mmWave Network Scheduler for Interference Reduction
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Solution Overview
Problem
Millimeter-Wave (mmWave) networks face interference due to irregular beam patterns, which affect the efficient routing of packets between User Equipments (UEs) and Access Points (APs), leading to suboptimal data transmission.
Innovation Solution
A scheduler system that uses Received Signal Strength (RSS) information to instruct routers on packet routing and APs on data transmission, employing an iterative process to select the most suitable UE and beam for each AP, optimizing the weighted sum rate through the Shannon capacity formula and weight assignment to ensure fairness and minimize interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mmWave networks use multi-antenna phased array to concentrate signal energy into narrow beams, then high throughput is enabled, but interference between beams occurs due to irregular beam patterns
Solution Approach 1:
The system performs preliminary beam selection and scheduling before data transmission. The scheduler selects optimal beam-UE pairs and assigns time slots in advance, allowing the system to prepare beam configurations that minimize interference while maximizing throughput before actual transmission occurs.
Solution Approach 2:
The system dynamically adjusts beam selections and scheduling based on real-time channel conditions. The scheduler receives channel state information and reconfigures beam-UE assignments on-the-fly, allowing the system to adapt to changing interference conditions and maintain optimal performance.
2Productivity
If the scheduler selects UE and beam for each AP based on Shannon capacity formula, then data transmission efficiency is optimized, but system complexity increases due to iterative selection process
Solution Approach 1:
The scheduler uses feedback from channel state information to iteratively improve beam-UE selections. Channel measurements are fed back to the scheduler, which uses this information to adjust selections based on the Shannon capacity formula, creating a closed-loop optimization system.
Solution Approach 2:
The system changes scheduling parameters such as beam directions, time slots, and power levels to optimize transmission efficiency. The scheduler adjusts these parameters based on channel conditions and selected UE-AP pairs, using the Shannon capacity formula to guide parameter optimization.
3Object-generated harmful factors
If each AP transmits data to the most suitable UE using the strongest beam, then interference is minimized, but routing decisions become more complex requiring router instructions
Solution Approach 1:
The scheduler acts as an intermediary between routers and APs, centralizing the complex routing decisions. Instead of requiring complex local intelligence at each node, the scheduler collects channel information and makes centralized beam-UE assignments, simplifying the overall system architecture.
Solution Approach 2:
The system segments the scheduling function into separate time slots and beam assignments. By dividing the transmission timeline into discrete slots and assigning specific beams to specific UE-AP pairs within each slot, the system simplifies the routing control while maintaining optimal interference avoidance.
Data Source
AI summary
Embodiments herein disclose a system and methods for scheduling in mmWave networks. Embodiments herein disclose a system and methods for scheduling in mmWave networks, wherein the router is provided with instructions on how to route packets requested by User Equipments (UEs) to the respective Access Points (APs). Embodiments herein disclose a system and methods for scheduling in mmWave networks, wherein each AP is provided with instructions on which UE the AP has to transmit data to and using which beam.