Massive MIMO Spectrum Sharing With Zero-Forcing Interference Protection
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Solution Overview
Problem
Existing spectrum sharing methods in massive MIMO networks fail to adequately protect the primary network from secondary network interference and vice versa, leading to degraded spectral efficiency and increased implementation costs, while requiring excessive cooperation and channel state information sharing.
Innovation Solution
Implement a modified zero-forcing beamformer and power allocation schemes at the secondary base station, utilizing a learning phase and reverse time-division duplexing to minimize interference and optimize data rates, without relying on perfect channel state information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional spectrum sharing methods are used, then the secondary network can access the licensed spectrum, but the primary network performance is degraded due to insufficient interference protection
Solution Approach 1:
The patent applies preliminary anti-action by having the secondary base station estimate the primary channel and pre-compute zero-forcing beamformers that will nullify primary user interference before the actual communication occurs. The secondary base station estimates the primary channel during a learning phase and uses this information to design beamformers that automatically protect against primary network interference, eliminating the need for continuous coordination and ensuring primary network performance is maintained while allowing secondary network access.
2Productivity
If massive MIMO technology is deployed, then spectral efficiency is improved, but implementation cost and complexity increase
Solution Approach 1:
The patent applies preliminary action by having the secondary base station perform channel estimation during a learning phase before actual data transmission. This preliminary estimation allows the system to pre-compute the necessary beamformer matrices and power allocation strategies, so that during normal operation, the secondary base station can directly use these pre-calculated parameters without requiring continuous complex computations or coordination, thereby reducing operational complexity while maintaining high spectral efficiency.
3Reliability
If channel state information sharing is required, then interference protection can be achieved, but cooperation overhead and information loss increase
Solution Approach 1:
The patent applies self-service by enabling the secondary base station to autonomously estimate the primary channel and compute protective beamformers without requiring explicit channel state information sharing from the primary network. The secondary base station performs its own channel estimation during the learning phase and independently calculates the zero-forcing beamformers, eliminating the need for inter-network information exchange while still achieving effective interference protection.
4Loss of time
If reverse time-division duplexing is used, then training overhead is reduced, but interference management becomes more challenging
Solution Approach 1:
The patent converts the interference management challenge into a benefit by using the learning phase, during which the secondary base station estimates the primary channel, to directly compute zero-forcing beamformers that nullify primary user interference. The interference that would normally be harmful is transformed into useful channel estimation information that enables automatic interference cancellation, so that the same time resources used for learning also provide the basis for effective interference protection during data transmission.
Data Source
AI summary
A method, system and apparatus for spectrum sharing in massive multiple input multiple output (MIMO) networks are disclosed. According to one aspect, a method in a secondary network node of a secondary network, the secondary network node configured to communicate with a plurality of secondary users, is provided. The method includes, performing channel estimates of primary users of a primary network during a learning phase that coincides with a training phase of the primary network. The method also includes determining a beamformer and power allocation based at least in part on the channel estimates to maximize at least one of a weighted uplink data rate and a weighted downlink data rate subject to a constraint on a data rate of the primary network.


