A D2D-Based Solution for MTC Connectivity Problem in NOMA-Based Cellular IoT Networks: Dynamic User Grouping and Resource Allocation

In a cellular network (CN), cellular users (CUs) located nearby machine type communications (MTC) devices (MTC-Ds) may act as uplink gateways to relay data to the base station (BS). We consider non-orthogonal multiple access (NOMA) and successive interference cancellation (SIC) at the receiver to in...

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Vydané v:Mobile networks and applications Ročník 25; číslo 5; s. 1998 - 2011
Hlavní autori: Kazeminia, Mahdi, Mehrjoo, Mehri, Tomasin, Stefano
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.10.2020
Springer Nature B.V
Predmet:
Algorithms
Cellular communication
Communications Engineering
Complexity
Computer Communication Networks
Computer networks
Electrical Engineering
Engineering
Gateways
Heuristic methods
Integer programming
Interference
IT in Business
Mathematical programming
Mixed integer
Networks
Nonorthogonal multiple access
Operations research
Optimization
Power management
Resource allocation
User grouping
Device to device communication
Nonorthogonal multiple access
Successive interference cancellation
Machine type communication
Resource allocation
ISSN:1383-469X, 1572-8153
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Shrnutí:In a cellular network (CN), cellular users (CUs) located nearby machine type communications (MTC) devices (MTC-Ds) may act as uplink gateways to relay data to the base station (BS). We consider non-orthogonal multiple access (NOMA) and successive interference cancellation (SIC) at the receiver to increase the number of connected devices and the spectrum efficiency. Both underlay and overlay spectrum access modes (SAMs) are considered. We introduce a dynamic user grouping (UG) concept as a practical constraint for SIC, in order to decrease the receiver complexity. Moreover, SIC constraints are introduced based on a minimum signal-to-noise-plus-interference ratio (SINR) at the receiver. We formulate a joint dynamic UG, power allocation, and resource block (RB) assignment problem, aiming at maximizing the total sum-rate of both CUs and MTC-Ds. We transform the obtained non-convex mixed-integer programming problem into a convex problem by using a quadratic fractional programming (FP). A heuristic method is also proposed to reduce complexity. Simulation results demonstrate that the proposed solution outperforms the conventional CN (C-CN) method and the results of the proposed optimization methods are close to optimal solution obtained by the exhaustive search (ES) algorithm in terms of total average sum-rate and network connectivity, while requiring less transmit power. The heuristic method decrease the computational complexity of the FP method at the expense of a small reduction in spectrum efficiency.
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content type line 14
ISSN:1383-469X
1572-8153
DOI:10.1007/s11036-020-01546-y