Adaptive Modulation for Energy Harvesting Relay Systems
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Solution Overview
Problem
Energy harvesting relay systems face challenges in determining optimal modulation modes due to limited energy and unpredictable channel conditions, leading to difficulties in maximizing system throughput.
Innovation Solution
An adaptive modulation method using a naive Bayes classifier algorithm to determine modulation modes based on current channel and energy storage status, allowing the relay to dynamically adjust modulation orders and power allocation to optimize throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the relay uses higher-order modulation modes to increase system throughput, then the data transmission rate improves, but the energy consumption increases and reliability decreases due to channel uncertainties
Solution Approach 1:
The patent implements dynamic modulation mode selection where the relay adapts its modulation order based on real-time channel conditions and energy storage status. The system transitions from static to dynamic operation by continuously monitoring channel quality indicators and adjusting modulation parameters accordingly, allowing optimal balance between throughput and reliability under varying conditions.
Solution Approach 2:
The system changes modulation parameters (modulation order, power allocation) based on observed channel conditions and energy status. By adjusting these parameters dynamically rather than using fixed settings, the system optimizes the trade-off between transmission rate and reliability, selecting higher-order modulations when conditions permit and lower-order modulations when reliability is compromised.
2Use of energy by moving object
If the relay stores energy for future transmission, then the energy availability for high-rate transmission improves, but the system cannot respond to unpredictable channel status changes
Solution Approach 1:
The relay performs self-service by autonomously monitoring its own energy storage status and channel conditions, then making independent decisions about whether to transmit stored energy or continue harvesting. This self-service mechanism enables the system to balance energy storage and transmission adaptively without external control, responding dynamically to changing conditions.
Solution Approach 2:
The system implements feedback loops where the relay continuously monitors channel quality metrics and energy storage levels, then uses this feedback information to adjust its transmission strategy. The feedback mechanism allows the system to adapt to unpredictable channel changes by comparing actual conditions against thresholds and modifying behavior accordingly.
3Productivity
If the source node transmits at high data rates, then the system throughput increases, but the relay buffer overflow risk increases due to limited storage capacity
Solution Approach 1:
The system takes preliminary action by having the source node adjust its transmission rate based on the relay's current buffer status and historical throughput capability. Before transmitting large volumes of data, the source checks whether the relay can handle the incoming traffic, preventing buffer overflow by coordinating transmission rates with relay processing capacity.
Solution Approach 2:
The source relay transmission rate is made dynamic rather than fixed. The source node continuously adjusts its transmission parameters based on feedback about relay buffer status and channel conditions, enabling the system to maintain high throughput when possible while preventing buffer overflow through adaptive rate control.
Data Source
AI summary
The present disclosure provides an adaptive modulation method for a naive Bayes classifier-based energy harvesting relay system. First, a sending end determines its modulation mode based on a status of a data buffer of a relay, so that sent data does not exceed a storage capability of the relay. Then, based on data sent by the sending end, and channel status information and energy harvesting status information within a period of time, the relay determines an optimal modulation mode used by the relay within the period of time. After that, the modulation mode, the channel status information, and the energy harvesting status information are used as training data to obtain a classification model based on a naive Bayes classifier algorithm. In this way, the relay can adaptively select a modulation mode when only knowing of channel information and energy information in a current timeslot.