NEMOx Clustered Network MIMO for Scalable Capacity
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Solution Overview
Problem
Existing network MIMO systems are limited to small-scale networks due to stringent synchronization requirements and overhead in sharing data signals between access points, making it impractical to scale to large networks while maintaining performance and feasibility.
Innovation Solution
The NEMOx architecture organizes networks into clusters with fully synchronized remote antennas and access points, using a decentralized channel-access algorithm to manage inter-cluster interference and optimize power allocation and client selection within clusters, allowing for scalable capacity gains without the need for coordination between clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network MIMO systems use tight coordination between access points to serve multiple users concurrently, then system capacity is enhanced, but device complexity and synchronization requirements increase
Solution Approach 1:
The patent divides the network into multiple clusters, each managed independently by a cluster head. This segmentation allows each cluster to operate autonomously with local coordination, reducing the overall complexity of inter-access-point coordination while maintaining the ability to serve multiple users concurrently within each cluster.
Solution Approach 2:
The patent introduces a hierarchical dimension to the network architecture by organizing access points into clusters with designated cluster heads. This adds a management layer that handles coordination locally, reducing the complexity of direct peer-to-peer coordination between all access points while still enabling multi-user service capability.
2Productivity
If data signals are shared between all access points for netMIMO operation, then multi-user MIMO performance is achieved, but overhead and loss of information increase
Solution Approach 1:
The patent segments the data sharing requirement by limiting signal exchange to only within clusters rather than across the entire network. Each cluster head manages data sharing locally, reducing the overhead and information loss associated with network-wide signal distribution while maintaining multi-user MIMO performance within each cluster.
Solution Approach 2:
The patent implements local data sharing within clusters rather than uniform network-wide sharing. Each cluster operates with its own data exchange mechanisms, allowing optimized local coordination that reduces overall overhead and information loss while maintaining the necessary performance for multi-user service.
3Area of stationary object
If network MIMO scales to large networks, then coverage and capacity increase, but synchronization difficulty and overhead increase
Solution Approach 1:
The patent divides large networks into multiple independent clusters, each with its own synchronization domain. This allows the network to scale geographically while maintaining manageable synchronization overhead within each cluster, as cluster heads handle local synchronization independently without requiring coordination across the entire network.
Solution Approach 2:
The patent adds a hierarchical management dimension to scale the network efficiently. By organizing access points into clusters with designated heads, the system can expand to large geographic areas while maintaining synchronized operation through localized cluster management rather than requiring global synchronization coordination.
Data Source
AI summary
Systems and methods for system for channel access adaptation are disclosed. One system includes a plurality of remote antennas and a plurality of access points. The remote antennas transmit data to receivers and obtain channel state information. Additionally, each access point controls a different cluster of the remote antennas and receives the respective channel state information from the remote antennas of the cluster. Further, each access point is configured to, independently from other access points, adapt channel allocations to the remote antennas of the respective cluster based on a tracking of sums of collision loss probabilities. Each given sum is determined by the access point for a different given set of a plurality of sets of cooperating remote antennas in the respective cluster, where each constituent collision loss probability in the given sum is determined by the access point from a different interference clique to which the given set belongs.