Efficient Channel Estimation with Reduced Complexity for Cell-Free Massive MIMO in 6G Networks

Abstract

The development of 6G networks created a larger interest on cell-free massive MIMO (CF-mMIMO) as it had the potential of providing high-quality cell-free wireless service uniformly because of their ability to remove cell boundaries and side-effects of inter-cell interference. Irrespective of these benefits, effective implementation of CF-mMIMO is limited by the intensive amount of calculation and communication overhead due to require high accuracy on channel state information (CSI), especially when RF infrastructure scales up. In this paper, a channel estimation framework is given that is efficient and has minimal complexity to work with a CF-mMIMO system. The suggested approach combines sparsity-conscious signal processing and variable pilot reuse techniques to reduce computational overhead as well as fronthaul signaling loads. It uses distributed architecture where by access points do local estimation and only transmit compressed channel features to the central unit. Dense deployment simulation show that the proposed framework can produce similar relative normalized mean square error (NMSE) and uplink spectral efficiency to the conventional minimum mean square error (MMSE) estimators, that can produce similar relative normalized mean square error (NMSE) and uplink spectral efficiency to the conventional minimum mean square error (MMSE) estimators, with substantially lower processing time and data exchange needs. The proposed solution is viable in this work because it offers a viable and economically viable solution to the high-density and low-latency requirements of the next-generation 6G networks through the acquisition of CSI in CF-mMIMO systems.