Efficient and Low-Complex Signal Detection with Iterative Feedback in Wireless MIMO-OFDM Systems

Area of Science:

• Electrical Engineering
• Computer Science

Background:

• Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems face challenges with error propagation and high computational complexity in signal detection.
• Existing methods often struggle to balance detection accuracy with computational efficiency.

Purpose of the Study:

• To propose a low-complexity and efficient signal detection scheme for MIMO-OFDM systems.
• To mitigate error propagation and improve detection performance.

Main Methods:

• A constellation point feedback optimization of Minimum Mean Square Error-Ordered Successive Interference Cancellation (MMSE-OSIC) is employed.
• Maximum Likelihood (ML) criterion is used to select candidate vectors, reducing error propagation from previous decisions.
• A symmetric successive relaxation iterative algorithm is introduced to avoid complex matrix inversions.
• A parallel coarse and fine detection strategy is implemented.

Main Results:

• The proposed method approaches optimal detection performance with reduced computational complexity.
• The symmetric successive relaxation algorithm avoids matrix inversions, enabling faster signal detection.
• Error diffusion and complexity accumulation are improved compared to traditional OSIC schemes.
• The parallel detection method further reduces iterations and enhances overall performance.

Conclusions:

• The developed signal detection scheme significantly enhances MIMO-OFDM system performance.
• This approach is crucial for advancing future technologies like 6G mobile communications and wireless sensor networks.