Towards intelligent user clustering techniques for non-orthogonal multiple access: a survey

Hamedoon, Syed M.; Chattha, Jawwad Nasar; Bilal, Muhammad

doi:10.1186/s13638-024-02333-z

EURASIP Journal on Wireless Communications and Networking

Table 1 Non-machine learning-based user clustering schemes in NOMA

From: Towards intelligent user clustering techniques for non-orthogonal multiple access: a survey

Clustering scheme	Related work	Scenario	Assumptions	Objective functions	Application	Findings
Mathematical optimization	[6, 8, 18]	Downlink	CSI is known at base station	Sum rate maximization	MISO	(1) It performs better than random clustering (2) Jointly user clustering and power allocation
Game-theoretic approach	[9]	Downlink	The channels assumed to be Rician fading	Maximize the energy efficiency	MIMO	Joint user clustering, passive beamforming and power allocation
Matching algorithm	[10]	Downlink	The channels assumed to be Rician fading	Maximize the energy efficiency	RIS assisted NOMA	Joint user clustering, passive beamforming and power allocation
Other user clustering techniques	[12]	Downlink	Users inside a cell are independent of path loss effect	Sum rate maximization	MU-MIMO	To suppress multi-user and inter-symbol interference
	[13, 14]	Downlink	Assume no inter-cluster interference	Higher spectral efficiency	Massive MIMO	To improve performance by NOMA beamspace with efficient user clustering
	[15]	Downlink	Location information is assumed to be known by the UE	Sum rate maximization	5G mm-wave	Location aided clustering approach is used for cluster assignment and improves the performance as compared to other beamforming schemes
	[16]	Uplink	BS knows perfectly all the channel gains	Reduce system latency	MISO	To solve the problem of user clustering by optimally resource block allocation with time-based proportional fairness
	[17]	Downlink	CSI is known at the base station, fixed user distances	Improves transmission power	MISO	Iterative power and joint user clustering algorithm reduces the system’s power consumption
	[19]	Downlink	CSI is known at the base station	Sum rate maximization	SISO	This schemes used Brute Force Search method and improves system performance
	[20]	Uplink	No intercell interference from other neighbouring cells	Sum rate maximization	Narrow band IoT	(1) Joint user clustering and resource allocation of MTC devices (2) Satisfying the QoS requirements and transmission power in each cluster
	[21]	Uplink	CSI is known at the base station	Higher spectral efficiency	MISO	Provides optimal solution for decoding order and power control
	[11]	Downlink	CSI is known at the base station	Minimize total power consumption	SISO	(1) Consider decoding power consumption (2) Compressive sensing method is used to solve the convex problem

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