Distributed Cellular Handover Agents for Fair Resource Allocation
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Solution Overview
Problem
Current cellular network management systems fail to ensure fair resource allocation across socio-economic segments, leading to unequal quality of service and potential biases against underprivileged groups, as they often prioritize 'mainstream' patterns and lack effective multi-objective optimization and adaptation to changing preferences.
Innovation Solution
A distributed multi-agent reinforcement learning (MARL) system is deployed in cellular networks, where computing agents at base transceiver stations make handover decisions based on socio-demographic indicators and network state information to maximize device-level quality of experience and mitigate resource inequalities, using a learned policy trained on historical data to optimize user-cell associations and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional centralized resource allocation methods are used, then network control simplicity is maintained, but fairness across socio-economic segments deteriorates
Solution Approach 1:
The patent divides the centralized network control into multiple distributed computing agents deployed at base transceiver stations. Each agent independently makes handover decisions for user devices, segmenting the control function across multiple entities. This segmentation enables localized fairness optimization without requiring a complex centralized system to process all decisions, as each agent operates autonomously based on local socio-demographic data and network conditions.
2Productivity
If single-agent reinforcement learning is used, then decision-making simplicity is maintained, but scalability and latency performance deteriorate
Solution Approach 1:
The patent transitions from a single-agent reinforcement learning system to a multi-agent reinforcement learning system where each base transceiver station operates an independent computing agent. This segmentation enables parallel decision-making across the network, dramatically improving scalability and reducing latency. Each agent learns and executes handover decisions independently based on local observations, eliminating the centralized bottleneck while maintaining coordinated network-wide optimization through shared learning objectives.
3Productivity
If mainstream pattern prioritization is used, then network efficiency is improved, but fairness to underprivileged groups deteriorates
Solution Approach 1:
The patent applies local quality by incorporating socio-demographic indicators into the reinforcement learning objective function of each computing agent. Instead of uniform resource allocation or mainstream-pattern prioritization, each agent optimizes handover decisions locally based on the specific socio-economic characteristics of users in its coverage area. This enables differentiated treatment that actively compensates for historical disadvantages, ensuring that underprivileged groups receive appropriate resource allocation while maintaining overall network efficiency.
4Adaptability or versatility
If static handover policies are used, then system simplicity is maintained, but adaptability to changing user preferences and network conditions deteriorates
Solution Approach 1:
The patent implements dynamics by employing reinforcement learning algorithms that continuously adapt handover policies based on changing network conditions and user preferences. Each computing agent learns optimal handover strategies through interaction with the environment, adjusting its policy dynamically rather than following static rules. The system incorporates user feedback and socio-demographic data to evolve its decision-making, enabling adaptability to changing conditions while managing complexity through distributed autonomous learning at each base station.
Data Source
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AI summary
A method, distributed system, device and computer programs for efficient and fair control of cellular networks are provided. The cellular network is distributed in different cells where each cell comprises a base transceiver station (BS) providing network coverage to a plurality of computing devices, each BS having associated thereto a computing agent. At least one computing agent comprises receiving signal strength information from at least one given computing device connected to its associated BS, user and device information of the given computing device, and cell information from its neighboring cells; and determining, a handover decision, that seeks to maximize a device-level quality of experience and to mitigate systematic assignation of fewer network resources to underprivileged demographic users, for said given computing device by executing a reinforced learning decision-making algorithm on an established trained policy and on the received signal strength, user and device and cell information.