Joint Sensing Duration Adaptation, User Matching, and Power Allocation for Cognitive OFDM-NOMA Systems

Xu, Wenjun; Li, Xue; Lee, Chia-Han; Pan, Miao; Feng, Zhiyong

doi:10.1109/twc.2017.2777476

Cited by 69 publications

(50 citation statements)

References 40 publications

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“…h a B h b K h a (7) In formula (7), b i,k is the power allocation factor of the k th user in the user cluster C i in the orthogonal multiple access mode. Then, the users in the user cluster C i need to be clustered again according to the above method until the sum of the throughputs of the users in the single user cluster is greater than the sum of the throughputs of the users in the orthogonal multiple access.…”

Section: Improved Clustering Scheme Based On User Groupsmentioning

confidence: 99%

See 1 more Smart Citation

User Clustering Scheme for Downlink of NOMA System

2020

KSII TIIS

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An improved clustering scheme based on user group is proposed. Every two users are grouped among N-users in the allowed system according to their link gain from large to small. Each user group is numbered sequentially. Two user clusters are obtained according to the principle of maximizing link gain difference for the users in the first and last user groups. The remaining user groups are added to the two existing user clusters according to the parity of the group number. The clustering should be clustered again among the users in either user cluster if the throughput summation of a user cluster in NOMA is less than that of these users in orthogonal multiple access. The simulation results show that the proposed clustering scheme can increase the system throughput by about 8% compared with the hybrid clustering scheme when the number of users requiring service is 12.

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Section: Improved Clustering Scheme Based On User Groupsmentioning

confidence: 99%

“…In NOMA system, multiple users perform power multiplexing at the transmitting end and share the same subchannel, which form a user cluster. Therefore, the choice of user clustering [6] and power allocation [7] schemes will have a large impact on system performance. User pairing and power allocation are discussed in [8,9].…”

Section: Introductionmentioning

confidence: 99%

User Clustering Scheme for Downlink of NOMA System

2020

KSII TIIS

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“…One is assigning users one by one according to their preference lists ("User Preference") [19]. The other is grouping users by Gale-Shapley algorithm("Gale-Shapley") [18]. In the simulations, the number of users is set to an integer multiple of the number of groups.…”

Section: B Performance Comparisonmentioning

confidence: 99%

“…There also exist some user grouping strategies based on the maching theory or matching game [15], where the set of users and channels were considered as two sets of players [16]- [19]. The authors in [18] proposed a suboptimal algorithm based on matching theory to maximize the system capacity in half-duplex cognitive OFDM-NOMA systems. In [19], users were assigned one by one based on matching game to maximize the weighted total sum-rate with consideration of user fairness.…”

Section: Introductionmentioning

confidence: 99%

Interference-aware User Grouping Strategy in NOMA Systems with QoS Constraints

Guo

Zhu

et al. 2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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To meet the performance and complexity requirements from practical deployment of non-orthogonal multiple access (NOMA) systems, several users are grouped together for NOMA transmission while orthogonal resources are allocated among groups. User grouping strategies have significant impact on the power consumption and system performance. However, existing related studies divide users into groups based on channel conditions, where diverse quality of service (QoS) and interference have not been considered. In this paper, we focus on the interference-aware user grouping strategy in NOMA systems, aiming at minimizing power consumption with QoS constraints. We define a power consumption and externality (PCE) function for each user to represent the power consumption involved by this user to satisfy its QoS requirement as well as interference that this user brings to others in the same group. Then, we extend the definition of PCE to multi-user scenarios and convert the user grouping problem into the problem of searching for specific negative loops in the graph. Bellman-Ford algorithm is extended to find these negative loops. Furthermore, a greedy suboptimal algorithm is proposed to approach the solution within polynomial time. Simulation results show that the proposed algorithms can considerably reduce the total power consumption compared with existing strategies.Index Terms-Non-orthogonal multiple access (NOMA), user grouping, quality of service (QoS), interference

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“…For example, non-orthogonal multiple access (NOMA) technique was explored in [7] to improve the throughput of the networks. Throughput-optimal problem of nodes scheduling for NOMA was tackled in [8] for a multi-carrier network. Considering the fairness between different nodes, the work in [9] investigated a fair opportunistic scheduler based on probabilistic scheduling.…”

Section: Introductionmentioning

confidence: 99%