Error exponents for two-hop Gaussian multiple source-destination relay channels

We investigate the two-hop multiple source-destination relay channels using the rate splitting transmission scheme where each source can split its message into private and public parts. We determine the system error probability via integrated exponent function under amplify-and-forward (AF) and decode-and-forward (DF) relay strategies. The most significant difference between AF and DF system error probability evaluations lies in a minimized cut-set bound of transmission rate under the DF strategy. There are many cases among transmission rate intervals for different system parameters (e.g. transmit power) and it is extremely complex to derive the system error probability for the DF strategy. We obtain a relatively simple result, which unifies various cases by a few expressions. Numerical results show that the system error probability decreases with the increase of the public message. Moreover, in order to draw deep insight into the reliability requirement of each source node in this network, we provide the error exponent region (EER) for different source nodes to show the trade-off of error probability among source nodes.

[1]  R. Gallager Information Theory and Reliable Communication , 1968 .

[2]  Michael Gastpar,et al.  Cooperative strategies and capacity theorems for relay networks , 2005, IEEE Transactions on Information Theory.

[3]  E. Meulen,et al.  Three-terminal communication channels , 1971, Advances in Applied Probability.

[4]  Laurence B. Milstein,et al.  CDMA overlay situations for microcellular mobile communications , 1995, IEEE Trans. Commun..

[5]  C. Shannon Probability of error for optimal codes in a Gaussian channel , 1959 .

[6]  Neri Merhav,et al.  Error Exponents of Optimum Decoding for the Interference Channel , 2010, IEEE Transactions on Information Theory.

[7]  Abbas El Gamal,et al.  Lecture Notes on Network Information Theory , 2010, ArXiv.

[8]  D. R. Fulkerson,et al.  Maximal Flow Through a Network , 1956 .

[9]  Te Sun Han,et al.  A new achievable rate region for the interference channel , 1981, IEEE Trans. Inf. Theory.

[10]  Robert G. Gallager,et al.  A perspective on multiaccess channels , 1984, IEEE Trans. Inf. Theory.

[11]  Toby Berger,et al.  Review of Information Theory: Coding Theorems for Discrete Memoryless Systems (Csiszár, I., and Körner, J.; 1981) , 1984, IEEE Trans. Inf. Theory.

[12]  János Körner,et al.  Universally attainable error exponents for broadcast channels with degraded message sets , 1980, IEEE Trans. Inf. Theory.

[13]  Abbas El Gamal,et al.  Capacity theorems for the relay channel , 1979, IEEE Trans. Inf. Theory.

[14]  Hyundong Shin,et al.  Random coding error exponent for dual-hop nakagami-m fading channels with amplify-and-forward relaying , 2009, IEEE Communications Letters.

[15]  Achilleas Anastasopoulos,et al.  Error Exponent Regions for Gaussian Broadcast and Multiple-Access Channels , 2008, IEEE Transactions on Information Theory.

[16]  Hyundong Shin,et al.  Amplify-and-forward two-way relay channels: Error exponents , 2009, 2009 IEEE International Symposium on Information Theory.

[17]  Elwyn R. Berlekamp,et al.  Lower Bounds to Error Probability for Coding on Discrete Memoryless Channels. II , 1967, Inf. Control..

[18]  Jun Chen,et al.  Performance of wideband CDMA systems with complex spreading and imperfect channel estimation , 2001, IEEE J. Sel. Areas Commun..

[19]  Urbashi Mitra,et al.  Multihopping Strategies: An Error-Exponent Comparison , 2007, 2007 IEEE International Symposium on Information Theory.

[20]  Neri Merhav,et al.  Error Exponents for Broadcast Channels With Degraded Message Sets , 2011, IEEE Transactions on Information Theory.

[21]  Imre Csiszár,et al.  Information Theory - Coding Theorems for Discrete Memoryless Systems, Second Edition , 2011 .

[22]  I. N. Sanov On the probability of large deviations of random variables , 1958 .