Resource allocation for NOMA wireless systems

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

New energy-efficient-user clustering and power allocation for NOMA in 5G millimeter-wave massive MIMO

In wireless communications, designing Data Rate (R) and Energy Efficient (EE) Beamspace-Multiple Inputs Multiple Outputs (BS-MIMO) for the millimetre wave communications are challenging research problems from the last decade. There are different solutions presented such as Fully-Digital (FD), BS-MIMO, and BS-MIMO Non-Orthogonal Multiple-Accesses (BS-MIMO NOMA). To address these problems, a novel mm-Wave communication is proposed with a user clustering approach called BS MIMO C-NOMA. It combines the advantages of NOMA and BS-MIMO at first then combines user-clustering to enhance throughput of BS-MIMO NOMA downlink-multi-user NOMA. The efficient users cluster has been proposed to improve the spectral, power efficiency, and the quantity of upheld clients can be bigger than the quantity of Radio Frequency (RF) chains in the time frequency assets as well compared to existing solutions. Iterative power optimization methods with less complexity have been designed to use a dynamic power allocation in order to achieve EE performance. In this thesis, a lens antenna was used in the experiment to assess the lens's effectiveness in maximizing the signal. The simulation outcomes demonstrate the R and EE in C-NOMA are much higher than in NOMA by a percentage of 4.2 % and 26 % respectively, It means R is increased 6 % with a maximum of 50 users in C-NOMA compared to NOMA, and an increase of 3.92 % with a maximum 100 users. Then, EE results with a maximum 50 users for FD, MIMO, NOMA and C-NOMA are 1.172, 7.249, 10.879 and 13.72, respectively. Therefore, EE effects against SNR with 32 users in C-NOMA is 10.11 the highest compared to FD, MIMO and NOMA with 1.23, 6.58 and 7.58, respectively. The EE results of maximum 100 users for FD, MIMO, NOMA and C-NOMA are 1.29, 4.08, 7.85 and 10.82, respectively. The results of EE in C-NOMA show an increase of 37 % compared to NOMA with a maximum of 100 users. Signal strength result is increased from -18 dBm to -11 dBm by using the lens. We believe the proposed C-NOMA method can achieve higher result of R and EE compared to NOMA

Energy-Efficient Power Allocation for MIMO-NOMA With Multiple Users in a Cluster

In this paper, energy-ef�cient power allocation (PA) is investigated for a multiple-input multiple-output non-orthogonal multiple access system with multiple users in a cluster. To ensure the quality of service (QoS) for the users, a minimum rate requirement is pre-de�ned for each user. Because of the QoS requirement, it is first necessary to determine whether the considered energy-efficiency (EE) maximization problem is feasible or not, by comparing the total transmit power with the required power for satisfying the QoS of the users. If feasible, a closed-form solution is provided for the corresponding sum rate maximization problem, and on this basis, the EE maximization problem is solved by applying non-convex fractional programming. Otherwise, a low-complexity user admission scheme is proposed, which admits users one by one following the ascending order of the required power for satisfying the QoS. Numerical results are presented to validate the effectiveness of the proposed energy-efficient PA strategy and user admission scheme

Securing Downlink Massive MIMO-NOMA Networks with Artificial Noise

In this paper, we focus on securing the confidential information of massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks by exploiting artificial noise (AN). An uplink training scheme is first proposed with minimum mean squared error estimation at the base station. Based on the estimated channel state information, the base station precodes the confidential information and injects the AN. Following this, the ergodic secrecy rate is derived for downlink transmission. An asymptotic secrecy performance analysis is also carried out for a large number of transmit antennas and high transmit power at the base station, respectively, to highlight the effects of key parameters on the secrecy performance of the considered system. Based on the derived ergodic secrecy rate, we propose the joint power allocation of the uplink training phase and downlink transmission phase to maximize the sum secrecy rates of the system. Besides, from the perspective of security, another optimization algorithm is proposed to maximize the energy efficiency. The results show that the combination of massive MIMO technique and AN greatly benefits NOMA networks in term of the secrecy performance. In addition, the effects of the uplink training phase and clustering process on the secrecy performance are revealed. Besides, the proposed optimization algorithms are compared with other baseline algorithms through simulations, and their superiority is validated. Finally, it is shown that the proposed system outperforms the conventional massive MIMO orthogonal multiple access in terms of the secrecy performance