Aiming　at　the　problem　that　most　existing　algorithms　were　based　on　the　Gaussian　inverse　gamma　prior　model　(GIG-SBL),　which　ignored　the　sparsity　of　the　support　set　vector　within　the　sparse　solution,　a　sparse　Bayesian　learning　framework　based　on　the　Bernoulli　Gaussian　inverse　gamma　prior　model　(BGIG-SBL)　was　proposed.　By　introducing　a　binary　vector　of　Bernoulli　prior,　the　BGIG-SBL-SMV　algorithm　based　on　single　measurement　vector　(SMV)　was　designed,　which　utilized　the　sparsity　of　the　support　set　vector　to　improve　reconstruction　performance.　Then　the　proposed　algorithm　was　extended　to　multiple　measurement　vector　(MMV)　models　by　sharing　the　same　hyperparameters.　The　BGIG-SBL-MMV　algorithm　was　developed　based　on　the　joint　sparsity　of　MMV.　The　experimental　results　show　that　the　proposed　BGIG-SBL-SMV　can　achieve　a　performance　gain　of　2　dB　in　mMTC　over　the　traditional　GIG-SBL-SMV.　Moreover,　BGIG-SBL-MMV　has　a　performance　gain　of　4　dB　as　compared　with　its　SMV　counterparts,　which　demonstrates　the　advantages　of　the　proposed　schemes.　©　2023　Editorial　Board　of　Journal　on　Communications.　All　rights　reserved.