Grant-Free Uplink Resource Selection for Low-Collision M2M Access
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Solution Overview
Problem
Existing grant-free access mechanisms in massive machine type communication (mMTC) face challenges such as high collision probabilities due to random resource block selection by devices, leading to inefficient power consumption and resource utilization, and lack of adaptive policies for diverse throughput, latency, and power saving requirements.
Innovation Solution
A multi-agent reinforcement learning framework is employed to model PHY and MAC dynamics, enabling devices to select optimal transmit power, physical resource blocks, and modulation and coding schemes dynamically, using partially observable stochastic games to minimize power consumption while meeting delay constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If grant-free random access is used to reduce signaling overhead, then energy consumption is reduced, but collision probability increases
Solution Approach 1:
The patent implements dynamic resource selection where devices adaptively choose time-frequency resources based on real-time channel conditions and traffic patterns rather than using fixed random access. This dynamic approach reduces collisions while maintaining the energy efficiency of grant-free access.
Solution Approach 2:
The system employs feedback mechanisms where devices receive information about successful transmissions and collisions, allowing them to adjust their resource selection strategy. This feedback loop enables the system to reduce collision probability while maintaining low energy consumption through intelligent resource allocation.
2Device complexity
If static resource allocation is used to simplify system complexity, then device complexity is reduced, but adaptability to diverse QoS requirements deteriorates
Solution Approach 1:
The patent segments the system into multiple independent components: physical layer parameters (modulation, coding), MAC layer parameters (resource selection, timing), and QoS parameters. Each segment can be independently optimized without increasing overall system complexity, enabling adaptation to diverse QoS requirements.
Solution Approach 2:
Different devices are assigned different resource configurations and parameters tailored to their specific QoS requirements. This local optimization allows each device to operate with complexity appropriate to its needs while the overall system supports diverse service types.
3Speed
If aggressive transmission strategies are used to reduce latency, then transmission speed is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts transmission parameters including modulation order, coding rate, and transmit power based on channel conditions and QoS requirements. This allows the system to achieve high transmission speeds when needed while consuming minimal energy during low-priority transmissions.
Solution Approach 2:
The patent implements periodic monitoring of channel conditions and traffic patterns, allowing devices to switch between aggressive transmission modes and energy-saving modes based on current system state, thus balancing latency and energy consumption.
Data Source
AI summary
The disclosure relates to a method, device and non-transitory computer readable media or selecting a transmit power, a physical resource block (PRB), and a modulation and coding scheme (MCS) for a grant free uplink transmission. The method comprises obtaining an observation of the radio environment of the device. The method comprises selecting an action, based on the observation, for execution during a next time slot, the action comprising selecting the transmit power, the PRB, and the MCS for the grant free uplink transmission.