Stochastic signaling in the presence of channel state information uncertainty

In this paper, stochastic signaling is studied for power-constrained scalar valued binary communications systems in the presence of uncertainties in channel state information (CSI). First, stochastic signaling based on the available imperfect channel coefficient at the transmitter is analyzed, and it is shown that optimal signals can be represented by a randomization between at most two distinct signal levels for each symbol. Then, performance of stochastic signaling and conventional deterministic signaling is compared for this scenario, and sufficient conditions are derived for improvability and nonimprovability of deterministic signaling via stochastic signaling in the presence of CSI uncertainty. Furthermore, under CSI uncertainty, two different stochastic signaling strategies, namely, robust stochastic signaling and stochastic signaling with averaging, are proposed. For the robust stochastic signaling problem, sufficient conditions are derived for reducing the problem to a simpler form. It is shown that the optimal signal for each symbol can be expressed as a randomization between at most two distinct signal values for stochastic signaling with averaging, as well as for robust stochastic signaling under certain conditions. Finally, two numerical examples are presented to explore the theoretical results.

[1]  Xiaoli Ma,et al.  Space-time-multipath coding using digital phase sweeping , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[2]  Bastian Goldlücke,et al.  Variational Analysis , 2014, Computer Vision, A Reference Guide.

[3]  Sinan Gezici,et al.  Optimal signaling and detector design for power-constrained binary communications systems over non-gaussian channels , 2010, IEEE Communications Letters.

[4]  Wayne E. Stark,et al.  Worst-case power-constrained noise for binary-input channels with varying amplitude signals , 1994, Proceedings of 1994 IEEE International Symposium on Information Theory.

[5]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[6]  John P. Fonseka,et al.  Optimal binary communication with nonequal probabilities , 2003, IEEE Trans. Commun..

[7]  Murat Azizoglu,et al.  Convexity properties in binary detection problems , 1996, IEEE Trans. Inf. Theory.

[8]  Sinan Gezici,et al.  On the optimality of stochastic signaling under an average power constraint , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[9]  Teng Joon Lim,et al.  Binary Demodulation in Rayleigh Fading with Noisy Channel Estimates - Detector Structures and Performance , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[10]  Edite M. G. P. Fernandes,et al.  Optimization of nonlinear constrained particle swarm , 2006 .

[11]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[12]  Sinan Gezici,et al.  Optimal Stochastic Parameter Design for Estimation Problems , 2012, IEEE Transactions on Signal Processing.

[13]  Michael N. Vrahatis,et al.  Particle Swarm Optimization Method for Constrained Optimization Problems , 2002 .

[14]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[15]  Fumiyuki Adachi,et al.  Combined effects of phase sweeping transmitter diversity and channel coding , 1992 .

[16]  Shlomo Shamai,et al.  Worst-case power-constrained noise for binary-input channels , 1992, IEEE Trans. Inf. Theory.

[17]  Erik G. Larsson Constellation randomization (CoRa) for outage performance improvement on MIMO channels , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[18]  Sonia Aïssa,et al.  On the effects of Gaussian channel estimation errors on the capacity of adaptive transmission with space-time block coding , 2005, WiMob'2005), IEEE International Conference on Wireless And Mobile Computing, Networking And Communications, 2005..

[19]  Bernard Mulgrew,et al.  Non-parametric likelihood based channel estimator for Gaussian mixture noise , 2007, Signal Process..

[20]  Joseph Jean Boutros,et al.  On random rotations diversity and minimum MSE decoding of lattices , 2000, IEEE Trans. Inf. Theory.

[21]  Peter Sinčák,et al.  Intelligent technologies - theory and applications : new trends in intelligent technologies , 2002 .

[22]  Costas N. Georghiades,et al.  Transmit diversity over quasi-static fading channels using multiple antennas and random signal mapping , 2000, 2000 IEEE International Conference on Communications. ICC 2000. Global Convergence Through Communications. Conference Record.

[23]  Sinan Gezici,et al.  Effects of Channel State Information Uncertainty on the Performance of Stochastic Signaling , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[24]  Pramod K. Varshney,et al.  Theory of the Stochastic Resonance Effect in Signal Detection: Part I—Fixed Detectors , 2007, IEEE Transactions on Signal Processing.

[25]  Erik G. Larsson,et al.  Improving the frame-error-rate of spatial multiplexing in block fading by randomly rotating the signal constellation , 2004, IEEE Communications Letters.

[26]  Bernard Fino,et al.  Multiuser detection: , 1999, Ann. des Télécommunications.

[27]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[28]  H. Vincent Poor,et al.  An introduction to signal detection and estimation (2nd ed.) , 1994 .

[29]  Ashok Patel,et al.  Optimal Noise Benefits in Neyman–Pearson and Inequality-Constrained Statistical Signal Detection , 2009, IEEE Transactions on Signal Processing.

[30]  Sinan Gezici,et al.  Detector Randomization and Stochastic Signaling for Minimum Probability of Error Receivers , 2012, IEEE Transactions on Communications.

[31]  Stephen E. Fienberg,et al.  Testing Statistical Hypotheses , 2005 .

[32]  Pramod K. Varshney,et al.  Theory of the Stochastic Resonance Effect in Signal Detection—Part II: Variable Detectors , 2007, IEEE Transactions on Signal Processing.

[33]  Sinan Gezici,et al.  Stochastic signal design on the downlink of a multiuser communications system , 2012, 2012 IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[34]  Sinan Gezici,et al.  Noise-enhanced M-ary hypothesis-testing in the minimax framework , 2009, 2009 3rd International Conference on Signal Processing and Communication Systems.

[35]  H. Vincent Poor,et al.  Noise Enhanced Hypothesis-Testing in the Restricted Bayesian Framework , 2010, IEEE Transactions on Signal Processing.

[36]  François Gagnon,et al.  Error Rates of the Maximum-Likelihood Detector for Arbitrary Constellations: Convex/Concave Behavior and Applications , 2009, IEEE Transactions on Information Theory.

[37]  Sinan Gezici,et al.  Optimal Stochastic Signaling for Power-Constrained Binary Communications Systems , 2010, IEEE Transactions on Wireless Communications.