A Novel User Grouping Algorithm for Downlink NOMA

2021 ◽  
Author(s):  
Navideh Ghafouri Jeshvaghani ◽  
Naser Movahhedinia ◽  
Mohammad Reza Khayyambashi
Keyword(s):  
Interference Cancellation ◽  
Multiple Access ◽  
Spectrum Efficiency ◽  
System Capacity ◽  
Substantial Impact ◽  
User Grouping ◽  
Radio Access ◽  
Power Domain ◽  
Rate Maximization ◽  
Sum Rate

Abstract Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for resource allocation improvement in the 5th generation of cellular networks. Compared to orthogonal multiple access techniques (OMA), NOMA offers extra benefits, including greater spectrum efficiency which is provided through multiplexing users in the transmission power domain while using the same spectrum resources non-orthogonally. Even though NOMA uses Successive Interference Cancellation (SIC) to repeal the interference among users, user grouping has shown to have a substantial impact on its performance. This prformance improvement can appear in different parameters such as system capacity, rate, or the power consumption. In this paper, we propose a novel user grouping scheme for sum-rate maximization which increases the sum-rate up to 25 percent in comparison with two authenticated recent works. In addition to being matrix-based and having a polynomial time complexity, the proposed method is also able to cope with users experiencing different channel gains and powers in different sub-bands.

Non Orthogonal Multiple Access: Basic Concepts and Misconceptions

2019 ◽  
Vol 7 (1) ◽  
pp. 1-4
Author(s):  
Syed Rizwan Hassan ◽  
Noman Shabbir ◽  
Arooj Unbreen ◽  
Ateeq Ur Rehman ◽  
Ahmad Iqbal
Keyword(s):  
Mobile Communication ◽  
Interference Cancellation ◽  
Multiple Access ◽  
Successive Interference Cancellation ◽  
Receiver Side ◽  
Superposition Coding ◽  
Radio Access ◽  
Power Domain ◽  
Basic Concepts

Non Orthogonal Multiple Access (NOMA) is fundamentally different from the Orthogonal Multiple Access (OMA) techniques as in NOMA the user make access to channel by using the same frequency and in the same time. In NOMA the multiplexing is performed in power domain by using superposition coding at the transmitter and receiver side uses Successive Interference Cancellation (SIC) to separate the transmitted signals that are multiplexed in power domain. This technique is a possible candidate for future radio access so this is a hot topic in research. Being a different method from OMA that is implemented in all previous mobile communication generations several misconceptions have been developed about this technique. This paper give an overview about NOMA based system and also analyze the major misconceptions about this technique and also explain the concepts to resolve these mistaken beliefs.

scholarly journals Dual Dynamic Scheduling for Hierarchical QoS in Uplink-NOMA: A Reinforcement Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4404
Author(s):  
Xiangjun Li ◽  
Qimei Cui ◽  
Jinli Zhai ◽  
Xueqing Huang
Keyword(s):  
Reinforcement Learning ◽  
Power Control ◽  
Interference Cancellation ◽  
Dynamic Scheduling ◽  
Dynamic Environment ◽  
Spectrum Efficiency ◽  
System Capacity ◽  
Power Domain ◽  
Markov Decision ◽  
Fierce Competition

The demand for bandwidth-intensive and delay-sensitive services is surging daily with the development of 5G technology, resulting in fierce competition for scarce radio resources. Power domain Nonorthogonal Multiple Access (NOMA) technologies can dramatically improve system capacity and spectrum efficiency. Unlike existing NOMA scheduling that mainly focuses on fairness, this paper proposes a power control solution for uplink hybrid OMA and PD-NOMA in dual dynamic environments: dynamic and imperfect channel information together with the random user-specific hierarchical quality of service (QoS). This paper models the power control problem as a nonconvex stochastic, which aims to maximize system energy efficiency while guaranteeing hierarchical user QoS requirements. Then, the problem is formulated as a partially observable Markov decision process (POMDP). Owing to the difficulty of modeling time-varying scenes, the urgency of fast convergency, the adaptability in a dynamic environment, and the continuity of the variables, a Deep Reinforcement Learning (DRL)-based method is proposed. This paper also transforms the hierarchical QoS constraint under the NOMA serial interference cancellation (SIC) scene to fit DRL. The simulation results verify the effectiveness and robustness of the proposed algorithm under a dual uncertain environment. As compared with the baseline Particle Swarm Optimization algorithm (PSO), the proposed DRL-based method has demonstrated satisfying performance.

scholarly journals An Efficient Resource Allocation Algorithm for OFDM-Based NOMA in 5G Systems

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1399 ◽  
Author(s):  
Omar A. Saraereh ◽  
Amer Alsaraira ◽  
Imran Khan ◽  
Peerapong Uthansakul
Keyword(s):  
Resource Allocation ◽  
Power Allocation ◽  
Multiple Access ◽  
Low Complexity ◽  
System Capacity ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
User Grouping ◽  
Power Domain ◽  
Efficient Resource

Non-orthogonal multiple access (NOMA) has become the key technology in the future 5G wireless networks. It can achieve multi-user multiplexing in the transmit power domain by allocating different power, which can effectively improve the system capacity and spectral efficiency. Aiming at the problem of high computational complexity and improving system capacity in non-orthogonal multiple access (NOMA) based on orthogonal frequency division multiple access (OFDMA) for 5G wireless cellular networks, this paper proposes an improved low complexity radio resource allocation algorithm for user grouping and power allocation optimization. The optimization model is established with the goal of maximizing system capacity. Through the step-by-step optimization idea, the complex non-convex optimization problem is decomposed into two sub-problems to be solved separately. Firstly, all users are grouped based on the greedy method, and then the power allocation is performed on the sub-carriers of the fixed group. Simulation results show that the proposed algorithm has better system capacity than the existing state-of-the-art algorithms and reduced complexity performance.

Sign in / Sign up

Export Citation Format

Share Document