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On the Limits of Network Densification and the Cell Paradigm Shift
The limit to how far network densification can go is not necessarily bound to be less or equal to the user deployment density. Provided that idle mode capability is enabled [71, 72], many studies have pushed the ratio of deployed transmission nodes to UEs requiring network access beyond the intuitive unity limit [73–75]. The fundamental limit to network densification lies in the growing interference caused by the decreasing inter-site distance. It was demonstrated in [7] that when the density of small cells grows beyond a certain threshold, the experienced Signal to Interference and Noise Ratio (SINR) decreases as the interfering signals transition from non-LoS (NLoS) to LoS propagation, degrading the network performance. In fact, the problem of interference management is central to all mobile communications systems. All the proposed Multiple Access (MA) schemes in every mobile generation can be summarized as a proposition to manage the problem of inter-user interference while sharing the same resources. The same is true at the level of traditional cellular architectures where frequency reuse schemes were resorted to for inter-cell interference mitigation. One could argue that inter-cell interference is basically inter-user interference taken on a network scale, but now that the network densification intensifies, the validity of such a distinction may be at question.
Coordinated Scheduling and Coordinated Beamforming
In CS, the cooperating nodes seek to avoid interference by allocating cell-edge users E1 and F1 different channels 51 and 5 2 (Fig. 1.5), while allocating other frequencies for inner users in the cell (e.g. user E2 in Fig. 1.5). This joint decision on the user-channel association is possible thanks to the sharing of user CSI between the corresponding nodes. Note, however, that the results of a CS coordination are not limited to the interference avoidance policy, but also take into account the potentially competing QoS requirements of both users, the available power at every RRH, the history of user serving, etc. That is to say the coordination results are parts of a whole in the ongoing resource allocation problem to best serve the two cells. These coordination results are applied every time scheduling is performed, which can be as short as 1 ms for LTE. Therefore, resources can be dynamically allocated even with instantaneous changes of UEs channel conditions.
With CB (Fig. 1.6), users are served through the same time/frequency resource while being allocated different spatial resources, i.e. beam patterns. Thanks to the CSI sharing, which includes channel quality indicators and precoding matrix indicators, interference is prevented through each transmission node allocating the main beam to its user, and nullifying the beam to the other neighboring UE, as shown in Fig. 1.6.
Energy Efficiency Maximization in DAS
Several works target the optimization of system Energy Efficiency (EE) in DAS. In [87], two antenna selection techniques are proposed, either based on user path-loss information or on RRH energy consumption. Also, proportional fairness scheduling is considered for subband allocation with a utility function adapted to optimize the EE. In [88], Subcarrier Assignment (SA) and PA are done in two separate stages. In the first one, the number of subcarriers per RRH is determined, and subcarrier-RRH assignment is performed assum-ing initial equal power distribution. In the second stage, PA is performed by maximizing the EE under the constraints of the total transmit power per RRH, of the targeted bit error rate and of a proportionally-fair throughput distribution among active users. The optimization techniques proposed in [87,88] for DAS are designed for the orthogonal case. In other words, they allow the allocation of only one user per subcarrier.
NOMA in DAS and C-RAN
Applying power multiplexing on top of the orthogonal frequency division multiplexing (OFDM) layer has proven to significantly increase system throughput compared to or-thogonal signaling, while also improving fairness and cell-edge user experience. A few previous works have studied the application of NOMA in the DAS context. An outage probability analysis for the case of two users in C-RAN is provided in [89] where all RRHs serve simultaneously both users. The results show the superiority of NOMA when compared to TDMA, in the context of C-RANs. In [90], the study investigates the appli-cation of distributed NOMA for the uplink of C-RANs. The partially centralized C-RAN architecture allows the use of joint processing by distributed antennas, in which RRHs can exchange correctly decoded messages from other RRHs in order to perform SIC. In [91], an efficient end-to-end uplink transmission scheme is proposed where the wireless link between users and RRHs on one side, and the fronthaul links between the RRHs and BBU on the other side are studied. User grouping on blocks of subcarriers is proposed to mitigate the computational complexity, and a fronthaul adaptation for every user group is performed in order to strike a tradeoff between throughput and fronthaul usage.
State of the Art of Power Minimization in the NOMA Context
Recent works tackle the downlink power minimization problem in the NOMA context. In [92], the proposed joint RA scheme consists in a deletion-based algorithm where the entire spectrum is first allocated to all users; then, optimal PA followed by the removal of users from subcarriers are iteratively conducted until the constraints of the maximum number of multiplexed users are satisfied. The algorithm presents near-optimal results, however, it proceeds with a high computational complexity as a numerical solver is required for solving the optimal PA in every iteration. Moreover, the PMCs are not taken into consideration. The PMCs state that the signal to be decoded first must have a higher power level than the other received signals, so that it is detectable at the receiver side. A similar deletion-based approach to [92] is followed in [29] but with consideration of PMCs. First the entire spectrum is allocated to all users and the optimal PA is obtained for a relaxed version of the minimization problem without PMCs. Then, the number of multiplexed users per subcarrier is reduced to a maximum of two (according to a simple criterion), before the iterative adjustment phase is conducted serially over all the users to meet the PMCs using bisection search. However, the proposed adjustment procedure does not take into account the rate coupling between multiplexed users. Thus, the obtained solution satisfies PMCs but without a guarantee of user rate satisfaction. Power minimization strategies are also proposed in [93] for Multiple-Input Multiple-Output NOMA (MIMO-NOMA), where PA and receive beamforming design are alternated in an iterative way. Constraints on the targeted Signal to Interference and Noise Ratio (SINR) are considered to guarantee successful SIC decoding. Provided results for a moderate number of users (4 or 6) show an important gain in performance with respect to OMA, however the subcarrier allocation problem is not included, only PA is considered. In [85], a set of techniques have been introduced, allowing the joint allocation of subcarriers and power, with the aim of minimizing the total power in NOMA-CAS. Particularly, it was shown that the most efficient method, from the power minimization perspective, consists of applying user pairing at a subsequent stage to single-user assignment, i.e. after applying OMA signaling at the first stage, instead of jointly assigning collocated users to subcarriers. The work in this chapter follows the same approach to perform power minimization.
Power Minimization in OMA Signaling
The problem in (2.3) is NP-hard [94, 95] even for the OMA case, and its solution resides in finding the optimal subcarrier assignment which consists in a subcarrier-user-RRH allocation (S 8:), and the optimal PA (% ) corresponding to that SA. This being said, for any fixed :SA (including the optimal one:= AS ), the optimal PA for power minimization in OMA is the well known waterfilling algorithm: [96]. Therefore, we start by presenting the properties of the waterfilling algorithm in details, then the gained insights allow the design of an efficient joint channel and power allocation scheme.
Table of contents :
Contents
List of Figures
List of Tables
Acronyms
Résumé de la thèse
Introduction
1 Background
1.1 Principles of Downlink NOMA
1.2 Network Densification and Distributed Antenna Systems
1.2.1 Distributed and Centralized Densification
1.2.2 More on Network Centralization
1.2.3 On the Limits of Network Densification and the Cell Paradigm Shift
1.3 Coordinated Multipoint
1.3.1 Coordinated Scheduling and Coordinated Beamforming
1.3.2 Dynamic Point Selection and Joint Transmission
1.4 Device to Device Communication
1.4.1 Full Duplex
1.5 Summary
2 NOMA Mutual SIC for Power Minimization in Distributed Antenna Systems
2.1 Related Works
2.1.1 Energy Efficiency Maximization in DAS
2.1.2 NOMA in DAS and C-RAN
2.1.3 State of the Art of Power Minimization in the NOMA Context
2.2 System Model
2.3 Problem Formulation
2.4 Power Minimization in OMA Signaling
2.4.1 Optimal PA: The Waterfilling Algorithm
2.4.1.1 Subcarrier Addition
2.4.1.2 Subcarrier Removal
2.4.2 Joint Subcarrier Assignment and Power Allocation in OMA
2.5 Power Minimization in NOMA Signaling
2.5.1 Same Serving RRH
2.5.2 Different serving RRHs
2.5.2.1 Theoretical Background
2.5.2.2 Mutual SIC UnConstrained (MutSIC-UC)
2.5.2.3 Mutual SIC with Direct Power Adjustment (MutSIC-DPA)
2.5.2.4 Mutual SIC with Sequential Optimization for Power Adjustment (MutSIC-OPAd, MutSIC-SOPAd, and Mut&SingSIC)
2.6 Complexity Analysis
2.7 Performance Results
2.7.1 System Parameters
2.7.2 Simulation Results
2.8 Conclusion
2.A Formulation of the Power Optimization Problem for the Constrained Case in Mutual SIC
2.B Complexity Analysis of SRRH-OPA and Comparison with SRRH-LPO
3 NOMA Mutual SIC for Power Minimization in Hybrid Distributed Antenna Systems
3.1 Related Works
3.2 System Description and Problem Formulation
3.3 Optimal Power Allocation for OMA HDAS
3.3.1 Single Power-Limited Antenna
3.4 Resource Allocation for HDAS using OMA
3.4.1 The OMA-HDAS Approach
3.4.2 The OMA-HDAS-Realloc Approach
3.5 Resource Allocation for HDAS using NOMA
3.6 Complexity Analysis
3.7 Performance Evaluation
3.8 Conclusion
4 Enhancing the Spectral Efficiency of CoMP Systems using NOMA mutual SIC
4.1 Related Works
4.2 System Model
4.3 Mutual SIC Conditions for CoMP Scenarios
4.4 Mutual SIC in a Two-User System
4.4.1 Two-User System with Dynamic Point Selection
4.4.1.1 DPS-DMSIC
4.4.1.2 DPS-NoSIC
4.4.2 Two-User System with Joint Transmission
4.4.2.1 JT-DMSIC
4.4.2.2 JT-NoSIC
4.5 Mutual SIC in a Three-User System
4.5.1 The Conventional Approach (CellEdgeJT-CellCenterSIC)
4.5.2 Triple Mutual SIC in a Joint Transmission System (FullJT-TMSIC)
4.5.3 Enhancement over the Conventional Approach (CellEdgeJT-TMSIC)
4.5.4 On Successful SIC in FullJT-TMSIC and CellEdgeJT-TMSIC
4.6 Performance Evaluation
4.7 Conclusion
5 Analysis of Drone Placement Strategies for Complete Interference Cancellation in Two-Cell NOMA CoMP Systems
5.1 Related Works
5.2 System Model
5.2.1 Path Loss Model
5.2.2 Signal Model and TMSIC Conditions
5.2.3 TMSIC Solution Space
5.2.4 UAV Placement Problem Formulation
5.3 Probabilistic Framework for TMSIC-Based UAV Positioning
5.4 Proposed UAV Positioning Techniques (UPT) based on TMSIC
5.4.1 Maximum Probability Positioning (MPP)
5.4.2 Maximum Rate Positioning (MRP)
5.4.3 Maximum Probability and Rate Positioning (MPRP)
5.4.4 Mean Path Loss Positioning (MPLP)
5.4.5 Probabilistic Approach Based on Subband Splitting Positioning (SSP)
5.5 Power Allocation Strategy
5.5.1 TMSIC Power Allocation and TMSIC Testing
5.5.2 Alternative Power Allocation Techniques
5.5.2.1 DMSIC
5.5.2.2 NoSIC
5.5.2.3 SSIC
5.6 Performance Assessment Procedure and Simulation Results
5.6.1 Performance Assessment
5.6.2 Simulation Results
5.7 Conclusion
6 NOMA Mutual SIC for Full-Duplex D2D Systems Underlaying Cellular Networks
6.1 Related Works
6.2 System Model
6.2.1 Formulation of the Joint Channel and Power Allocation Problem
6.3 Power Allocation for No-SIC Scenarios
6.3.1 FD-NoSIC
6.3.2 HD-NoSIC
6.4 Power Allocation Problem Modification for HD and FD with Mutual SIC (HD-SIC and FD-SIC)
6.5 Power Allocation for HD-SIC scenario
6.6 Derivation of the SIC conditions for FD mutual SIC
6.6.1 First decoding order: 1 decodes <2 then <1
6.6.2 Second decoding order: 1 decodes <1 then <2
6.7 Power Allocation Problem Simplification of FD-SIC by Constraint Reduction
6.8 Solution for FD-SIC Optimal Power Allocation
6.8.1 3D Solution Space Representation
6.8.2 Search Space Reduction
6.8.3 Selection of the Useful Intersections
6.8.3.1 Interplay between % »2 and % »4
6.8.3.2 Selection of the Useful Parallelepiped Sides
6.8.3.3 Segments Endpoints
6.8.3.3.1 Side (2
6.8.3.3.2 Side (1
6.8.3.3.3 Side (*
6.8.4 D2D Throughput Optimization
6.8.4.1 Side (1
6.8.4.2 Side (2
6.8.4.3 Side (*
6.8.5 Summary of the Power Allocation Procedure and Extension to the Second Decoding Order
6.9 Channel Allocation
6.10 Numerical Results
6.10.1 Results for a Single D2D-CU System
6.10.2 Results for a complete cellular system with CUs and D2Ds
6.11 Conclusion
6.A Necessary and Sufficient Conditions for the Existence of a Power Allocation Enabling FD-SIC
Conclusions and Future Works
Bibliography
