2019
D. Pliatsios, P. Sarigiannidis
Power allocation in downlink non-orthogonal multiple access IoT-enabled Systems: A particle swarm optimization approach Conference Paper
2019.
Περίληψη | BibTeX | Ετικέτες: Internet of Things (IoT), Non Orthogonal Multiple Access (NOMA), Particle Swarm Optimization (PSO) | Σύνδεσμοι:
@conference{Pliatsios2019416,
title = {Power allocation in downlink non-orthogonal multiple access IoT-enabled Systems: A particle swarm optimization approach},
author = { D. Pliatsios and P. Sarigiannidis},
url = {https://www.researchgate.net/publication/335366103_Power_Allocation_in_Downlink_Non-orthogonal_Multiple_Access_IoT-enabled_Systems_A_Particle_Swarm_Optimization_Approach},
doi = {10.1109/DCOSS.2019.00085},
year = {2019},
date = {2019-01-01},
journal = {Proceedings - 15th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2019},
pages = {416-422},
abstract = {The exponential proliferation of the Internet of Things (IoT) concept renders it an integral part of the emerging 5th Generation (5G) of mobile networks. In order to address the requirement of massive IoT-type connections, the spectrum has to be better utilized. Non-Orthogonal Multiple Access (NOMA) is a promising technique that can effectively improve the spectrum efficiency. In this paper, we consider the power allocation problem of a downlink Single Input Single Output (SISO) NOMA system. We aim to maximize the total system throughput, while maintaining a good level of fairness among the users. The system available bandwidth is divided into a number of subbands, and each subband is assigned to two users. The combination of the assigned users is based on the performance of each one, in terms of throughput. Two methods are reported for allocating power to each subband. In the first method, the maximum transmission power is equally divided to all bands, whereas, in the second, we use the Particle Swarm Optimization (PSO) algorithm to allocate the power to each band aiming to maximize the total system throughput. The power allocated to each band is further divided into the two multiplexed users. A series of extensive simulations and comparisons were employed to evaluate our proposed power allocation methods. The results indicate that our methods provide a significant improvement over the compared ones, both in terms of achieved system throughput and fairness among the users. © 2019 IEEE.},
keywords = {Internet of Things (IoT), Non Orthogonal Multiple Access (NOMA), Particle Swarm Optimization (PSO)},
pubstate = {published},
tppubtype = {conference}
}
The exponential proliferation of the Internet of Things (IoT) concept renders it an integral part of the emerging 5th Generation (5G) of mobile networks. In order to address the requirement of massive IoT-type connections, the spectrum has to be better utilized. Non-Orthogonal Multiple Access (NOMA) is a promising technique that can effectively improve the spectrum efficiency. In this paper, we consider the power allocation problem of a downlink Single Input Single Output (SISO) NOMA system. We aim to maximize the total system throughput, while maintaining a good level of fairness among the users. The system available bandwidth is divided into a number of subbands, and each subband is assigned to two users. The combination of the assigned users is based on the performance of each one, in terms of throughput. Two methods are reported for allocating power to each subband. In the first method, the maximum transmission power is equally divided to all bands, whereas, in the second, we use the Particle Swarm Optimization (PSO) algorithm to allocate the power to each band aiming to maximize the total system throughput. The power allocated to each band is further divided into the two multiplexed users. A series of extensive simulations and comparisons were employed to evaluate our proposed power allocation methods. The results indicate that our methods provide a significant improvement over the compared ones, both in terms of achieved system throughput and fairness among the users. © 2019 IEEE.
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