has the ability of self-adaptation and self-learning, and is more accurate than the conventional linear relation model [ 25 ].…”

Section: Design Of the Neural Network Modelmentioning

confidence: 99%

Design of Plant Protection UAV Variable Spray System Based on Neural Networks

Wen

Zhang

Yin

et al. 2019

Sensors

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Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized.

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“…In particular, these works do not consider the impact of RF circuits consumption on multiple antenna systems. Furthermore, the approach in [29] tries to search for the most optimized setup at the circuit level, aiming to adapt circuit parameters in real-time in order to reduce the power consumption. However, only the LNA design is addressed in the consumption analysis.…”

Section: A Related Workmentioning

confidence: 99%

Energy Efficiency in Multiple Antenna Machine-Type Communications With Reconfigurable RF Transceivers

et al. 2019

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In machine-type communication (MTC) networks, each node is equipped with a set of radiofrequency (RF) transceivers, antennas and power source. Besides, wireless nodes are typically batterypowered, whose recharging or replacement is often not viable or even impossible. Therefore, in such systems where energy is at a premium, minimizing power consumption improves energy efficiency and increases battery lifetime. In this paper, a novel energy-saving approach includes in the communication system model the use of reconfigurable RF transceivers. More specifically, the components involved in our power minimization framework are the power amplifier (PA) at the transmitter and the low noise amplifier (LNA) at the receiver, which are the blocks responsible for RF amplification and are on top of power consuming blocks in RF transceivers. Our goal is to show that RF circuits based on multimode operation can significantly improve the energy efficiency. We perform a joint selection of the best operating modes for PA and LNA circuits in different communication scenarios. Results show that by combining PA and LNA operating modes, an improvement of more than 75% in energy efficiency is obtained for multiple antenna communications, when compared to the state-of-the-art literature of non-reconfigurable amplifiers. Besides, we show that adapting spectral efficiency contributes towards improving energy efficiency. In this case, we consider different spectral efficiency values, including the effect that the PA operates at different backed-off power levels. In addition, when comparing single-input single-output (SISO) and multiple-input multiple-output (MIMO) transmission schemes, antenna selection (AS) outperforms the others schemes in terms of energy efficiency for short and moderate distances, but singular value decomposition (SVD) allows for longer communication distances. INDEX TERMS Energy efficiency, reconfigurable RF transceivers, machine-type communications, multiple antennas.

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Self-Learning RF Receiver Systems: Process Aware Real-Time Adaptation to Channel Conditions for Low Power Operation

Cited by 24 publications

References 45 publications

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

Design of Plant Protection UAV Variable Spray System Based on Neural Networks

Energy Efficiency in Multiple Antenna Machine-Type Communications With Reconfigurable RF Transceivers

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