A MIMO-NOMA Framework With Complex-Valued Power Coefficients

Abstract

This paper proposes a widely linear processing framework for multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) downlink systems. In the framework, a widely linear MIMO-NOMA (WL-MIMO-NOMA) model is derived by assuming that the base station transmits real-valued downlink signals. WL-MIMO-NOMA adopts complex-valued power allocation coefficients to stagger the user signals in phase. The main features of WL-MIMO-NOMA are the following: first, in general case, WL-MIMO-NOMA can remove all the inter-cluster interference and at least half of the intra-cluster interference; and second, in user pairing case, both interferences can be completely eliminated. This is distinct from the existing work where real power coefficients are used, which cannot guarantee the complete separation of the paired user signals because the signals transmitted to the paired users are overlapped in phase. In addition, the closed-form expressions of outage probabilities are derived. The phase difference of the complex power coefficients is optimized to minimize the outage probability. It is proven that, with the optimal phase difference, successive interference cancellation is unnecessary in user pairing case. Finally, the framework is extended to the mixed case of real/complex circular signals. Simulation results show that the proposed framework outperforms the existing work, and the numerical results agree well with the analytical analysis.

Zero-Forcing-Based Downlink Virtual MIMO–NOMA Communications in IoT Networks

“…In regard to the similar systems, a power allocation scheme was further proposed for maximizing the ergodic sum rate in [25]. Furthermore, a linear MIMO-NOMA model with complex-valued power coefficients was derived by considering multi-stream transmissions in [26]. Unlike [24]- [26] that assume independent fading channels, the joint design of power allocation and receive beamforming was developed for single-stream MIMO-NOMA systems with receive correlation in [27].…”

“…Furthermore, a linear MIMO-NOMA model with complex-valued power coefficients was derived by considering multi-stream transmissions in [26]. Unlike [24]- [26] that assume independent fading channels, the joint design of power allocation and receive beamforming was developed for single-stream MIMO-NOMA systems with receive correlation in [27]. Whereas, the outage performance of multi-stream MIMO-NOMA systems was examined in [28] by considering transmit correlation.…”

“…The last constraint can thus be rewritten as θ m,k ≤ ζ m,i /(2 Rm,i − 1) − i−1 l=1 ζ m,l equivalently. As a consequence, (25) can be converted into a convex optimization problem with linear constraints, as shown in (26) at the top of the following page. Alternatively, the optimization problem can be solved by using KKT conditions.…”

“…With regard to (26), the associated Lagrangian is written as (80) at the top of the following page, where η m , ξ m,k , ς m,k and ω m,k,i refer to the KKT multipliers. On the basis of (80), the optimal solution should satisfy the necessary KKT conditions (81)-(86) as follows…”