Low‐complexity sphere decoding for MIMO‐SCMA systems. Issue 4 (7th January 2021) - Pan, Zhipeng; Lei, Jing; Wen, Lei; Tang, Chaojing; Wang, Zhongfeng
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.