Low-Complexity Soft-Output Signal Detection Based on Improved Kaczmarz Iteration Algorithm for Uplink Massive MIMO...

Area of Science:

• Wireless communication systems
• Signal processing
• Information theory

Background:

• Massive MIMO systems offer high spectral efficiency but face computational challenges.
• Traditional MMSE-based signal detection algorithms are computationally intensive due to matrix inversion, especially with a large number of users.
• Efficient signal detection is crucial for practical multi-user uplink massive MIMO performance.

Purpose of the Study:

• To develop a low-complexity signal detection algorithm for multi-user uplink massive MIMO systems.
• To overcome the high computational complexity associated with traditional MMSE algorithms.
• To improve convergence speed and maintain near-optimal performance.

Main Methods:

• Proposed a novel soft-output signal detection algorithm based on an improved Kaczmarz method.
• Circumvented the need for matrix inversion, significantly reducing computational complexity.
• Introduced an optimal relaxation parameter to accelerate convergence and derived approximate methods for LLR calculation.

Main Results:

• The proposed algorithm achieves a complexity reduction of an order of magnitude compared to MMSE-based methods.
• Demonstrated rapid convergence and performance close to the MMSE algorithm with few iterations.
• Outperformed existing low-complexity signal detection algorithms in simulations.

Conclusions:

• The improved Kaczmarz method offers an efficient and effective solution for signal detection in massive MIMO systems.
• The algorithm provides a practical alternative for scenarios with a large number of users and antennas.
• This work contributes to the advancement of efficient signal processing in next-generation wireless networks.