Weitere Artikel dieser Ausgabe durch Wischen aufrufen
In order to reduce the complexity of hard-output K-best decoding algorithm in multiple-input multiple-output (MIMO) systems and guarantee performance of the system, we propose a bit-sort (BS) strategy based on bit counting operation in hardware implementation for the K-best decoder. The proposed BS K-best algorithm finds out the smallest \(K\) paths by scanning and counting the bits of every candidate, which is much simpler than the pairwise comparison operation in conventional K-best algorithm. Moreover, we proposed a dynamic bit-sort (DBS) strategy for the K-best decoder based on the BS strategy. The DBS K-best algorithm further reduces the complexity of BS K-best algorithm by selecting a dynamic \(K\) value that depends on the candidates. The complexity analysis shows the complexity of bit counting operations in proposed BS K-best algorithm is much less than that of the pairwise comparisons in conventional K-best algorithm, and the DBS K-best algorithm can further reduce about 50 % counting complexity of BS K-best algorithm. The simulation results show both the BS K-best decoder and DBS K-best decoder can achieve the same performance as that of hard-output sphere decoding algorithm if a proper \(K\) is selected.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Foschini, G. J. (1996). Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas. Bell Labs Technical Journal, 1(2), 41–59. CrossRef
Zhu, X., & Murch, R. D. (2002). Performance analysis of maximum likelihood detection in a MIMO antenna system. IEEE Transactions on Communications, 50(2), 187–191. CrossRef
Studer, C., Burg, A., & Bolcskei, H. (2008). Soft-output sphere decoding: Algorithms and VLSI implementation. IEEE Journal on Selected Areas in Communications, 26(2), 290–300. CrossRef
Guo, Z., & Nilson, P. (2006). Algorithm and implementation of the K-best sphere decoding for MIMO detections. IEEE Journal on Selected Areas in Communications, 24(3), 491–503. CrossRef
Li, Q., & Wang, Z. (2006). Improved K-best sphere decoding algorithms for MIMO systems. In Proceedings of the IEEE International Symposium Circuits and Systems (pp. 1159–1162).
Studer, C. (2009). Iterative MIMO decoding: Algorithms and VLSI implementation aspects. Ph.D. dissertation, ETH Zurich, Switzerland, Series in Microelectronics, 202.
Wu, Y. H., Liu, Y. T., Chang, H. C., et al. (2008). Early-pruned K-best sphere decoding algorithm based on radius constraints. ICC, 2008, 4496–4500.
Roy, S., & Banerjee, P. (2005). An algorithm for trading off quantization error with hardware resources for MATLAB-based FPGA design. IEEE Transactions on Computers, 54(7), 886–896. CrossRef
Erceg, V. et al. (2004). TGn channel models. IEEE 802.11 document 03/940r4.
The Institute of Electrical and Electronics Engineers, Inc. (2012). Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. IEEE Std 802.11-2012, pp. 1600–1602.
Knuth, D. E. (1998). Art of computer programming volume 3: Sorting and searching. Boston, MA: Addison-Wesley.
Sun, Y., & Cavallaro, J. R. (2010). Low-complexity and high-performance soft MIMO detection based on distributed M-algorithm through Trellis-Diagram. In IEEE International conference on acoustics speech and signal process (ICASSP) (pp. 3398–3401).
- Research on Low Complexity K-best Sphere Decoding Algorithm for MIMO Systems
- Springer US