Distributed Spectrum Access System Architecture for Scalable Interference Management
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Solution Overview
Problem
The existing spectrum allocation and management systems are unable to efficiently and reliably allocate shared spectrum resources to a large number of secondary users while protecting primary users from interference, especially in scenarios with millions of users, and require scalable and low-latency solutions to manage dynamic spectrum access effectively.
Innovation Solution
A cloud-based distributed computing architecture is employed, using a Radio Environment Map (REM) for precise interference estimation and statistical calculations to manage spectrum access, with distributed block entities handling primary user protection and secondary user optimization, ensuring minimal latency and high scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a traditional centralized Spectrum Access System (SAS) is used to manage spectrum access, then spectrum allocation can be controlled, but the system cannot scale to handle millions of users and experiences high latency
Solution Approach 1:
The centralized SAS is segmented into multiple distributed block entities, each responsible for a specific geographic region or user subset. This segmentation enables parallel processing of spectrum access requests, reducing latency while maintaining comprehensive spectrum management coverage across the entire network
Solution Approach 2:
The system transitions from a single centralized dimension to a multi-dimensional distributed architecture where block entities are distributed across different geographic locations and network domains. This dimensional expansion enables simultaneous local decision-making while maintaining global coordination
2Reliability
If a centralized SAS processes all spectrum requests, then unified spectrum policy can be enforced, but the computational complexity and processing time increase significantly with millions of users
Solution Approach 1:
Spectrum policy enforcement is segmented into local policy rules executed by individual block entities and global coordination functions. This allows most policy decisions to be made locally and instantly, while only coordination-related communications occur between block entities, dramatically reducing processing time
Solution Approach 2:
Block entities pre-cache spectrum policy rules, interference models, and radio environment map data in their local memory. This preliminary action eliminates the need to fetch this data from a central server during real-time spectrum requests, reducing latency while maintaining policy enforcement integrity
3Measurement precision
If the SAS maintains a comprehensive Radio Environment Map (REM) for all users, then interference can be accurately predicted, but the memory requirements and data processing burden become unmanageable at scale
Solution Approach 1:
The comprehensive REM is segmented into distributed regional REMs, with each block entity maintaining only the radio environment data relevant to its local region. This segmentation maintains interference prediction accuracy for local users while reducing overall data storage requirements through spatial partitioning
Solution Approach 2:
Each block entity optimizes its local REM with high-precision data for its specific geographic region, while using coarser or aggregated data for distant regions. This local quality approach ensures accurate interference prediction for relevant users while minimizing redundant data storage
4Productivity
If a distributed block entity architecture is implemented, then system scalability and latency are improved, but the complexity of coordinating between blocks increases
Solution Approach 1:
A lightweight coordination mechanism acts as an intermediary between block entities, enabling them to exchange only essential information (spectrum assignments, interference reports, policy updates) without requiring complex peer-to-peer communication protocols. This intermediary layer abstracts the coordination complexity
Data Source
AI summary
Cloud-based systems and methods are provided for assigning shared spectrum resources to secondary users. The methods may comprise receiving, by a central block entity, a request for channel availability from a secondary user to access a shared spectrum in a wireless network, authenticating the secondary user, validating the secondary user, routing the request for channel availability to a managing block entity, receiving available frequency information from the managing block entity, assigning a frequency and an effective isotropic radiated power (EIRP) to the secondary user based on the available frequency information from the managing block entity, and transmitting, to the secondary user, a channel availability response based on the assigned EIRP. The managing block entity may be configured to determine available frequency information based on one or more parameters associated with the secondary user. The channel availability response may comprise a frequency on which the secondary user can transmit.