title: Resource allocation for NOMA wireless systems
Creators: name: Zeng, Ming
Publisher: name: Memorial University of Newfoundland
Year: 2020

description: Power-domain non-orthogonal multiple access (NOMA) has been widely recognized as
 a promising candidate for the next generation of wireless communication systems. By
 applying superposition coding at the transmitter and successive interference cancellation
 at the receiver, NOMA allows multiple users to access the same time-frequency resource
 in power domain. This way, NOMA not only increases the system’s spectral and energy
 efficiencies, but also supports more users when compared with the conventional orthogonal
 multiple access (OMA). Meanwhile, improved user fairness can be achieved by NOMA.
 Nonetheless, the promised advantages of NOMA cannot be realized without proper
 resource allocation. The main resources in wireless communication systems include time,
 frequency, space, code and power. In NOMA systems, multiple users are accommodated
 in each time/frequency/code resource block (RB), forming a NOMA cluster. As a result,
 how to group the users into NOMA clusters and allocate the power is of significance. A
 large number of studies have been carried out for developing efficient power allocation
 (PA) algorithms in single-input single-output (SISO) scenarios with fixed user clustering.
 To fully reap the gain of NOMA, the design of joint PA and user clustering is required.
 Moreover, the study of PA under multiple-input multiple-output (MIMO) systems still
 remains at an incipient stage. In this dissertation, we develop novel algorithms to allocate
 resource for both SISO-NOMA and MIMO-NOMA systems.
 More specifically, Chapter 2 compares the system capacity of MIMO-NOMA with
 MIMO-OMA. It is proved analytically that MIMO-NOMA outperforms MIMO-OMA in terms of both sum channel capacity and ergodic sum capacity when there are multiple
 users in a cluster. Furthermore, it is demonstrated that the more users are admitted to
 a cluster, the lower is the achieved sum rate, which illustrates the tradeoff between the
 sum rate and maximum number of admitted users.
 Chapter 3 addresses the PA problem for a general multi-cluster multi-user MIMONOMA
 system to maximize the system energy efficiency (EE). First, a closed-form solution
 is derived for the corresponding sum rate (SE) maximization problem. Then, the EE
 maximization problem is solved by applying non-convex fractional programming.
 Chapter 4 investigates the energy-efficient joint user-RB association and PA problem
 for an uplink hybrid NOMA-OMA system. The considered problem requires to jointly
 optimize the user clustering, channel assignment and power allocation. To address this
 hard problem, a many-to-one bipartite graph is first constructed considering the users
 and RBs as the two sets of nodes. Based on swap matching, a joint user-RB association
 and power allocation scheme is proposed, which converges within a limited number of
 iterations. Moreover, for the power allocation under a given user-RB association, a low complexity
 optimal PA algorithm is proposed.
 Furthermore, Chapter 5 focuses on securing the confidential information of massive
 MIMO-NOMA networks by exploiting artificial noise (AN). An uplink training scheme is
 first proposed, and on this basis, the base station precodes the confidential information
 and injects the AN. Following this, the ergodic secrecy rate is derived for downlink transmission.
 Additionally, PA algorithms are proposed to maximize the SE and EE of the
 system.
 Finally, conclusions are drawn and possible extensions to resource allocation in NOMA
 systems are discussed in Chapter 6.