Distributed Scheduling for Multi-Antenna Wireless Systems
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Solution Overview
Problem
The complexity of selecting an optimal subset of users in multi-antenna wireless communication systems with zero-forcing beam forming leads to mathematical intractability and significant communication overhead, making it impractical for large pool sizes.
Innovation Solution
Offloading the optimization process to individual receivers, allowing them to assess their own enrollment based on aggregate quality, reducing the computational burden on the base station and minimizing data transmission overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If exhaustive search optimization is used to select optimal user subset, then aggregate data rate is maximized, but computational complexity becomes mathematically intractable for large pool sizes
Solution Approach 1:
The patent segments the user selection process into two phases: (1) base station performs initial channel characterization and sends test signals to multiple users, and (2) each user independently evaluates their own suitability for enrollment based on received channel information. This segmentation distributes the computational burden from centralized exhaustive search to distributed individual evaluations, making the system scalable to large user pools while maintaining near-optimal aggregate data rate performance.
2Productivity
If exhaustive search optimization is performed at base station, then optimal user subset is identified, but communication overhead for channel characterization becomes overwhelming
Solution Approach 1:
The patent extracts the heavy channel characterization and evaluation computations from the base station and relocates them to the individual user equipment. The base station only needs to send compact test signals and receive simple enrollment decisions, dramatically reducing the communication overhead while still achieving optimal user subset selection through distributed intelligence.
3Measurement precision
If centralized optimization is performed at base station, then global optimal solution is achieved, but scalability to large number of users is limited
Solution Approach 1:
The patent implements self-service by enabling each user equipment to independently evaluate its own enrollment suitability based on channel conditions and system state information. This self-evaluation mechanism allows the system to scale to large user pools without overwhelming the base station, while still achieving near-optimal user subset selection through the coordinated interactions of self-interested users.
Data Source
AI summary
A channel allocation system for a beam-forming wireless network selects receivers to enroll in communication from a pool of candidate receivers, by off-loading a determination of the effects of adding each candidate receiver to the candidate receiver itself. In one embodiment, the candidate receivers nominate themselves for enrollment based on their determination of aggregate data rate changes resulting from their enrollment and the comparison of this aggregate data rate change against an estimate of the aggregate data rate changes of other candidate receivers.