Dynamic Feedback-Based Adaptive Modulation for Energy-Efficient Communication

Mukherjee, Priyadarshi; De, Swades

doi:10.1109/lcomm.2019.2907622

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…In 5G systems, the two kinds of feedback mode are periodic and aperiodic feedback [ 3 ]. Some researchers have mathematically analyzed the feedback interval [ 7 , 8 , 9 ]. The authors of [ 7 ] mathematically analyzed the optimal feedback interval that maximizes the average received power.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [ 8 ] mathematically analyzed the feedback interval that maximizes the bidirectional transmission throughput. The authors of [ 9 ] calculated the CSI-dependent interval and used adaptive modulation to obtain higher energy efficiency and throughput. However, the work of [ 7 , 8 , 9 ] focused on finding intervals of periodic rather than aperiodic feedback.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [ 9 ] calculated the CSI-dependent interval and used adaptive modulation to obtain higher energy efficiency and throughput. However, the work of [ 7 , 8 , 9 ] focused on finding intervals of periodic rather than aperiodic feedback. Hence, there is a limit to reduce the number of feedback transmissions.…”

Section: Introductionmentioning

confidence: 99%

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Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices

Han

Kim

2023

Sensors

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As the number of Internet of things (IoT) devices increases exponentially, scheduling and managing the radio resources for IoT devices has become more important. To efficiently allocate radio resources, the base station (BS) needs the channel state information (CSI) of devices every time. Hence, each device needs to periodically (or aperiodically) report its channel quality indicator (CQI) to the BS. The BS determines the modulation and coding scheme (MCS) based on the CQI reported by the IoT device. However, the more a device reports its CQI, the more the feedback overhead increases. In this paper, we propose a long short-term memory (LSTM)-based CQI feedback scheme, where the IoT device aperiodically reports its CQI relying on an LSTM-based channel prediction. Additionally, because the memory capacity of IoT devices is generally small, the complexity of the machine learning model must be reduced. Hence, we propose a lightweight LSTM model to reduce the complexity. The simulation results show that the proposed lightweight LSTM-based CSI scheme dramatically reduces the feedback overhead compared with that of the existing periodic feedback scheme. Moreover, the proposed lightweight LSTM model significantly reduces the complexity without sacrificing performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices

Han

Kim

2023

Sensors

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“…An energy-efficient adaptive modulation and power control scheme was proposed for wireless sensor networks in [7], where the sensor node changes its transmit power and modulation scheme in adaptation to the signal-to-noise-ratio (SNR) and target BER. In [8], the EE of a point-to-point link was improved by proposing a dynamic feedback-based adaptive modulation scheme, where the channel state information (CSI) is learned from the receiver feedback per time slot.…”

Section: Introductionmentioning

confidence: 99%

“…In [6]- [8], the circuit power was assumed to be negligible. Capturing non-negligible circuit power in analysis, an energy-efficient data rate for a target BER was obtained in a closed form regarding to the constellation size, distance and bandwidth [9].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Adaptive Modulation and Data Schedule for Delay-Sensitive Wireless Communications

et al. 2020

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In this paper, targeting at improving the energy efficiency (EE) for Quality-of-Service (QoS)guaranteed wireless communications, we develop new adaptive modulation and data scheduling algorithms for delay-sensitive bursty data. Assuming a-priori knowledge on data arrivals and latency requirements, the problem is formulated as a mix-integer programming that minimizes the total energy consumption at the transmitter with a non-linear Doherty power amplifier (PA) and non-negligible circuit power. According to the different properties of the PA in different output power regions, we decouple the formulated problem and solve it in two stages. In the first stage, assuming the PA has a linear efficiency, we develop an optimal modulation and data scheduling scheme (MDS) relying on convex relaxation and the resultant optimality conditions. The MDS is able to reveal the specific structure of the optimal policy in a computationally efficient and graphical manner. On top of that, a heuristic MDS scheme (HMDS) is proposed to adjust the MDS when the PA works in the non-linear region in the second stage, where a quadratic function is obtained to approximate the non-linear PA model. The offline HMDS algorithm is further extended to practical online scenarios in a well-structured way, where the modulation and data scheduling policy is produced on-the-fly. Simulation corroborates that the proposed offline algorithm can achieve the exactly same performance as the standard CVX solver, while requiring only 0.69% of its computational time. INDEX TERMS Energy efficiency (EE), adaptive modulation, quality of service (QoS), circuit power, non-linear power amplifier (PA).

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Machine learning-based adaptive CSI feedback interval

Hong

2022

ICT Express

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Dynamic Feedback-Based Adaptive Modulation for Energy-Efficient Communication

Cited by 9 publications

References 11 publications

Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices

Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices

Energy-Efficient Adaptive Modulation and Data Schedule for Delay-Sensitive Wireless Communications

Machine learning-based adaptive CSI feedback interval

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