In this paper, we consider resource allocation in the the fourth generation
(4G) cellular uplink. Our focus is on 4G cellular systems that conform to the
3GPP LTE standard and its enhancements, which are expected to be the
predominant 4G cellular systems. In order to cater to an ever-increasing user
traffic, the 4G uplink allows for precoded multi-stream (precoded MIMO)
transmission from each scheduled user and also allows multi-user (MU)
scheduling wherein multiple users can be assigned the same time-frequency
resource.
Solving linear regression problems based on the total least-squares (TLS)
criterion has well-documented merits in various applications, where
perturbations appear both in the data vector as well as in the regression
matrix. However, existing TLS approaches do not account for sparsity possibly
present in the unknown vector of regression coefficients. On the other hand,
sparsity is the key attribute exploited by modern compressive sampling and
variable selection approaches to linear regression, which include noise in the
data, but do not account for perturbations in the regression matrix.