Mobile Device Target Positioning for IBFD Cross-Channel Interference
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Solution Overview
Problem
In-band full-duplex (IBFD) technology faces challenges with self-interference (SI) and cross-channel interference (CCI) in 4G and 5G base stations and user equipment, which limits throughput and increases size, cost, and power consumption, especially for mobile devices like UAVs.
Innovation Solution
An apparatus and method that estimate future communication states and determine target positions for mobile devices to minimize CCI by repositioning them based on estimated path loss and throughput optimization, using a probabilistic approach to maximize average throughput while reducing interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If In-band full-duplex (IBFD) technology is used to improve throughput, then data transmission efficiency is improved, but self-interference and cross-channel interference increase
Solution Approach 1:
The system performs preliminary estimation of future communication states and determines target positions in advance before the actual communication occurs. By predicting future states and pre-calculating optimal positions, the system can proactively mitigate interference issues before they affect throughput performance.
Solution Approach 2:
The system dynamically adjusts mobile device positions based on estimated future communication states. Rather than using static positioning, the target positions are determined dynamically by considering probabilistic future states, path loss, and interference conditions, allowing the system to adapt to changing conditions while maintaining IBFD benefits.
2Object-generated harmful factors
If mobile devices are repositioned to reduce cross-channel interference, then interference is reduced, but device mobility and complexity increase
Solution Approach 1:
The base station acts as an intermediary that performs the complex calculations for determining target positions. Rather than requiring each mobile device to independently calculate and manage positioning, the base station centralizes the computational complexity, using received communication state information to determine optimal positions for all devices.
Solution Approach 2:
The system uses probabilistic models and estimated parameters to create simplified representations of future communication states. By working with probability distributions and estimated path loss values rather than deterministic exact values, the system reduces computational complexity while maintaining effective interference mitigation.
3Productivity
If future communication states are estimated using probabilistic methods, then throughput optimization is improved, but computational complexity increases
Solution Approach 1:
The system changes the parameters used for throughput optimization from deterministic exact values to probabilistic estimated values. By using expected values and probability distributions for future communication states, the system can perform optimization with reduced computational requirements compared to exhaustive probabilistic analysis, while still achieving improved average throughput.
Data Source
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AI summary
An apparatus and method is disclosed, in which the apparatus comprising means for: estimating or determining a future communications state for each of a plurality a mobile devices K associated with a base station, the future communications state being indicative of the mobile device communicating in an uplink to a base station or in a downlink from the base station at a future time slot t or being idle and also determining a target position of at least one of the mobile devices, based at least partly on its estimated communication state, for enabling re-positioning of said at least one mobile device to the determined target position substantially at said future time slot t.