Interference Mitigation via Relaying

This paper studies the effectiveness of relaying for interference mitigation in an interference-limited communication scenario. We are motivated by the observation that in a cellular network, a relay node placed at the cell edge observes a combination of intended signal and inter-cell interference that is correlated with the received signal at a nearby destination, so a relaying link can effectively allow the antennas at the relay and at the destination to be pooled together for both signal enhancement and interference mitigation. We model this scenario by a multiple-input multiple-output (MIMO) Gaussian relay channel with a digital relay-to-destination link of finite capacity, and with correlated noise across the relay and destination antennas. Assuming a compress-and-forward strategy with Gaussian input distribution and quantization noise, we propose a coordinate ascent algorithm for obtaining a stationary point of the non-convex joint optimization of the transmit and quantization covariance matrices. For fixed input distribution, the globally optimum quantization noise covariance matrix can be found in closed-form using a transformation for the relay’s observation that simultaneously diagonalizes two conditional covariance matrices by congruence. For fixed quantization, the globally optimum transmit covariance matrix can be found via convex optimization. This paper further shows that such an optimized achievable rate is within a constant additive gap of the MIMO relay channel capacity. The optimal structure of the quantization noise covariance enables a characterization of the slope of the achievable rate as a function of the relaying link capacity. Moreover, this paper shows that the improvement in spatial degrees of freedom by MIMO relaying in the presence of noise correlation is related to the aforementioned slope via a connection to the deterministic relay channel.

[1]  Shlomo Shamai,et al.  Communication via decentralized processing , 2005, ISIT.

[2]  Sae-Young Chung,et al.  Aligned interference neutralization and the degrees of freedom of the 2 × 2 × 2 interference channel , 2010, 2011 IEEE International Symposium on Information Theory Proceedings.

[3]  Franz Rellich,et al.  Perturbation Theory of Eigenvalue Problems , 1969 .

[4]  Aitor del Coso,et al.  Distributed compression for MIMO coordinated networks with a backhaul constraint , 2009, IEEE Transactions on Wireless Communications.

[5]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[6]  Michael Gastpar,et al.  The distributed Karhunen-Loeve transform , 2002, 2002 IEEE Workshop on Multimedia Signal Processing..

[7]  T. Markham,et al.  A Generalization of the Schur Complement by Means of the Moore–Penrose Inverse , 1974 .

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

[9]  Young-Han Kim Capacity of a Class of Deterministic Relay Channels , 2008, IEEE Trans. Inf. Theory.

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

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

[12]  Wei Yu,et al.  A DoF analysis of compress-and-forward in MIMO Gaussian relay channel with correlated noises , 2015, 2015 IEEE 14th Canadian Workshop on Information Theory (CWIT).

[13]  Abbas El Gamal,et al.  Network Information Theory , 2021, 2021 IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT).

[14]  Calyampudi Radhakrishna Rao,et al.  Linear Statistical Inference and its Applications , 1967 .

[15]  Wei Yu,et al.  Optimized MIMO transmission and compression for interference mitigation with cooperative relay , 2015, 2015 IEEE International Conference on Communications (ICC).

[16]  Andrea J. Goldsmith,et al.  Study of Gaussian Relay Channels with Correlated Noises , 2010, IEEE Transactions on Communications.

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

[18]  Armin Wittneben,et al.  Spectral efficient protocols for half-duplex fading relay channels , 2007, IEEE Journal on Selected Areas in Communications.

[19]  Syed Ali Jafar,et al.  The Effect of Noise Correlation in Amplify-and-Forward Relay Networks , 2009, IEEE Transactions on Information Theory.

[20]  Aitor del Coso,et al.  Compress-and-Forward Cooperative MIMO Relaying With Full Channel State Information , 2010, IEEE Transactions on Signal Processing.

[21]  Chao Tian,et al.  Remote Vector Gaussian Source Coding With Decoder Side Information Under Mutual Information and Distortion Constraints , 2009, IEEE Transactions on Information Theory.

[22]  Wei Yu,et al.  Capacity of the Gaussian Relay Channel with Correlated Noises to Within a Constant Gap , 2012, IEEE Communications Letters.

[23]  Young-Han Kim,et al.  The Approximate Capacity of the MIMO Relay Channel , 2014, IEEE Transactions on Information Theory.

[24]  Suhas N. Diggavi,et al.  Approximate Capacity of a Class of Gaussian Interference-Relay Networks , 2011, IEEE Transactions on Information Theory.

[25]  C. E. M. Pearce,et al.  Some New Bounds for Singular Values and Eigenvalues of Matrix Products , 2000, Ann. Oper. Res..