Low‐complexity sphere decoding for MIMO‐SCMA systems

Abstract

Multiple‐input multiple‐output‐sparse code multiple access, a non‐trivial integration of sparse code multiple access and multiple‐input multiple‐output techniques, is able to achieve high spectrum efficiency and massive user connections. However, this integration also increases the complexity of signal detection. Here, the signal detection problem of multiple‐input multiple‐output sparse code multiple access is transformed into a tree search problem and use sphere decoding to detect the signal. By setting the initial radius to positive infinity, sphere decoding can achieve optimal maximum likelihood performance while the complexity is high. In order to further reduce the complexity of sphere decoding, a block‐wise sorted QR decomposition algorithm is proposed. Based on block‐wise sorted QR decomposition, the improved sphere decoding, namely block‐wise sorted QR decomposition‐sphere decoding, is able to make the tree search more efficient. Since only the detection order of each user's signal has been changed, block‐wise sorted QR decomposition‐sphere decoding can maintain the optimal maximum likelihood performance. Simulation results and complexity analysis show that block‐wise sorted QR decomposition‐sphere decoding can achieve optimal performance and both hard‐output and soft‐output block‐wise sorted QR decomposition‐sphere decoding have much lower complexity than joint message passing algorithm. Furthermore, given the same signal‐to‐noise ratio, the complexity of block‐wise sorted QR decomposition‐sphere decoding decreases with the increase of receiving antennas, while the complexity of joint message passing algorithm increases linearly.

Sparse Layered MIMO with Iterative Detection

“…Substituting (47) into (46), we obtain (30). The restriction of values of p to the set P, defined in (31), is due to the fact that the high-SNR approximation of the sum over p is dominated by the terms with the largest exponent of SNR (line 2 in (30)), which are the terms with the minimum value of (β p,N +1 + • • • + β p,n ).…”

Modifed SIC Algorithm based on Lattice Reduction Aided for MIMO OFDM Detection

“…This has the advantage of effectively reducing the error propagation and improving the accuracy of the detection algorithm. Several typical detection algorithms based on QR sorting in [10][11][12][13] are proposed, including QR sorting algorithm based on MMSE criterion and QR decomposition algorithm based on Schmidt orthogonalization. The above sorted QR algorithms are designed to make the order of the elements on the main diagonal of the matrix satisfy the optimal detection order at the receiving end.…”