Abstract
To improve spectrum efficiency in wireless multiple‐input, multiple‐output (MIMO) systems, it is essential to mitigate channel interference. Opportunistic scheduling of multiusers and interference cancellation, such as adaptive multiuser detection, are two commonly used techniques. In this paper, we study the performance improvement of using jointly these techniques to enable the use of the same set of spreading codes to transmit two data streams in closed‐loop multiuser MIMO systems. We show that with the proposed scheduling schemes, the performance of an adaptive blind receiver is not degraded when the scheduled users share the same spreading code.
Highlights
1 Introduction The main obstacle limiting the performance of multiuser multiple-input, multiple-output (MU-MIMO) system and its spectral efficiency is the interference caused by MU-MIMO transmissions that share the same channel [1,2]
It is well known that the adaptive blind receiver (ABR) is based on the minimum output energy (MOE) criterion which searches the component of the desired signal that lies in a subspace orthogonal to the subspace spanned by the interference and noise simultaneously [12]
The performance achieved by Scheduler C with respect to Scheduler B is due to the fact that interference signal lie on subspaces orthogonal to the desired signal, and in this situation, a perfect data separation can be performed by ABR
Summary
The main obstacle limiting the performance of multiuser multiple-input, multiple-output (MU-MIMO) system and its spectral efficiency is the interference caused by MU-MIMO transmissions that share the same channel [1,2]. It is well known that the ABR is based on the minimum output energy (MOE) criterion which searches the component of the desired signal that lies in a subspace orthogonal to the subspace spanned by the interference and noise simultaneously [12] With this in mind, a careful selection of co-scheduled users that lie in orthogonal subspaces can reduce the interference level and improve the spectral efficiency by enabling spreading code reuse. In the work of [16], the authors show that the trajectory of the tap-weight vector, x1, depends on the eigenvalues of the equivalent autocorrelation matrix projected on a spreading code subspace orthogonal to the desired user This subspace approach analysis shows that the transient behavior of the MOE learning curve just depends on the largest K − 1 eigenvalues of the equivalent matrix autocorrelation when the adaptive component is initialized in x1[ 0] = 0 [17]. The error probability analysis of this receiver is presented
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