ANN Precoding Engine for Massive MIMO PAPR Reduction
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Solution Overview
Problem
Massive MIMO-OFDM systems face high Peak-to-Average Power Ratio (PAPR) issues, leading to expensive linear RF components and costly digital predistortion to manage nonlinear signal distortions, which can be mitigated by adopting low-PAPR precoding schemes but existing methods are complex and latency-prone due to iterative computations.
Innovation Solution
The implementation of an artificial neural network (ANN) precoding engine that processes input signals to achieve low PAPR precoding in a non-iterative manner, trained using signals from existing low PAPR massive MIMO precoding algorithms, reducing hardware complexity and latency by about 80% compared to state-of-the-art techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If iterative low-PAPR precoding algorithms are used, then PAPR is reduced, but hardware complexity and latency increase significantly
Solution Approach 1:
The patent pre-calculates and stores precoding matrices offline that are optimized for low-PAPR characteristics. During actual transmission, the system simply selects and applies pre-computed matrices rather than performing iterative optimization in real-time, thereby reducing hardware complexity while maintaining low-PAPR performance
Solution Approach 2:
The patent creates a simplified copy of the complex iterative algorithm by pre-computing the results offline and storing them in lookup tables. The runtime system uses these pre-computed solutions instead of executing the full iterative algorithm, achieving low-PAPR without the associated computational complexity
2Object-affected harmful factors
If iterative low-PAPR precoding algorithms are used, then PAPR is reduced, but transmission latency increases
Solution Approach 1:
The patent performs the time-consuming iterative optimization offline before transmission occurs. The pre-computed precoding matrices are stored and quickly retrieved during actual data transmission, eliminating iterative computation latency while maintaining low-PAPR benefits
Solution Approach 2:
The patent skips the iterative optimization steps during real-time transmission by using pre-computed solutions. The system rushes through the transmission process using ready-made precoding matrices, avoiding the time-consuming iterative computations that would otherwise be necessary
3Productivity
If conventional OFDM signaling is used, then capacity gains are achieved, but expensive linear RF components and digital predistortion are required
Solution Approach 1:
The patent changes the key parameter of PAPR by using pre-computed precoding matrices specifically optimized for low-PAPR characteristics. This parameter change allows the system to maintain high capacity while avoiding the need for expensive linear RF components and digital predistortion that are required by conventional high-PAPR OFDM signaling
Data Source
AI summary
A method and apparatus for artificial neural network precoding for massive MIMO systems are disclosed. In one embodiment, a method includes processing, by an artificial neural network, ANN, precoding engine, at least one input signal by 5 performing a low peak-to-average-power ratio, PAPR, precoding on the at least one input signal; and transmitting, via at least one antenna array having at least one antenna, at least one precoded output signal processed by the ANN precoding engine.