M-MIMO Wireless Distribution System Antenna Configuration
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Solution Overview
Problem
Current wireless distribution systems face challenges in scaling multiple-input multiple-output (MIMO) technology due to space limitations and complexity, particularly in client devices, which restricts the ability to meet increasing bandwidth demands from bandwidth-hungry applications like HD video and virtual reality, and require adaptive antenna configurations to address non-uniform client device density distributions.
Innovation Solution
A massive MIMO wireless distribution system with a non-uniform client device density distribution, where remote units are strategically located and equipped with varying numbers of antennas, determined using an iterative algorithm and performance-estimation function to maximize system capacity, reducing complexity and costs while enhancing user experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If MIMO technology is scaled up with more antennas to meet increasing bandwidth demands, then system capacity and data throughput are improved, but device complexity and space requirements worsen
Solution Approach 1:
The patent applies local quality by deploying remote units with different numbers of antennas based on local client device density distributions. Areas with higher client density receive remote units with more antennas, while areas with lower density receive remote units with fewer antennas. This localized adaptation optimizes system capacity where needed without unnecessarily increasing complexity in all areas.
Solution Approach 2:
The patent segments the wireless distribution system into multiple remote units, each serving specific geographic areas. This segmentation allows independent configuration of antenna numbers for each remote unit based on local demands, rather than requiring all units to have the same high number of antennas, thus reducing overall system complexity while maintaining high capacity where needed.
2Device complexity
If uniform antenna configuration is used across all remote units, then device complexity is reduced, but adaptability to non-uniform client device density distributions worsens
Solution Approach 1:
The patent implements local quality by configuring each remote unit with a specific number of antennas tailored to the client device density of its serving area. The system adapts to non-uniform distributions by having remote units in high-density areas use more antennas while those in low-density areas use fewer antennas, optimizing performance for each local condition.
Solution Approach 2:
The patent applies dynamics by making the antenna configuration flexible and adaptive rather than static and uniform. The system can dynamically adjust the number of active antennas in each remote unit based on real-time or planned client device density distributions, allowing the system to adapt to changing conditions while maintaining manageable complexity through standardized configuration procedures.
3Productivity
If more remote units with more antennas are deployed to cover non-uniform client device distributions, then system capacity is improved, but infrastructure costs and installation complexity worsen
Solution Approach 1:
The patent reduces infrastructure costs by applying local quality - deploying remote units with antenna counts matched to local client density requirements. This avoids the waste of installing high-antenna-count remote units in low-density areas, thereby reducing hardware costs, installation complexity, and maintenance requirements while still providing adequate capacity in high-density areas.
Solution Approach 2:
The patent applies partial action by deploying the minimum necessary antenna resources in each area rather than uniformly excessive resources everywhere. In low-density areas, fewer antennas are deployed (partial action), while in high-density areas, more antennas are deployed as needed. This approach achieves adequate system capacity without the excessive infrastructure costs that would result from uniform high-level deployment across all areas.
Data Source
AI summary
Embodiments of the disclosure relate to a massive multiple-input multiple-output (M-MIMO) wireless distribution system (WDS) and related methods for optimizing the M-MIMO WDS. In one aspect, the M-MIMO WDS includes a plurality of remote units each deployed at a location and includes one or more antennas to serve a remote coverage area. At least one remote unit can have a different number of the antennas from at least one other remote unit in the M-MIMO WDS. In another aspect, a selected system configuration including the location and number of the antennas associated with each of the remote units can be determined using an iterative algorithm that maximizes a selected system performance indicator of the M-MIMO WDS. As such, it may be possible to optimize the selected system performance indicator at reduced complexity and costs, thus helping to enhance user experiences in the M-MIMO WDS.