Joint MIMO Constellation Precoding for Lower Symbol Error Rates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing modulation schemes for MIMO systems are sub-optimal in terms of symbol error rate (SER) due to regular structures and focus on complex-valued plain methods, which do not effectively utilize the potential of MIMO systems.
Innovation Solution
A joint constellation and precoding method that designs constellations for the entire communication channel, optimizing transmit symbols to minimize symbol error rate by maximizing Mahalanobis distance and utilizing a joint codebook, thereby optimizing MIMO multiplexing gain and reducing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate modulation designs are used for each sub-channel in MIMO systems, then the system structure remains simple and easy to implement, but the symbol error rate performance becomes sub-optimal
Solution Approach 1:
The patent combines separate sub-channel modulation designs into a unified joint constellation and precoding design. Instead of designing modulation schemes independently for each sub-channel, the invention creates a joint codebook that simultaneously optimizes constellation points and precoding matrices across all sub-channels, achieving optimal SER performance while maintaining implementation feasibility through structured codebook designs.
Solution Approach 2:
The patent extends the modulation design from the traditional two-dimensional complex-valued I/Q plane to a higher-dimensional space that incorporates multiple sub-channels and spatial dimensions. By designing constellations in this expanded dimensionality, the system can exploit spatial diversity and multiplexing gains across multiple antennas and sub-channels simultaneously, achieving better SER performance.
2Productivity
If joint constellation and precoding design is applied to the entire communication channel, then MIMO multiplexing gain is optimized and power consumption is reduced, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary design of joint codebooks offline or during system initialization, pre-computing optimal constellation and precoding configurations for various channel conditions. These pre-designed codebooks are then stored and directly applied during actual communication, avoiding real-time computational complexity while maintaining optimal multiplexing gain and power efficiency.
Solution Approach 2:
The patent employs parameterized codebook designs where constellation points and precoding matrices are defined by a set of parameters that can be adjusted based on channel state information. This parameterization allows the system to adapt to different channel conditions and multiplexing scenarios without requiring full re-computation, thus reducing computational complexity while optimizing multiplexing gain.
3Reliability
If regular structured constellations are used in existing modulation schemes, then the implementation is straightforward, but the symbol error rate performance is sub-optimal
Solution Approach 1:
The patent introduces asymmetric and irregular constellation structures in the joint codebook design, where constellation points are non-uniformly distributed in the expanded dimensionality space. These irregular constellations are specifically optimized to maximize minimum distances between points and exploit channel characteristics, achieving superior SER performance compared to regular structured constellations while maintaining implementation feasibility through algorithmic generation.
Data Source
AI summary
The disclosed exemplary embodiments are directed to wireless communication apparatus and methods that, among other advantages, improve bit error rates. In some examples, a multi-input multi-output (MIMO) transmitter receives data for transmission. The transmitter applies a joint constellation and precoding process to the received data to generate corresponding transmit symbols. The joint constellation and precoding process encodes the symbols jointly, using a codebook that maps sequences of bits to symbols. Further, the transmitter applies a Mahalanobis distance process to the transmit symbols to separate the transmit symbols, and generates a transmission signal based on the separated transmit symbols. The transmitter transmits the transmission signal to each of multiple antennae for wireless transmission. In some examples, a receiver receives the transmitted symbols from the plurality of antennae, and applies a corresponding decoding process to the received symbols to generate the data.