Grouped MIMO Precoding for Receiver Complexity Reduction
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Solution Overview
Problem
Current wireless communication systems face challenges in maintaining accurate signal transmission and reception while reducing power requirements and complexity in user equipment devices, which affects battery life and system efficiency.
Innovation Solution
Implementing a MIMO precoding approach that weights unevenly different MIMO subchannels based on their relative channel gains, allowing for the decomposition of the MIMO channel into distinct groups with different transmission characteristics, which can be decoded sequentially, thereby improving channel capacity and reducing receiver complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If MIMO channel decomposition into different groups is implemented, then receiver complexity is reduced, but channel capacity may be limited by sequential decoding
Solution Approach 1:
The MIMO channel is segmented into multiple MIMO subgroups based on channel gain characteristics. Each subgroup is processed independently with tailored precoding strategies, allowing the receiver to decode simpler subgroups first and progressively reconstruct the full signal, thereby reducing overall receiver complexity while maintaining channel capacity through systematic decomposition.
Solution Approach 2:
Different precoding matrices are applied to different MIMO subgroups based on their local channel characteristics. Stronger subgroups receive different weighting and processing than weaker subgroups, optimizing the trade-off between decoding complexity and capacity utilization for each local channel condition.
2Productivity
If unequal weighting of MIMO subchannels is applied, then channel capacity is improved, but receiver complexity increases
Solution Approach 1:
The channel is divided into subgroups that can be decoded in sequence. By segmenting the unequal weighting problem into manageable subgroups with different precoding matrices, the receiver processes simpler components first, reducing overall complexity while still achieving capacity improvements through unequal weighting of the subgroups.
Solution Approach 2:
Precoding matrices with different parameters (weights, dimensions) are applied to different subgroups. This parameter variation allows the system to optimize channel capacity for each subgroup based on its specific channel gain characteristics, while the sequential decoding approach manages the complexity introduced by these parameter changes.
3Reliability
If more MIMO antennas are used, then transmit and receive abilities are improved, but power consumption and device complexity increase
Solution Approach 1:
The MIMO antenna system is segmented into multiple subgroups, each processed with dedicated precoding matrices. This segmentation allows the receiver to process signals from different antenna groups sequentially with optimized complexity, enabling the system to utilize more antennas for improved reliability while managing power consumption through efficient sequential processing rather than simultaneous full-MIMO decoding.
Data Source
AI summary
This disclosure relates to performing grouped MIMO communications in a cellular communication system. A cellular base station may select a precoding matrix for transmitting a downlink signal to a wireless device via a MIMO channel. The selected precoding matrix may have unequal weights for different MIMO subchannels. The downlink signal may be precoded using the selected precoding matrix. The precoded downlink signal may be transmitted to the wireless device via the MIMO channel. The wireless device may receive and decode the downlink signal.