Low complexity greedy, genetic and hybrid user scheduling algorithms for multiuser MIMO systems with successive zero‐forcing

Abstract

In this paper, we consider low complexity user scheduling algorithms for multiuser multiple‐input multiple‐output systems employing successive zero‐forcing precoding. Optimal scheduling involves an exhaustive search (ES), which is prohibitively complex. Greedy algorithms (GrAs) with heuristic scheduling metrics achieve performance close to that of the ES. Meanwhile, genetic algorithms (GAs) are a rapid suboptimal option of optimising utility (e.g. scheduling) metrics. Herein, we evaluate the performance and complexity of greedy and genetic scheduling algorithms for successive zero‐forcing. We also propose and evaluate two hybrid algorithms combining the traits of the GrA and GA. The algorithms' performance is assessed through a series of computer simulations. We demonstrate both the GrA and GA achieve a near‐optimal sum rate with low complexity, whereas the hybrid algorithms further enhance the GrA and GA performance without an increase in the order of complexity.Copyright © 2012 John Wiley & Sons, Ltd.

Improved and weighted sum rate maximization for successive zero-forcing in multiuser MIMO systems

“…In this scenario, with the limited user pool to choose from, it is better to transmit to less than the maximum number about 43-57% of the time. We made a similar observation in [8,9] for unweighted sum rates when scheduling to small user pools. The likelihood of scheduling the maximum possible number of users increases with the size of the user pool K, due to multiuser diversity and the increased chance of finding users with orthogonal channels.…”

“…However, we have found two main deficiencies with the existing method. The first was discovered during our related work on scheduling for BD in [7] and SZF in [8,9]. We found in one case at high SNR, our scheduling algorithms for SZF performed significantly worse than the equivalent ones for BD at the same SNR.…”

“…In [8,9], we calculate the complexity of the method for calculating covariance matrices from [5] in terms of the number of flops (floating point operations) required. It was found that the existing method has complexity order O(K 0 M 3 T ), assuming all users have the same number of receive antennas N. That is also the order of finding the null space basis vectors for SZF.…”

“…In Figure 3, we present a somewhat more complicated scenario more related to our scheduling work in [8,9]. In this case, we consider a larger user pool size of K = 16 with M T = 8.…”

“…The primary impetus for this work was based on observations made during our related work on scheduling for BD [7] and SZF [8,9]. We found that, unexpectedly, the performance of our SZF scheduling algorithms was worse than for BD at high SNR.…”