Fairness for Non-Orthogonal Multiple Access in 5G Systems

By Stelios Timotheou, Ioannis Krikidis. IEEE Signal Processing Letters, 2015

785 citations recorded by the source corpus.

In non-orthogonal multiple access (NOMA) downlink, multiple data flows are superimposed in the power domain and user decoding is based on successive interference cancellation. NOMA's performance highly depends on the power split among the data flows and the associated power allocation (PA) problem. In this letter, we study NOMA from a fairness standpoint and we investigate PA techniques that ensure fairness for the downlink users under i) instantaneous channel state information (CSI) at the transmitter, and ii) average CSI. Although the formulated problems are non-convex, we have developed low-complexity polynomial algorithms that yield the optimal solution in both cases considered.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2dabfb5a4c91279d5afb4e529c09e6bcd8662db4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

Citations in the corpus

Capacity Comparison Between MIMO-NOMA and MIMO-OMA With Multiple Users in a Cluster (2017, background): “In [8], the problem of maximizing the fairness among users of a NOMA downlink system is studied in terms of data rate under full channel state information (CSI) and outage probability under average CSI.”
A Minorization-Maximization Method for Optimizing Sum Rate in the Downlink of Non-Orthogonal Multiple Access Systems (2015, methodology): “[10], studied the power allocation for NOMA in a SISO system f rom a fairness point of view.”
Non-Orthogonal Multiple Access With Cooperative Full-Duplex Relaying (2016, background): “Proof: According to [2], [6], the outage probability of UE1 can be expressed as”
Resource Allocation for Downlink NOMA Systems: Key Techniques and Open Issues (2017, methodology): “The max-min fairness problem is shown to be quasi-convex, and optimal PA can be obtained via the bisection method [13].”
Nonorthogonal Multiple Access for 5G and Beyond (2017, methodology): “In [79], the PA strategy was investigated under the scenarios of knowing either the instantaneous CSI or the average CSI.”
Impact of User Pairing on 5G Nonorthogonal Multiple-Access Downlink Transmissions (2016, background): “User fairness in NOMA systems has been considered in [8], and the combination of cooperative diversity with NOMA has been considered in [9].”
Performance Evaluation of Non-Orthogonal Multiple Access in Visible Light Communication (2016, neutral): “From a fairness standpoint, the power allocation problem was investigated in [17] for both CSI and average CSI at the transmitter side.”
Modulation and Multiple Access for 5G Networks (2017, methodology): “As the power allocation in NOMA is based on the order of CSI, the cases with perfect and imperfect CSI are different and should be investigated separately, as in [47].”
A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges (2020, background): “Moreover, due to the feature of NOMA, the RA problems of NOMA-based HetNets not only must consider PA, but also need to consider the fairness of different users [61].”
Enhancing the Physical Layer Security of Non-Orthogonal Multiple Access in Large-Scale Networks (2016, methodology): “[10] S. Timotheou and I. Krikidis, “Fairness for non-orthogonal multiple access in 5G systems,”IEEE Signal Process.”
A General Power Allocation Scheme to Guarantee Quality of Service in Downlink and Uplink NOMA Systems (2016, background): “In [9], the authors have studied the max-min fairness in NOMA, and showed that all users can achieve the same data rate.”
Dynamic User Clustering and Power Allocation for Uplink and Downlink Non-Orthogonal Multiple Access (NOMA) Systems (2016, background): “On the other hand, the impact of power allocation on the fairness of downlink NOMA was investigated in [8], considering perfect channel state information (CSI) feedback as well as average CSI feedback.”
On Optimal Power Allocation for Downlink Non-Orthogonal Multiple Access Systems (2017, background): “In [24] and [26], the authors investigated the optimal power allocation based on MMF for users on one channel using statistical channel state information (CSI) and instantaneous CSI, respectively.”
Power and Channel Allocation for Non-Orthogonal Multiple Access in 5G Systems: Tractability and Computation (2016, background): “, updating power allocation coefficients [14].”
On the Performance of Non-orthogonal Multiple Access Systems With Partial Channel Information (2016, methodology): “Note that if the users’ power allocation coefficients are optimized according to instantaneous channel conditions, the performance can be further enhanced [15], but this is also beyond the scope of this paper.”
Uplink Nonorthogonal Multiple Access in 5G Systems (2016, background): “The works in [7] study NOMA from a fairness standpoint and investigate power allocation”
A General MIMO Framework for NOMA Downlink and Uplink Transmission Based on Signal Alignment (2015, background): “User fairness in the context of NOMA has been addressed in [7], where power allocation was investigated under different channel state information (CSI) assumptions.”
The Application of MIMO to Non-Orthogonal Multiple Access (2015, background): “The user fairness of NOMA has been considered in [9] by studying t he impact of different choices of power allocation coefficients.”
Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges (2016, background): “NOMA with a fairness constraint also outperforms OMA approaches by significantly improving the performance of the users with the worst channel conditions [30].”