Research ArticleOpen AccessGoogle Scholar indexed
Capacity Analysis and Information Optimization of WSDM
Department of Wireless Communications, PLA University of Science and Technology, Nanjing, China
Department of Wireless Communications, PLA University of Science and Technology, Nanjing, China
- 1 Department of Wireless Communications, PLA University of Science and Technology, Nanjing, China
- 2 Department of Wireless Communications, PLA University of Science and Technology, Nanjing, China
International Journal of Communications, Network and System Sciences·Volume 09 (2016)·Pages 160–167·Published 30 May 2016·DOI10.4236/ijcns.2016.95015
Copy link · social · email
Abstract
Wireless statistic division multiplexing (WSDM) is a multiplexing scheme that transmits multiple signals simultaneously in the same frequency band over wireless channels. Based on the Shannon capacity of band-limited waveform AWGN channel with input power constraint, we obtain channel capacity of WSDM. Compared to time division multiplexing (TDM), frequency division multiplexing (FDM), and code division multiplexing (CDM), WSDM is more effective in raising spectrum efficiency. What’s more, we propose information optimization method to separate time-frequency mixed signals. Computer simulations also verify that the proposed method is feasible and performs better than traditional algorithms.
KeywordsCapacity Analysis and Information Optimization of WSDM
- Stotas, S. and Nallanathan, A. (2011) Enhancing the Capacity of Spectrum Sharing Cognitive Radio Networks. IEEE Transactions on Vehicular Technology, 60, 3768-3779. http://dx.doi.org/10.1109/TVT.2011.2165306
- Rebeiz, E., Yuan, F.L. and Urriza, P. (2014) Energy-Efficient Processor for Blind Signal Classification in Cognitive Radio Networks. IEEE Transactions on Circuits and Systems, 61, 587-599. http://dx.doi.org/10.1109/tcsi.2013.2278392
- Kao, D.T.H. and Sabharwal, A. (2013) Two-User Interference Channels with Local Views: On Capacity Regions of TDM-Dominating Policies. IEEE Transactions on Information Theory, 59, 7014-7040. http://dx.doi.org/10.1109/TIT.2013.2274512
- Theodoulidis, T.P., Kantartzis, N.V., Tsiboukis, T.D. and Kriezis, E.E. (1997) FDM-Based Second Order Vector Potential Formulation for 3D Eddy Current Curvilinear Problems. IEEE Transactions on Magnetics, 33, 1287-12190. http://dx.doi.org/10.1109/20.582490
- Lamare, R.C. and Diniz, P.S.R. (2009) Set-Membership Adaptive Algorithms Based on Time-Varying Error Bounds for CDMA Interference Suppression. IEEE Transactions on Vehicular Technology, 58, 644-654. http://dx.doi.org/10.1109/TVT.2008.926608
- Zhang, J.G. (1999) Design of Integrated Services Digital Broadcasting Systems Using Multirate Optical Fiber Code-Division Multiplexing. IEEE Transactions on Broadcasting, 45, 283-293. http://dx.doi.org/10.1109/11.796270
- Zhao, W., Shen, Y., Xu, P., Wang, J., Yuan, Z., Wei, Y., Jian, W. and Li, H. (2014) A Novel Wireless Statistical Division Multiplexing Communication System and Performance Analysis. International Journal of Future Generation Communication and Networking, 7, 1-10. http://dx.doi.org/10.14257/ijfgcn.2014.7.5.01
- Zhao, W., Shen, Y., Xu, P., Wei, Y., Yuan, Z. and Jian, W. (2015) Statistic Division Multiplexing for Wireless Communication Systems. Fifth IEEE International Conference on Information Science and Technology. 2015 5th International Conference on Information Science and Technology (ICIST), 24-26 April 2015, Changsha, 392-397.
- Osterwise, C. and Grant, S.L. (2014) On Over-Determined Frequency Domain BSS. IEEE Transactions on Audio, Speech, Language Processing, 22, 954-964.
- Fu, G.S., Phlypo, R., Anderson, M., et al. (2014) Blind Source Separation by Entropy Rate Minimization. IEEE Transactions on Signal Processing, 62, 4245-4255. http://dx.doi.org/10.1109/TSP.2014.2333563
- Lahat, D., Cardoso, J.F. and Messer, H. (2012) Second-Order Multidimensional ICA: Performance Analysis. IEEE Transactions on Signal Processing, 60, 4598-4610. http://dx.doi.org/10.1109/TSP.2012.2199985