Impact of interference correlation on the decoding error statistics

Scenarios where interference is correlated with useful signals are often met in communications systems. Interestingly, even though uncorrelated interference mitigation has been extensively studied by the literature, there is still lack of insight into how correlation can affects information transfer processes. In this paper we formally address this issue and show how a positive correlation between the interference and useful signal power introduces a non-intuitive increase in the diversity gain, improving the error statistics with respect to the uncorrelated case. In contrast, the diversity gain decreases under negative correlation, degrading the system performance. These findings are confirmed by simulations for scenarios of practical relevance.

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

[2]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[3]  Cihan Tepedelenlioglu,et al.  A Representation for the Symbol Error Rate Using Completely Monotone Functions , 2013, IEEE Transactions on Information Theory.

[4]  G. Casella,et al.  Statistical Inference , 2003, Encyclopedia of Social Network Analysis and Mining.

[5]  Sumit Kundu,et al.  Outage and BER analysis of cellular CDMA for integrated services with correlated signal and interference , 2003, IEEE Communications Letters.

[6]  L. Halsted Coherent Echo Modulation and Detection , 1966 .

[7]  Ali H. Sayed,et al.  Adaptive Filters , 2008 .

[8]  A.M. Haimovich,et al.  Impact of Channel Estimation on Ultra-Wideband System Design , 2007, IEEE Journal of Selected Topics in Signal Processing.

[9]  Christian Oberli,et al.  Intercarrier Interference in OFDM: A General Model for Transmissions in Mobile Environments with Imperfect Synchronization , 2009, EURASIP J. Wirel. Commun. Netw..

[10]  M. Haenggi,et al.  Interference in Large Wireless Networks , 2009, Found. Trends Netw..

[11]  Junyi Li,et al.  Network densification: the dominant theme for wireless evolution into 5G , 2014, IEEE Communications Magazine.

[12]  A. Goldsmith,et al.  A unified approach for calculating error rates of linearly modulated signals over generalized fading channels , 1998, ICC '98. 1998 IEEE International Conference on Communications. Conference Record. Affiliated with SUPERCOMM'98 (Cat. No.98CH36220).

[13]  Holger Boche,et al.  The Structure of General Interference Functions and Applications , 2008, IEEE Transactions on Information Theory.

[14]  Norman C. Beaulieu,et al.  A Closed-Form BER Expression for BPSK Using MRC in Correlated CCI and Rayleigh Fading , 2007, IEEE Transactions on Communications.

[15]  Norman C. Beaulieu,et al.  New simple closed-form BER expressions for MRC diversity BPSK in correlated rayleigh fading and CCI , 2009, IEEE Transactions on Communications.

[16]  Christian Oberli ML-based Tracking Algorithms for MIMO-OFDM , 2007, IEEE Transactions on Wireless Communications.

[17]  Luca Rugini,et al.  Probability of Error of Linearly Modulated Signals with Gaussian Cochannel Interference in Maximally Correlated Rayleigh Fading Channels , 2010, EURASIP J. Wirel. Commun. Netw..