Sparse Linear Precoders for Mitigating Nonlinearities in Massive MIMO

Dealing with nonlinear effects of the radio-frequency(RF) chain is a key issue in the realization of very large-scale multi-antenna (MIMO) systems. Achieving the remarkable gains possible with massive MIMO requires that the signal processing algorithms systematically take into account these effects. Here, we present a computationally efficient linear precoding method satisfying the requirements for low peak-to-average power ratio (PAPR) and low-resolution D/A-converters (DACs). The method is based on a sparse regularization of the precoding matrix and offers advantages in terms of precoded signal PAPR as well as processing complexity. Through simulation, we find that the method substantially improves conventional linear precoders.

On the Optimal Power Allocation and User Pairing for Uplink Non-Orthogonal Multiple Access Networks

In this paper, we derive the optimal user pairing and power allocation in uplink non orthogonal multiple access (NOMA) networks. The optimal power allocation that maximizes the sum rate is found for two user NOMA networks, while ensuring that the individual rates of NOMA users are at least equal to those they would achieve with orthogonal multiple access (OMA). Next, we prove that in a 2K user network and when the optimal power allocation is used, the optimal pairing reduces to pairing the user with index k to the user with index 2K-k+1, i.e., the user with the best channel condition and the one with the worst channel condition are paired together, the second best with the second worst and so on. Finally, the expressions of the corresponding optimal NOMA power coefficients are derived analytically for networks with more than two users. With these expressions at hand, our simulation results validate the superiority of NOMA over OMA in terms of sum rates.