Simulink Framework for Multi-Cell Multiuser MIMO OFDM Uplink with Massive MIMO Arrays and Finite-Dimensional Channel Modeling

Abstract

In this work, a chaos-based communication approach is employed to enhance the Bit Error Rate (BER) performance of wireless systems using Multiple-Input Multiple-Output (MIMO) antenna configurations. Conventional system designs often suffer from poor BER performance, which remains a critical challenge in reliable communication systems. To address this issue, a group communication framework based on a 2×2 MIMO architecture is proposed, incorporating Correlation Delay Shift Keying (CDSK) modulation. The BER performance of the proposed system is evaluated over a Rayleigh fading MIMO channel. Furthermore, a MIMO-based secure transmission scheme is considered, leveraging the increasing demand for higher data rates and efficient utilization of available radio resources in multi-device communication environments. The integration of MIMO with chaos-based communication improves system robustness and spectral efficiency. To further enhance data throughput and bandwidth utilization, Orthogonal Frequency Division Multiplexing (OFDM) is adopted, enabling high data rate transmission with improved resistance to channel impairments.To combat channel sparsity and estimation challenges in large-scale MIMO systems, this paper proposes a Structured Sparse Adaptive Compressive Sampling Matching Pursuit (SSA-CoSaMP) algorithm. The proposed algorithm exploits the inherent spatial–temporal sparsity of massive MIMO channels and dynamically adapts sparsity levels based on signal-to-noise ratio (SNR) conditions. Additionally, a threshold-based energy focusing mechanism is introduced to improve estimation accuracy.The proposed SSA-CoSaMP algorithm not only enhances channel estimation performance but also significantly reduces pilot overhead, thereby conserving bandwidth and energy resources. Simulation results demonstrate improved BER performance and system efficiency compared to conventional techniques.