Abstract
•Uplink MU-MIMO systems face data detection issues due to noise and frequency allocations.•The existing low-complexity AMP algorithm will converge incorrectly when the MIMO channels are spatially correlated.•We propose an iterative data detection strategy based on a coordinate descent method.•Proposed approach is substantially energy efficient and less complicated.•Proposed approach expendable to a multi-cellular in a dense environment for future communications.
The current development of the Internet of Things (IoT) network in sixth-generation (6G) communication opens up various opportunities. When IoT devices with edge platforms connect to the telecommunication network, mobility, interferences, and intelligent device capacity issues might occur. The challenge with uplink MU-MIMO systems is data detection owing to noise, frequency allocations, and dense deployment of intelligent devices. The exponential complexity of extensive wireless networks makes optimal maximum likelihood detection impossible. Using MATLAB simulation-based analysis, we suggested an iterative data detection strategy based on a coordinate descent method (CDM) to reduce computing complexity while preserving an acceptable bit error rate (BER). Linear systems demand low co-channel interference (CCI) MU-MIMO uplink communications (CCI). A superior mean square error or BER performance is achieved using Maximum ratio combining with CDM. The proposed system outperforms the Richardson Method (RM), approximation message passing (AMP), and linear minimum mean square error (LMMSE) algorithms in terms of BER and complexity.
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