Adaptive Handover Threshold Control for Wireless Devices
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Solution Overview
Problem
Current handover control systems for multi-mode wireless devices rely on fixed threshold settings, which are suboptimal for switching between different networks, leading to inefficient handover decisions and increased costs for network operators and handset manufacturers, as they fail to adapt to varying signal conditions and network preferences.
Innovation Solution
A wireless communications device with a handover processor that adjusts threshold values based on the success or failure of handover attempts, using a signal quality assessment system to determine optimal settings dynamically, thereby optimizing handover decisions for specific equipment and usage scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed handover threshold is used, then the handover decision is simple and consistent, but the handover reliability deteriorates because the threshold cannot adapt to varying signal conditions and network preferences
Solution Approach 1:
The patent transforms the static fixed threshold into a dynamic adaptive threshold that automatically adjusts based on handover outcomes. The threshold setting unit modifies threshold values according to whether handovers are successful or fail, allowing the system to learn from past performance and optimize future decisions. This dynamic adjustment resolves the contradiction by maintaining operational simplicity while significantly improving reliability through automatic adaptation.
Solution Approach 2:
The patent implements a feedback mechanism where the outcome of each handover attempt (success or failure) is fed back into the threshold setting unit to adjust future threshold values. This closed-loop feedback system allows the handover controller to learn from previous decisions and refine its threshold settings, thereby improving handover reliability without complicating the decision-making process.
2Productivity
If the handover threshold is set to be conservative (higher threshold), then the frequency of handover attempts is reduced, but the handover success rate deteriorates because suitable handovers may be missed
Solution Approach 1:
The patent employs dynamic threshold adjustment where the threshold value automatically adapts based on observed handover outcomes. When handovers are successful, the threshold may be adjusted to be more aggressive (lower), increasing attempt frequency to capture more opportunities. When handovers fail, the threshold becomes more conservative (higher), reducing unnecessary attempts. This dynamic behavior resolves the contradiction by optimizing the balance between frequency and success rate in real-time.
Solution Approach 2:
The system performs self-optimization by automatically adjusting its own threshold settings based on handover performance without external intervention. The threshold setting unit monitors handover outcomes and autonomously modifies future threshold values, allowing the system to learn what works best in different conditions and self-regulate the balance between attempt frequency and success rate.
3Reliability
If the handover threshold is set to be aggressive (lower threshold), then the handover success rate improves by capturing more opportunities, but the frequency of failed handovers increases due to unnecessary attempts
Solution Approach 1:
The patent uses feedback from handover outcomes to adjust threshold settings. When aggressive thresholds lead to failed handovers, the feedback mechanism detects these failures and subsequently raises the threshold to be more conservative, thereby reducing unnecessary energy-consuming attempts. This feedback-driven adaptation resolves the contradiction by learning from past failures to avoid repeating them, optimizing the balance between success rate and energy efficiency.
Solution Approach 2:
The system autonomously optimizes its threshold behavior through self-service learning. The threshold setting unit monitors handover results and automatically adjusts future threshold values based on what produces successful handovers versus failed ones, enabling the system to self-regulate energy consumption while maintaining high success rates without requiring external optimization.
4Reliability
If multiple parameters are monitored and complex algorithms are used to determine handover decisions, then the handover reliability improves, but the device complexity increases
Solution Approach 1:
The patent extracts the complexity from the handover decision-making process by using a single primary parameter (signal strength) with dynamically adjusted thresholds rather than monitoring multiple parameters and using complex algorithms. The threshold setting unit handles the complexity of adaptation internally, allowing the main handover decision logic to remain simple and straightforward while still achieving high reliability through intelligent threshold management.
Data Source
AI summary
The signal strength available to a mobile unit is periodically monitored (21), and if it falls below a threshold value X(off) the handset (40) determines whether the signal strength on an alternative network is greater than a value Y(on). If such a connection is available, a handover attempt is initiated (3). If the handover attempt (3) fails (step 4), the relevant threshold value X(off) and Y(on), depending on the cause of failure, is then raised (40) so that future handoffs are not attempted in those same conditions. If the call fails in these circumstances, the caller will have to establish a new call (step 1). If the handover is successful, (step 5) then one or both of the handover thresholds (X(off), Y(on)) may be lowered (step 51), allowing future handovers to take place more promptly. Lowering may be done in smaller increments than increases, or less frequently. By varying the threshold values empirically more efficient handover can be achieved than by pre-setting a permanent value.