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NON-ORTHOGONAL MULTIPLE ACCESS (NOMA)
The 4G network has seen considerable growth because of the distributions of the pro-fatable LTE networks worldwide. The need to develop next generation communication system such as the 5G neywork stems from the growing demand of connected mobile devices and progressive data traffic [33]. For this reason, the advancement of 5G has gained momentum in research and development. 5G requirements can be identified to sufficiently support wireless communication. Compared to LTE networks, 5G should have the capability to provide 1000-fold gains in system capacity, peak data rate of fiber-like 10 Gbps and 1 Gbps for low mobility and high mobility, respectively, with at least 100 billion devices connections, low energy consumption and latency [34, 35].
In order to comply with these demanding requirements, the 5G network architec-ture must differentiate itself from LTE, whilst progressing current OMA systems. To this end, several non-orthogonal multiple access schemes are under evaluation for 5G requirements. For example, sparse code multiple access (SCMA) is regarded as a multi-carrier form of NOMA [4], which primarily focus on creating a factor graph matrix for mapping users to the limited subcarriers. As stipulated in Release 13, 3GPP initiated a study on downlink multiuser superposition transmission (MUST) for LTE [2]. Its main contention is scrutinize multi-user non-orthogonal transmission and the proposal of ad-vanced receivers [3]. In addition to its application in cellular networks, NOMA has also been applied to other types of wireless networks, because of its superior spectral effi-ciency. For example, a version of NOMA, termed Layer Division Multiplexing (LDM), has been proposed for the next general digital TV standard ATSC 3.0 [5].
This thesis focuses on the power domain non-orthogonal multiple access scheme and will be denoted simply by NOMA throughout this dissertation. The concept is proposed in [6–8]. This scheme applies superposition coding to superpose multiple UEs signals at the transmitter side. By relying on the technological improvement of end-user receivers and their processing proficiencies, the implementation of Successive Interference Can-cellation (SIC) becomes crucial for NOMA which allows the separation and decoding of multi-user signals at the receiver side [9]. In figure 1.1, an illustration for single-cell OMA and NOMA in the power (as well as frequency) domain can be seen. They differentiate from each other in that NOMA permits two or more users to be attributed the same subband whilst providing a proper amount of power to each respective user, whereas each subband in OMA can only be assigned to one user.
RESOURCE AND POWER ALLOCATION IN NOMA
Unlike OMA, multiple users in NOMA are allowed to simultaneously share the same subband, and one user may need to be multiplexed on several subbands, with different cohabiting users and in various multiplexing orders within each subband. Even after SIC processing, co-channel interference in NOMA is non-negligible. Therefore, radio resource optimization in the case of NOMA is not straightforward. In this sense, several design aspects should be taken into consideration, such as user pairing and multiuser frequency scheduling, power allocation among subbands, power repartition between scheduled users within a subband, etc., as well as the interaction of these different design issues. Some algorithms and schemes are proposed to optimize the channel and power allocation for NOMA dowlink and uplink [63–65].
Most of the previous approaches address the radio resource optimization problems in NOMA by making assumptions to reduce the complexity of the optimization process, e.g., assuming uniform power allocation, predefining fixed groups of users or/and chan-nels before the optimization process. Some researches also consider splitting the difficult optimization procedure into several problems considered easier to be solved. By doing so, the overall problem becomes tractable, however, the optimality is sacrificed, e.g., splitting the joint channel and power allocation into two separate steps: channel alloca-tion and power allocation.
INTER-SUBBAND POWER ALLOCATION
The total bandwidth granted to serve users is divided over a fixed number of subbands. Every subband is then attributed a fraction of the available transmit power that the base station is allowed to use. An equal repartition is the simplest way to divide the power among subbands since it reduces the complexity of the scheduling process; therefore, a great number of papers dealing with NOMA consider this repartition [68, 69].
In [70], the performance of downlink NOMA with wideband and subband frequency scheduling is evaluated under wide-area cellular system configurations. Power alloca-tion is done such that the amount of power attributed to a subband is common to all subbands, and intra-subband power repartition between each pair of scheduled users is done as a subsequent stage using fractional transmit power allocation (FTPA) [71]. In [40], the throughput improvement using non-orthogonal superposition of users on top of an uplink OFDMA-based system is studied. All mobile users are considered to be transmitting signals at the same power level. Optimized scheduling techniques are proposed to maximize the system throughput. In this sense, a cost function is assigned to each possible pair of users and the Hungarian method is used to solve the problem of user pairing. Cost functions are chosen so as to optimize either the sum-rate or the weighted sum-rate. These two scheduling techniques are shown to provide significant improvements in sum-rates and cell-edge rates compared to orthogonal signaling.
In [43], a non-orthogonal multiuser beamforming system is proposed for improving the system capacity. Degradation in the sum capacity due to inter-cluster interference and inter-user interference is assessed, leading to a clustering and power allocation algorithm that aims at reducing interference and improving capacity. Clustering is done by selecting two users to be paired together, in the same beamforming vector, as having a large gain difference and a high correlation. In addition, power is equally divided between clusters, and then allocated among scheduled users in each cluster, in such a way to maximize the sum capacity, while guaranteeing a minimum capacity for the weakest user.
Table of contents :
1 Background theory and literature review
1.1 Multiple access techniques in LTE and Beyond
1.1.1 Orthogonal Multiple Access
1.1.2 Non-orthogonal multiple access (NOMA)
1.2 Resource allocation in downlink cellular networks
1.2.1 Classic utility functions
1.2.2 Resource allocation in OFDM networks
1.2.3 Resource and power allocation in NOMA
1.3 Conclusion
2 Techniques for the minimization of the occupied bandwidth in a NOMA system
2.1 Formulation of the resource allocation problem
2.2 Description of the proposed algorithm for resource allocation
2.2.1 Initialization and priority assignment
2.2.2 Subband assignment and user pairing
2.2.3 Multi-user power allocation
2.2.4 Adaptive switching to orthogonal signaling
2.2.5 Data rate estimation and control mechanism
2.3 Comparison with OMA
2.4 Numerical Results
2.4.1 Performance evaluation
2.4.2 Simulation Results
2.5 Conclusion
3 Proposals to improve the PF scheduler for a NOMA system
3.1 Conventional Proportional Fairness (PF) scheduling scheme
3.2 Improving the PF metric at the level of user scheduling
3.2.1 Proposed Weighted NOMA-based Proportional Fairness Scheduler (WNOPF)
3.2.2 Proposed Weighted OMA-based Proportional Fairness Scheduler (WOPF)
3.2.3 Proposed scheduling metric for the first scheduling slot
3.2.4 Service Differentiation
3.2.5 Complexity Assessment
3.3 Performance analysis of the proposed weighted PF metrics
3.3.1 System Model Parameters
3.3.2 Performance Evaluation
3.4 Proposals to improve the PF scheduler at the level of power allocation .
3.4.1 Proposed power allocation scheme
3.4.2 Performance analysis of the iterative waterfilling-based PF scheduler
3.4.3 Evaluation of the Computational Complexity
3.5 Conclusion
4 New throughput and/or fairness maximization metrics
4.1 Dependency of throughput on channel gain difference in NOMA .
4.2 Dependency of throughput on channel gain values for a NOMA system
4.3 Flexible Throughput vs. Fairness Metrics
4.3.1 Step 1: Initialization and Priority Assignment
4.3.2 Step 2: User Selection
4.3.3 Step 3: Subband Assignment
4.3.4 Step 4: User Pairing
4.3.5 Adaptive switching to OMA
4.4 Complexity Assessment
4.5 Performance analysis of the throughput vs. fairness maximization metrics
4.6 Conclusion
5 Hybrid Broadcast-Unicast system
5.1 Formulation of the resource allocation problem
5.2 A-posteriori broadcast allocation techniques
5.2.1 Broadband loss minimization metric (BLMM)
5.2.2 Channel-based allocation metric (CBAM)
5.3 A-priori broadcast allocation techniques
5.3.1 Equal-power based broadcast allocation technique (EPBAT)
5.3.2 Equal-power based hybrid broadcast-broadband allocation technique (EPHBAT)
5.3.3 Waterfilling-based broadcast allocation technique (WBAT)
5.4 Benchmarking schemes for hybrid broadcast-broadband systems .
5.5 Performance evaluation
5.6 Conclusion .
