RECEIVER ARCHITECTURES AND ALGORITHMS FOR NON-ORTHOGONAL MULTIPLE ACCESS
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Date
2020-05
Authors
Manglayev, Talgat
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
Multiple access (MA) schemes in cellular systems aim to provide high throughput to multiple
users simultaneously while utilising the network resources efficiently. Traditionally,
each user in the network is assigned a fraction of resources (such as slots in time or frequency)
to operate so that multi-user interference is avoided. These schemes are named
as ‘orthogonal multiple access’ (OMA) and are the basis of most cellular standards – from
the earliest first generation up to the current fourth-generation systems. Non-orthogonal
multiple access (NOMA) on the other hand is a novel method that allows all the users in
the network to operate in the entire available spectrum at the same time which enables
significant improvement in the system throughput.
While providing increased throughput, NOMA requires high computational power in
order to implement sophisticated interference cancellation algorithms at each user terminal,
as well as power allocation schemes at the base station. As a potential candidate
for the fifth-generation networks (5G), NOMA must meet certain requirements, and computational
efficiency is essential for reduced latency. Recently graphics processing units
(GPUs), which were initially intended for outputting images to display, appeared as an
alternative to multi-core central processing units (CPUs) for general-purpose computing.
GPUs have thousands of cores with approximately three times less frequency than a CPU
core. With their numerous advantages in executing heavy and time-consuming computations
in parallel, GPUs have become attractive platforms in a variety of fields.
The overall aim of this research is to significantly increase the scientific understanding
and technical knowledge on NOMA. This is achieved by exploring and developing novel
methods, models, designs and techniques that will facilitate the implementation of NOMA
for future generation networks. First, the achievable data rates for individual users are demonstrated in a successful
interference cancellation (SIC) based NOMA network. These results were compared
against the conventional orthogonal MA schemes with optimum power allocation and
varying fairness. In addition, a further investigation was carried out into the deficiency
of SIC receivers which can occur when a user in the networks attempts to decode other
users’ signal. Presented in the analysis is the findings from the experimental process
where the decoding order of a user with a mismatched signal was observed as well as the
significant impact on the computation time. The decoding time-difference between correct
and mismatched decoding order as a detection method of deficiency or fraudulence
in the network is then discussed. Next, a comparison is presented between the computational
times of the SIC receiver with another popular interference cancellation scheme
named ‘parallel interference cancellation’ (PIC). This was done using different platforms
specifically for an uplink NOMA system. The results showed that the computation time
of PIC scheme is significantly lower than SIC on the GPU platform even for a very large
number of available users in the network. Then, the execution time of NOMA with SIC
in the uplink of a cellular network with user clustering was examined. User clustering is
a popular method in NOMA networks that eases the sophisticated resource allocation and
network management issues. While most works found in the literature review concentrate
on the joint optimisation of user grouping and resources, this research project focused
on processing the signal detection of each cluster in parallel on the GPU platform at the
base station. Following this, parallel interference cancellation (PIC) was implemented
and compared with the existing SIC on both CPU and GPU platforms for uplink NOMAOFDM.
Architectures of the receivers were modified to fit into parallel processing. GPU
was found applicable to speed up computations in NOMA based next-generation cellular
networks outperforming up to 220 times SIC on CPU. Finally, the research presents
the power allocation problem from artificial intelligence (AI) perspective and propose a
method to predict the power allocation coefficients in a downlink NOMA system. The
results of the research show a close-to-optimal sum rate with about 120 times reduced computation time. The achieved results decreases the network latency and assist NOMA
to meet 5G requirements.
Description
Keywords
artificial intelligence, AI, CPU, GPU, parallel interference cancellation, PIC, successful interference cancellation, SIC, fifth-generation networks, 5G, Multiple access, MA, multi-core central processing units, graphics processing units, Research Subject Categories::TECHNOLOGY