One-Power-Zone Constraint for Wireless Backhaul Interference
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Solution Overview
Problem
Existing wireless backhaul networks face challenges in efficiently managing interference due to high computational complexity and slow convergence in joint power control and scheduling, particularly in densely deployed networks with Non Line of Sight (NLOS) technology, where conventional maximum power transmission strategies are not optimal.
Innovation Solution
The implementation of a one-power-zone (OPZ) constraint, where each hub maintains the same power level across all zones in a transmit frame, decouples power optimization from scheduling, allowing for independent power control and reducing implementation complexity, using methods like iterative function evaluation and Newton's method for optimizing weighted-sum rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If joint power control and scheduling optimization is implemented, then network performance improves, but computational complexity increases significantly
Solution Approach 1:
The patent segments the joint power control and scheduling problem into two separate sub-problems: power control optimization and scheduling optimization. By decoupling these problems, each can be solved independently with reduced computational complexity. The power control module optimizes transmit power levels while the scheduling module handles resource allocation separately, avoiding the exponential complexity of joint optimization.
Solution Approach 2:
The patent extracts the power control component from the joint optimization problem and treats it as a separate entity. The power control module is extracted to independently manage power allocation based on channel conditions and interference levels, while the scheduling module handles resource allocation. This extraction reduces the overall computational burden.
2Measurement precision
If iterative optimization methods are used for joint power control and scheduling, then solution accuracy improves, but convergence time increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating channel state information and interference metrics before the actual optimization process. The power control module uses pre-computed channel gains and interference levels to make rapid power adjustment decisions, reducing the need for extensive iterative computations and accelerating convergence.
3Power
If maximum power transmission strategy is used, then signal strength improves, but interference increases
Solution Approach 1:
The patent dynamically changes the transmit power parameter based on channel conditions, interference levels, and quality of service requirements. Instead of using fixed maximum power, the power control module adjusts transmit power levels in real-time, reducing power when interference is high and increasing it when channel conditions are good, thereby optimizing the trade-off between signal strength and interference.
Solution Approach 2:
The patent implements feedback mechanisms where the power control module continuously monitors channel state information, interference levels, and received signal quality. Based on this feedback, the system adjusts transmit power levels to maintain optimal performance while minimizing interference. The feedback loop enables adaptive power control that responds to changing network conditions.
Data Source
AI summary
Methods and apparatus are provided for managing interference in a wireless backhaul network comprising a plurality of hubs, each hub serving a plurality of remote backhaul modules (RBM), using power control with a one-power-zone (OPZ) constraint. Each hub uses a transmit frame structure comprising a plurality of zones, each RBM is scheduled on a different zone, and the same power level is maintained across all zones within a transmit frame. Under the OPZ constraint, and for scheduling policies under which the number of zones assigned to each RBM is fixed, the power and scheduling sub-problems are decoupled. This enables power control independent of scheduling, using methods having lower computational complexity. Methods are disclosed comprising iterative function evaluation or Newton's method approaches based on a weighted sum-rate maximization across the network, which can be implemented in a distributed fashion. Some of the methods can be implemented asynchronously at each hub.