2010/10/13 by Behrang Nosrat-Makouei, Jeffrey G. Andrews, Robert W. Heath Jr +1
Computer Science · Engineering · Mathematics · #Advanced MIMO Systems Optimization #Algorithm #Antenna (radio) #Beamforming #Channel (broadcasting) #Channel state information #Combinatorics #Computer science #Cooperative Communication and Network Coding #Energy Harvesting in Wireless Networks #Interference (communication) #MIMO #Mathematics #Precoding #Spatial correlation #Telecommunications #Topology (electrical circuits) #Transmission (telecommunications) #Wireless #cs.IT #math.IT
paper · pdf · doi:10.1109/tsp.2011.2124458
published as IEEE Transactions on Signal Processing, vol.59, no.6, pp. 2783-2794, June 2011 · 21 pages, 7 figures, submitted to IEEE Transactions on Signal Processing
arxiv created 2010/10/13 · openalex publication_date 2011/03/15 · arxiv updated 2012/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Interference alignment (IA), given uncorrelated channel components and perfect channel state information, obtains the maximum degrees of freedom in an interference channel. Little is known, however, about how the sum rate of IA behaves at finite transmit power, with imperfect channel state information, or antenna correlation. This paper provides an approximate closed-form signal-to-interference-plus-noise-ratio (SINR) expression for IA over multiple-input-multiple-output (MIMO) channels with imperfect channel state information and transmit antenna correlation. Assuming linear processing at the transmitters and zero-forcing receivers, random matrix theory tools are utilized to derive an approximation for the postprocessing SINR distribution of each stream for each user. Perfect channel knowledge and i.i.d. channel coefficients constitute special cases. This SINR distribution not only allows easy calculation of useful performance metrics like sum rate and symbol error rate, but also permits a realistic comparison of IA with other transmission techniques. More specifically, IA is compared with spatial multiplexing and beamforming and it is shown that IA may not be optimal for some performance criteria.