A method for Soft-output Fixed-complexity Sphere Decoding, SFSD, detection, comprising: performing QR decomposition on a channel response matrix to acquire a Q matrix and an R matrix; multiplying a conjugate transpose of the Q matrix by a received signal to acquire an equalized signal of the received signal; performing Maximum Likelihood, ML, path detection on the equalized signal, decreasing, layer by layer, reserved nodes in respective layers to acquire an ML path, and reserving branches as many as iterations; performing ML complementary set path detection on the branches, and reserving all nodes of an acquired complementary set layer and decreasing, layer by layer, reserved nodes in other layers to acquire an ML complementary set path; and acquiring Likelihood Ratio, LLR, information of each bit of each symbol of each layer according to the ML path and the ML complementary set path, wherein the performing Maximum Likelihood, ML, path detection on the equalized signal, decreasing, layer by layer, reserved nodes in respective layers to acquire an ML path, and reserving branches as many as iterations, comprises: performing path extension starting from a top layer of the R matrix, which is a layer having only one non-zero element, to a bottom layer; reserving all nodes of the top layer and decreasing, layer by layer, reserved nodes in layers below the top layer to acquire Euclidean distances of respective branches; and selecting a branch having a minimum Euclidean distance as the ML path, and reserving branches as many as iterations, wherein the performing ML complementary set path detection on the branches, and reserving all nodes of an acquired complementary set layer and decreasing, layer by layer, reserved nodes in other layers to acquire an ML complementary set path, comprises: performing path extension starting from acquired complementary set layers of respective reserved branches to corresponding bottom layers; reserving all nodes of the acquired complementary set layers and decreasing, layer by layer, reserved nodes in layers below the acquired complementary set layers to acquire Euclidean distances of respective branches; and selecting, layer by layer, starting from the acquired complementary set layers, a branch having a minimum Euclidean distance as an ML complementary set path of each layer. performing QR decomposition on a channel response matrix to acquire a Q matrix and an R matrix; multiplying a conjugate transpose of the Q matrix by a received signal to acquire an equalized signal of the received signal; performing Maximum Likelihood, ML, path detection on the equalized signal, decreasing, layer by layer, reserved nodes in respective layers to acquire an ML path, and reserving branches as many as iterations; performing ML complementary set path detection on the branches, and reserving all nodes of an acquired complementary set layer and decreasing, layer by layer, reserved nodes in other layers to acquire an ML complementary set path; and acquiring Likelihood Ratio, LLR, information of each bit of each symbol of each layer according to the ML path and the ML complementary set path, wherein the performing Maximum Likelihood, ML, path detection on the equalized signal, decreasing, layer by layer, reserved nodes in respective layers to acquire an ML path, and reserving branches as many as iterations, comprises: performing path extension starting from a top layer of the R matrix, which is a layer having only one non-zero element, to a bottom layer; reserving all nodes of the top layer and decreasing, layer by layer, reserved nodes in layers below the top layer to acquire Euclidean distances of respective branches; and selecting a branch having a minimum Euclidean distance as the ML path, and reserving branches as many as iterations, wherein the performing ML complementary set path detection on the branches, and reserving all nodes of an acquired complementary set layer and decreasing, layer by layer, reserved nodes in other layers to acquire an ML complementary set path, comprises: performing path extension starting from a top layer of the R matrix, which is a layer having only one non-zero element, to a bottom layer; reserving all nodes of the top layer and decreasing, layer by layer, reserved nodes in layers below the top layer to acquire Euclidean distances of respective branches; and selecting a branch having a minimum Euclidean distance as the ML path, and reserving branches as many as iterations, wherein the performing ML complementary set path detection on the branches, and reserving all nodes of an acquired complementary set layer and decreasing, layer by layer, reserved nodes in other layers to acquire an ML complementary set path, comprises: performing path extension starting from acquired complementary set layers of respective reserved branches to corresponding bottom layers; reserving all nodes of the acquired complementary set layers and decreasing, layer by layer, reserved nodes in layers below the acquired complementary set layers to acquire Euclidean distances of respective branches; and selecting, layer by layer, starting from the acquired complementary set layers, a branch having a minimum Euclidean distance as an ML complementary set path of each layer.