Optimizing Signal Reception in Cellular Networks via QUBO
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Solution Overview
Problem
Current optimization methods for signal reception in cellular communications networks are computationally expensive due to the need to estimate signal strength and interference across many locations and configurations, limiting the number of candidate sites and antenna configurations that can be considered.
Innovation Solution
A computer-implemented method that simplifies the optimization process by preselecting candidate best servers based on expected radio signal strength and applying constraints to reduce the number of potential configurations, using a quadratic unconstrained binary optimization (QUBO) function to determine the set of proposed servers, which can be efficiently evaluated using a quantum concept processor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current optimization methods estimate signal strength and interference across many locations and configurations, then signal reception quality is improved, but computational complexity increases
Solution Approach 1:
The service area is divided into discrete pixels, and the optimization problem is segmented into selecting at most one candidate best server per pixel. This segmentation allows the complex global optimization problem to be broken down into manageable per-pixel decisions, reducing overall computational complexity while maintaining signal quality assessment.
Solution Approach 2:
Candidate best servers are preselected for each pixel based on expected radio signal strength before the final optimization. This preliminary filtering reduces the number of configurations that need to be evaluated in the subsequent QUBO optimization, decreasing computational complexity while preserving the quality of signal reception assessment.
2Area of stationary object
If the number of candidate sites and antenna configurations is increased, then network coverage is improved, but the number of potential combinations to be considered increases
Solution Approach 1:
The method considers at most one candidate best server per pixel, which is a partial consideration of all possible servers. This partial action approach allows evaluating more candidate sites and configurations than traditional methods by making a simplifying assumption that reduces the combinatorial explosion while still achieving good coverage.
Solution Approach 2:
By preselecting candidate best servers based on expected signal strength before optimization, the method reduces the number of configurations that need to be evaluated. This preliminary filtering enables the consideration of more candidate sites overall while keeping the optimization tractable.
3Measurement precision
If traditional optimization methods are used to evaluate many configurations, then signal quality is improved, but processing time increases
Solution Approach 1:
The patent replaces traditional computational optimization methods with a quantum concept processor that uses quantum mechanical principles to solve the QUBO problem. This substitution leverages quantum parallelism and superposition to evaluate multiple configurations simultaneously, dramatically reducing processing time while maintaining signal quality assessment accuracy.
Solution Approach 2:
The patent reformulates the optimization problem as a QUBO function with specific parameters (signal strength, interference, constraints) that can be efficiently processed by quantum annealing. This parameter transformation enables the use of quantum computational methods that solve the problem faster than classical approaches while preserving the essential signal quality metrics.
Data Source
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AI summary
The present disclosure relates to a computer-implemented method for optimizing signal reception in a cellular communications network with a plurality of antennas for communicating with terminals. The method comprising: specifying a set S of candidate sites s and a set D of candidate antenna configurations, CAC, for placement of one or more antennas at the candidate sites s; specifying a set P of pixels p, each pixel p corresponding to a geographic location within a service area for which signal reception should be optimized; selecting a set Bp of candidate best servers, CBS, for each pixel p based on an expected radio signal strength for a terminal at the geographic location corresponding to the respective pixel p, each set Bp comprising zero or more candidate servers corresponding to one of the candidate sites s and one of the CAC; and determining a set of proposed servers. The determination is based at least on: an optimized aggregated signal-to-noise ratio, SNR, computed for all pixels p in case all of the proposed servers of the set which are built at the corresponding candidate sites s with the corresponding CAC, and a first restriction requiring that, from each set Bp of CBS, at most a single CBS is included in the set of proposed servers. The present disclosure further relates to a quantum concept processor and a computer program.