Efficient Resource Management for Sum Capacity Maximization in 5G NOMA Systems
Abstract
:1. Introduction
1.1. Related Work
1.2. Motivation and Contributions
- Consider a NOMA network using power multiplexing, where a BS intends to communicates with K users through M sub-channels. We formulate a resource management problem for sub-channel assignment and power allocation. Our objective is to maximize the sum capacity of the system subject to the user minimum capacity requirement.
- The problem of resource management decouples into two sub-problems where we fist provide two sub-optimal algorithms for sub-channel assignment under the fixed power of BS. We design these algorithms based on user channel condition and user minimum capacity requirement, respectively. For efficient power allocation, we then transform the optimization problem into standard convex optimization and for any given sub-channel allocation, we exploit dual theory to obtain the optimal solution.
- For a fair comparison, we also consider the work in Reference [12] as a benchmark for power allocation. Reference [12] first satisfy the minimum QoS of one user and then allocates the remaining power to another user to maximize its capacity. Different from the work in Reference [12], we first satisfy the minimum capacity requirements of both users and then optimize the remaining power among these users.
- For simulation results, we apply the sub-channel assignment algorithms to the proposed power allocation scheme and the one in Reference [12]. We also provide the results of the proposed power management technique and the power management technique of Reference [12] for random sub-channel assignment. Results demonstrate that our NOMA technique provides a higher capacity than that in Reference [12].
2. System Model and Problem Formulation
3. Resource Management With Fixed Power Control
3.1. User Channel Condition Based Sub-Channel Assignment Algorithm
Algorithm 1 Sub-channel Assignment Based on User Channel Condition |
1. Initialize K, M, , , , and . |
2. For do |
3. Divide matrix H into two matrices and . |
4. . |
5. . |
6. For do |
7. . |
8. For do |
9. For do |
10. If do |
11. Compute the sum capacity using . |
12. For do |
13. Compute . |
14. Compute . |
15. Compute on . |
16. End For . |
17. End For j. |
18. End For i. |
19. End For u. |
20. At each index of m, assign a sub-channel to any two users with high sum capacity. |
21. End For m. |
22. Plot the sum capacity of the system |
3.2. User Minimum Capacity Requirement Based Sub-Channel Algorithm
Algorithm 2 Sub-channel Assignment Based on User Minimum Capacity Requirement |
1. Initialize K, M, , , , and . |
2. For do |
3. For do |
4. For do |
5. , when and . |
6. If do |
7. Compute the sum capacity using . |
8. For do |
9. Compute . |
10. Compute . |
11. Compute on . |
12. End For . |
13. End For j. |
14. End For i. |
15. At each index of m, assign a sub-channel to any two users with high sum capacity |
16. End For m. |
17. Plot the sum capacity of the system. |
4. Resource Management with Optimal Power Control
5. Results and Analysis
- Prop-Sch1: It is the proposed power optimization technique as presented in Section 4.
- Sch2: This is the solution for power allocation scheme presented in Reference [12]. According to this scheme, the minimum capacity of one user satisfies first and then allocate all the remaining power to another user.
- Prop1: This denotes the user minimum capacity requirement based sub-channel assignment using proposed power allocation scheme.
- Prop2: This is the user channel condition based sub-channel allocation using proposed power technique.
- SProp: This is the random sub-channel assignment using proposed power allocation scheme.
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Abbreviation | Definition |
NOMA | Non-orthogonal multiple access. |
3G | Third generation. |
4G | Fourth generation. |
CDMA | Code-division multiple access. |
5G | Fifth generation. |
HetNet | Heterogeneous network. |
5GB | Beyond 5G. |
SC | Sub-channel. |
OMA | Orthogonal multiple access. |
BS | Base station. |
SIC | Successive interference cancellation. |
CU | Cellular user. |
AWGN | Additive white Gaussian noise. |
SINR | Signal-to-noise-plus-interference ratio. |
KKT | Karush–Kuhn–Tucker. |
RSMA | Rate splitting multiple access. |
OFDMA | Orthogonal frequency division multiple access. |
Notation | Definition |
The set of cellular users in the proposed NOMA network. | |
The set of sub-channels in the system. | |
The set of cellular users on a sub-channel at one time. | |
The maximum number of cellular users on a sub-channel at one time. | |
Represents the cellular user j. | |
Represents the sub-channel m. | |
The transmit power of BS for over . | |
The channel gain of over . | |
The total power of BS. | |
The received signal of over . | |
The data symbol of over . | |
The AWGN of over . | |
The noise variance of AWGN. | |
The capacity of over . | |
The SINR of over . | |
The binary variable for sub-channel allocation. | |
The sum capacity of the system. | |
The minimum user capacity to achieve the QoS requirements. | |
The power allocation factor. | |
The total transmit power on . | |
H | The channel matrix. |
R | The matrix of user minimum capacity for QoS requirements. |
The remaining power after capacity requirements. | |
The Lagrangian function. | |
The non-negative Lagrangian multipliers. | |
The iteration index. | |
The non-negative step size. |
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Ali, A.; Baig, A.; Awan, G.M.; Khan, W.U.; Ali, Z.; Sidhu, G.S. Efficient Resource Management for Sum Capacity Maximization in 5G NOMA Systems. Appl. Syst. Innov. 2019, 2, 27. https://doi.org/10.3390/asi2030027
Ali A, Baig A, Awan GM, Khan WU, Ali Z, Sidhu GS. Efficient Resource Management for Sum Capacity Maximization in 5G NOMA Systems. Applied System Innovation. 2019; 2(3):27. https://doi.org/10.3390/asi2030027
Chicago/Turabian StyleAli, Azhar, Amna Baig, Ghulam Mujtaba Awan, Wali Ullah Khan, Zain Ali, and Guftaar Ahmad Sardar Sidhu. 2019. "Efficient Resource Management for Sum Capacity Maximization in 5G NOMA Systems" Applied System Innovation 2, no. 3: 27. https://doi.org/10.3390/asi2030027