On Codebook Information for Interference Relay Channels With Out-of-Band Relaying

A standard assumption in network information theory is that all nodes are informed at all times of the operations carried out (e.g., of the codebooks used) by any other terminal in the network. In this paper, information theoretic limits are sought under the assumption that, instead, some nodes are not informed about the codebooks used by other terminals. Specifically, capacity results are derived for a relay channel in which the relay is oblivious to the codebook used by the source (oblivious relaying), and an interference relay channel with oblivious relaying and in which each destination is possibly unaware of the codebook used by the interfering source (interference-oblivious decoding). Extensions are also discussed for a related scenario with standard codebook-aware relaying but interference-oblivious decoding. The class of channels under study is limited to out-of-band (or “primitive”) relaying: Relay-to-destinations links use orthogonal resources with respect to the transmission from the source encoders. Conclusions are obtained under a rigorous definition of oblivious processing that is related to the idea of randomized encoding. The framework and results discussed in this paper suggest that imperfect codebook information can be included as a source of uncertainty in network design along with, e.g., imperfect channel and topology information.

[1]  Prakash Narayan,et al.  Reliable Communication Under Channel Uncertainty , 1998, IEEE Trans. Inf. Theory.

[2]  Sergio Verdú,et al.  On limiting characterizations of memoryless multiuser capacity regions , 1993, IEEE Trans. Inf. Theory.

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

[4]  Sae-Young Chung,et al.  When is compress-and-forward optimal? , 2010, 2010 Information Theory and Applications Workshop (ITA).

[5]  Ron Dabora,et al.  On the Role of Estimate-and-Forward With Time Sharing in Cooperative Communication , 2006, IEEE Transactions on Information Theory.

[6]  Aydano B. Carleial,et al.  Interference channels , 1978, IEEE Trans. Inf. Theory.

[7]  Xiugang Wu,et al.  Asymptotic Equipartition Property of Output when Rate is above Capacity , 2009, ArXiv.

[8]  Andrea J. Goldsmith,et al.  On the capacity of the interference channel with a relay , 2008, 2008 IEEE International Symposium on Information Theory.

[9]  Elza Erkip,et al.  Achievable Rates for the Gaussian Interference Relay Channel , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

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

[11]  Wei Yu,et al.  Capacity of a Class of Modulo-Sum Relay Channels , 2007, IEEE Transactions on Information Theory.

[12]  Jeffrey G. Andrews,et al.  Femtocell networks: a survey , 2008, IEEE Communications Magazine.

[13]  Young-Han Kim,et al.  Coding Techniques for Primitive Relay Channels , 2008 .

[14]  Abbas El Gamal,et al.  Bounds on capacity and minimum energy-per-bit for AWGN relay channels , 2006, IEEE Transactions on Information Theory.

[15]  Sennur Ulukus,et al.  A new upper bound on the capacity of a class of primitive relay channels , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[16]  Young-Han Kim,et al.  Capacity of a Class of Deterministic Relay Channels , 2006, 2007 IEEE International Symposium on Information Theory.

[17]  Gerhard Kramer,et al.  Topics in Multi-User Information Theory , 2008, Found. Trends Commun. Inf. Theory.

[18]  David Tse,et al.  Interference Mitigation Through Limited Receiver Cooperation , 2009, IEEE Transactions on Information Theory.

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

[20]  Wei Kang,et al.  Capacity of a Class of Diamond Channels , 2008, IEEE Transactions on Information Theory.

[21]  Sae-Young Chung,et al.  Noisy Network Coding , 2010, IEEE Transactions on Information Theory.

[22]  Shlomo Shamai,et al.  Communication Via Decentralized Processing , 2005, IEEE Transactions on Information Theory.

[23]  Shlomo Shamai,et al.  The empirical distribution of good codes , 1997, IEEE Trans. Inf. Theory.