Joint time and power allocation for uplink cooperative non-orthogonal multiple access based massive machine-type communication Network

Non-orthogonal multiple access is an essential promising solution to support large-scale connectivity required by massive machine-type communication scenario defined in the fifth generation (5G) mobile communication system. In this article, we study the problem of energy minimization in non-orthogonal multiple access–based massive machine-type communication network. Focusing on the massive machine-type communication scenario and assisted by grouping method, we propose an uplink cooperative non-orthogonal multiple access scheme with two phases, transmission phase and cooperation phase, for one uplink cooperative transmission period. Based on uplink cooperative non-orthogonal multiple access, the machine-type communication device with better channel condition and more residual energy will be selected as a group head, which acts as a relay assisting other machine-type communication devices to communicate. In the transmission phase, machine-type communication devices transmit data to the group head. Then, the group head transmits the received data with its own data to base station in the cooperation phase. Because the massive machine-type communication devices are low-cost dominant with limited battery, based on uplink cooperative non-orthogonal multiple access, we propose a joint time and power allocation algorithm to minimize the system energy consumption. Furthermore, the proposed joint time and power allocation algorithm includes dynamic group head selection and fractional transmit time allocation algorithms. Simulation results show that the proposed solution for uplink cooperative non-orthogonal multiple access–based massive machine-type communication network outperforms other schemes.

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Distributed Algorithm for Base Station Assignment in 4G/5G Machine-Type Communication Scenarios with Backhaul Limited Conditions

Sensors ◽

10.3390/s20226553 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6553

Author(s):

Edgar A. Esquivel-Mendiola ◽

Hiram Galeana-Zapién ◽

David H. Covarrubias ◽

Edwin Aldana-Bobadilla

Keyword(s):

Assignment Problem ◽

Path Loss ◽

Base Station ◽

System Throughput ◽

Dual Decomposition ◽

Minimum Path ◽

Machine Type ◽

Assignment Algorithm ◽

Communication Devices ◽

Machine Type Communication

A progressive paradigm shift from centralized to distributed network architectures has been consolidated since the 4G communication standard, calling for novel decision-making mechanisms with distributed control to operate at the network edge. This situation implies that each base station (BS) must manage resources independently to meet the quality of service (QoS) of existing human-type communication devices (HTC), as well as the emerging machine type communication (MTC) devices from the internet of things (IoT). In this paper, we address the BS assignment problem, whose aim is to determine the most appropriate serving BS to each mobile device. This problem is formulated as an optimization problem for maximizing the system throughput and imposing constraints on the air interface and backhaul resources. The assignment problem is challenging to solve, so we present a simple yet valid reformulation of the original problem while using dual decomposition theory. Subsequently, we propose a distributed price-based BS assignment algorithm that performs at each BS the assignment process, where a novel pricing update scheme is presented. The simulation results show that our proposed solution outperforms traditional maximum signal to interference plus noise ratio (Max-SINR) and minimum path-loss (Min-PL) approaches in terms of system throughput.

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QoS-Based Data Aggregation and Resource Allocation Algorithm for Machine Type Communication Devices in Next-Generation Networks

IEEE Access ◽

10.1109/access.2021.3107624 ◽

2021 ◽

Vol 9 ◽

pp. 119735-119754

Author(s):

Nasser Mohammed Ahmed ◽

Nasser-Eddine Rikli

Keyword(s):

Resource Allocation ◽

Data Aggregation ◽

Next Generation Networks ◽

Next Generation ◽

Resource Allocation Algorithm ◽

Allocation Algorithm ◽

Machine Type ◽

Communication Devices ◽

Machine Type Communication

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Joint user clustering and salp based particle swarm optimization algorithm for power allocation in MIMO-NOMA

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201412 ◽

2021 ◽

Vol 40 (5) ◽

pp. 9007-9019

Author(s):

Jyotirmayee Subudhi ◽

P. Indumathi

Keyword(s):

Energy Efficiency ◽

Particle Swarm Optimization ◽

Power Allocation ◽

Multiple Access ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

Allocation Algorithm ◽

User Clustering ◽

Sum Rate

Non-Orthogonal Multiple Access (NOMA) provides a positive solution for multiple access issues and meets the criteria of fifth-generation (5G) networks by improving service quality that includes vast convergence and energy efficiency. The problem is formulated for maximizing the sum rate of MIMO-NOMA by assigning power to multiple layers of users. In order to overcome these problems, two distinct evolutionary algorithms are applied. In particular, the recently implemented Salp Swarm Algorithm (SSA) and the prominent Optimization of Particle Swarm (PSO) are utilized in this process. The MIMO-NOMA model optimizes the power allocation by layered transmission using the proposed Joint User Clustering and Salp Particle Swarm Optimization (PPSO) power allocation algorithm. Also, the closed-form expression is extracted from the current Channel State Information (CSI) on the transmitter side for the achievable sum rate. The efficiency of the proposed optimal power allocation algorithm is evaluated by the spectral efficiency, achievable rate, and energy efficiency of 120.8134bits/s/Hz, 98Mbps, and 22.35bits/Joule/Hz respectively. Numerical results have shown that the proposed PSO algorithm has improved performance than the state of art techniques in optimization. The outcomes on the numeric values indicate that the proposed PSO algorithm is capable of accurately improving the initial random solutions and converging to the optimum.

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Uplink Cooperative Transmission for Machine-Type Communication Traffic in Cellular System

2016 IEEE 84th Vehicular Technology Conference (VTC-Fall) ◽

10.1109/vtcfall.2016.7881956 ◽

2016 ◽

Cited By ~ 5

Author(s):

Yue Li ◽

Mohammad Ghasemiahmadi ◽

Lin Cai

Keyword(s):

Cellular System ◽

Cooperative Transmission ◽

Machine Type ◽

Machine Type Communication ◽

Communication Traffic

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Improved Water-Filling Power Allocation for Energy-Efficient Massive MIMO Downlink Transmissions

International Journal of Electronics and Telecommunications ◽

10.1515/eletel-2017-0011 ◽

2017 ◽

Vol 63 (1) ◽

pp. 79-84

Author(s):

M. K Noor Shahida ◽

Rosdiadee Nordin ◽

Mahamod Ismail

Keyword(s):

Power Allocation ◽

Network Architecture ◽

Massive Mimo ◽

Multiple Input Multiple Output ◽

Base Station ◽

Downlink Transmission ◽

Allocation Algorithm ◽

Mimo Antenna ◽

Water Filling ◽

Water Filling Algorithm

Abstract Energy Efficiency (EE) is becoming increasingly important for wireless communications and has caught more attention due to steadily rising energy costs and environmental concerns. Recently, a new network architecture known as Massive Multiple-Input Multiple-Output (MIMO) has been proposed with the remarkable potential to achieve huge gains in EE with simple linear processing. In this paper, a power allocation algorithm is proposed for EE to achieve the optimal EE in Massive MIMO. Based on the simplified expression, we develop a new algorithm to compute the optimal power allocation algorithm and it has been compared with the existing scheme from the previous literature. An improved water filling algorithm is proposed and embedded in the power allocation algorithm to maximize EE and Spectral Efficiency (SE). The numerical analysis of the simulation results indicates an improvement of 40% in EE and 50% in SE at the downlink transmission, compared to the other existing schemes. Furthermore, the results revealed that SE does not influence the EE enhancement after using the proposed algorithm as the number of Massive MIMO antenna at the Base Station (BS) increases.

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Beamforming and Power Allocation in Dynamic TDD Networks Supporting Machine-Type Communication

ICC 2020 - 2020 IEEE International Conference on Communications (ICC) ◽

10.1109/icc40277.2020.9148941 ◽

2020 ◽

Cited By ~ 1

Author(s):

Chi-Han Lee ◽

Ronald Y. Chang ◽

Chun-Tao Lin ◽

Shin-Ming Cheng

Keyword(s):

Power Allocation ◽

Machine Type ◽

Dynamic Tdd ◽

Machine Type Communication

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User Association and Power Control for Energy Efficiency Maximization in M2M-Enabled Uplink Heterogeneous Networks with NOMA

Sensors ◽

10.3390/s19235307 ◽

2019 ◽

Vol 19 (23) ◽

pp. 5307 ◽

Cited By ~ 1

Author(s):

Shuang Zhang ◽

Guixia Kang

Keyword(s):

Energy Efficiency ◽

Power Control ◽

Heterogeneous Networks ◽

Base Station ◽

Sequential Optimization ◽

User Association ◽

Decode And Forward ◽

Vast Number ◽

Machine Type ◽

Machine Type Communication

To support a vast number of devices with less energy consumption, we propose a new user association and power control scheme for machine to machine enabled heterogeneous networks with non-orthogonal multiple access (NOMA), where a mobile user (MU) acting as a machine-type communication gateway can decode and forward both the information of machine-type communication devices and its own data to the base station (BS) directly. MU association and power control are jointly considered in the formulated as optimization problem for energy efficiency (EE) maximization under the constraints of minimum data rate requirements of MUs. A many-to-one MU association matching algorithm is firstly proposed based on the theory of matching game. By taking swap matching operations among MUs, BSs, and sub-channels, the original problem can be solved by dealing with the EE maximization for each sub-channel. Then, two power control algorithms are proposed, where the tools of sequential optimization, fractional programming, and exhaustive search have been employed. Simulation results are provided to demonstrate the optimality properties of our algorithms under different parameter settings.

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