Abstract
This paper proposes an adaptive threshold-aided K-best sphere decoding (AKSD) algorithm for large multiple-input multiple-output systems. In the proposed scheme, to reduce the average number of visited nodes compared to the conventional K-best sphere decoding (KSD), the threshold for retaining the nodes is adaptively determined at each layer of the tree. Specifically, we calculate the adaptive threshold based on the signal-to-noise ratio and index of the layer. The ratio between the first and second smallest accumulated path metrics at each layer is also exploited to determine the threshold value. In each layer, in addition to the K paths associated with the smallest path metrics, we also retain the paths whose path metrics are within the threshold from the Kth smallest path metric. The simulation results show that the proposed AKSD provides nearly the same bit error rate performance as the conventional KSD scheme while achieving a significant reduction in the average number of visited nodes, especially at high signal-to-noise ratios.
Highlights
Large multiple-input multiple-output (MIMO) systems have received enormous attention from researchers in the field of wireless communication for their high spectral and power efficiency [1]
We present the aided K-best sphere decoding (AKSD) algorithm, which reduces the computational complexity of the K-best sphere decoding (KSD) for large MIMO systems
By employing the adaptive threshold, the proposed AKSD algorithm significantly reduces the number of visited nodes while preserving nearly the same bit-error rate (BER) performance as the conventional KSD
Summary
Large multiple-input multiple-output (MIMO) systems have received enormous attention from researchers in the field of wireless communication for their high spectral and power efficiency [1]. To approach the bit-error rate (BER) performance of the original SD, the KSD needs to retain a large number of nodes in each layer, as presented in [9], which leads to a large computational complexity. We propose the AKSD algorithm, in which the adaptive threshold controls the number of visited nodes in each layer of the tree. In this algorithm, the threshold for retaining the most promising nodes at each layer is adaptively determined based on the ratio between the first and second minimum path metrics at each layer, the SNR, and the layer index. The simulation results show that, compared to the conventional KSD algorithm, the AKSD algorithm requires up to 71% less computational complexity.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
