Discrete-time Gaussian interfere-relay channel

In practical wireless relay communication systems, nondestination nodes are assumed to be idle not receiving signals while the relay sends messages to a particular destination node, which results in reduced bandwidth efficiency. To improve the bandwidth efficiency, we relax the idle assumption of non-destination nodes and assume that non-destination nodes may receive signals from sources. We note that the message relayed to a particular node in such a system gives rise to interference to other nodes. To study such a more general relay system, we consider, in this paper, a relay system in which the relay first listens to the source, then routes the source message to the destination, and finally produces interference to the destination in sending messages for other systems. We obtain capacity upper and lower bounds and study the optimal method to deal with the interference as well as the optimal routing schemes. From analytic results obtained, we find the conditions on which the direct transmission provides higher transmission rate. Next, we find the conditions, by numerical evaluation of the theoretical results, on which it is better for the destination to cancel and decode the interference. Also we find the optimal source power allocation scheme that achieves the lower bound depending on various channel conditions. We believe that the results provided in this paper will provide useful insights to system designers in strategically choosing the optimal routing algorithms depending on the channel conditions.

[1]  Anders Host-Madsen,et al.  On the capacity of wireless relaying , 2002, Proceedings IEEE 56th Vehicular Technology Conference.

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

[3]  Gregory W. Wornell,et al.  Distributed space-time-coded protocols for exploiting cooperative diversity in wireless networks , 2003, IEEE Trans. Inf. Theory.

[4]  Philip Levis,et al.  Practical, real-time, full duplex wireless , 2011, MobiCom.

[5]  Zhixiang Deng,et al.  Capacity of a class of relay channel with orthogonal components and non-causal channel state , 2013, 2013 IEEE International Symposium on Information Theory.

[6]  Thomas M. Cover,et al.  Network Information Theory , 2001 .

[7]  Shlomo Shamai,et al.  Bounds on the Capacity of the Relay Channel With Noncausal State at the Source , 2011, IEEE Transactions on Information Theory.

[8]  Young-Han Kim,et al.  On multiple user channels with state information at the transmitters , 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..

[9]  Panganamala Ramana Kumar,et al.  Towards an information theory of large networks: an achievable rate region , 2003, IEEE Trans. Inf. Theory.

[10]  Jun Cai,et al.  Semi-Distributed User Relaying Algorithm for Amplify-and-Forward Wireless Relay Networks , 2008, IEEE Transactions on Wireless Communications.

[11]  Luc Vandendorpe,et al.  Cooperative Relaying With State Available Noncausally at the Relay , 2009, IEEE Transactions on Information Theory.

[12]  Robert B. Ash,et al.  Information Theory , 2020, The SAGE International Encyclopedia of Mass Media and Society.

[13]  Gregory W. Wornell,et al.  Cooperative diversity in wireless networks: Efficient protocols and outage behavior , 2004, IEEE Transactions on Information Theory.

[14]  G. Kramer.  Models and Theory for Relay Channels with Receive Constraints , 2004 .

[15]  D. W. Bliss,et al.  Simultaneous Transmission and Reception for Improved Wireless Network Performance , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.

[16]  Mehul Motani,et al.  On Achievable Rates for the General Relay Channel , 2011, IEEE Transactions on Information Theory.

[17]  L. Goddard.  Information Theory , 1962, Nature.

[18]  Anders Høst-Madsen,et al.  Capacity bounds and power allocation for wireless relay channels , 2005, IEEE Transactions on Information Theory.