An Efficient Rank Adaptation Algorithm for Cellular MIMO Systems with IRC Receivers

Abstract

Multiple transmit and receive antennas introduce additional degrees of freedom, which can be used to increase the number of spatial channels between a transmitter-receiver pair. Alternately, the additional degrees of freedom can be used to improve the interference resilience property with the help of linear interference rejection combining (IRC) receivers. Typically, rank adaptation algorithms are aimed at balancing the trade-off between increasing the spatial gain, and improving the interference resilience property. In this paper, we propose an efficient and computationally effective rank adaptation algorithm based on an estimate of the mean signal-to-interference-plus-noise ratio (SINR) at an IRC receiver; wherein, we use results from random matrix theory to derive the expression for the mean post-IRC SINR in the presence of interferers with unequal powers. The performance of the proposed algorithm is analysed through system level simulations. The results are found to be comparable to the optimum performance, and match closely to that of a more complex existing rank adaptation method.

A centralized inter-cell rank coordination mechanism for 5G systems

“…More specifically, using results from RMT to analyse the asymptotic behaviour of the eigenvalues of Σ appearing in (3), it is proposed in [13] that the post-MMSE SINR can be approximates asγ…”

A distributed interference-aware rank adaptation algorithm for local area MIMO systems with MMSE receivers

“…Most existing rank adaptation algorithm (e.g. [6]- [8]) selects the rank that maximizes the individual cell's throughput without regard for the interference generated at the interfered receivers. In contrast, we propose the cells to select the rank that will instead maximize the throughput multiplied by an weighting factor.…”

“…Utilizing some of the MIMO spatial DoFs for suppressing interfering streams with the help of MMSE receivers results in better system level performance compared to using all spatial DoFs for transmitting/receiving the desired data streams [5]. Rank adaptation (RA) algorithms are aimed at balancing the tradeoff between increasing the spatial diversity gain by transmitting over multiple transmission layers (or ranks), and improving the interference resilience by allowing some of the spatial DoFs for interference rejection [6]- [8].…”

“…Hence, the rank decision that is optimal in a certain transmission interval may lead to disruptive performance in the following interval due to the unpredictability of the large variation in the traffic pattern. A fast-adaptive RA scheme that circumvents the need to estimate the ICM by estimating the post MMSE SINR directly from the interference powers has been proposed by the authors of this paper in [8].…”

“…In this work, we extend the RA algorithm in our earlier work [8] to include a simple and efficient method of 'interference-awareness'. The proposed algorithm involves implicit coordination among multiple coexisting cells.…”