Enhancing Bit Rate using Hybrid Access Method based on NOMA and
CDMA for 5G Networks
YAHIA HASAN JAZYAH
Faculty of Computer Studies,
Arab Open University,
KUWAIT
Abstract: - Non-orthogonal multiple Access (NOMA) is a transformative wireless communication technique
that has gained significant attention for its ability to enhance spectral efficiency and capacity in modern
communication systems. Unlike traditional orthogonal multiple access methods, which allocate separate
frequency, time, or code resources to users, NOMA controls power domain multiplexing to enable multiple
users to share the same time and frequency resources simultaneously. This work provides comprehensive details
about NOMA and proposes a hybrid access method that benefits from NOMA and the Code Division Multiple
Access (CDMA) to enhance the bit rate in the downlink from Base Station to mobile nodes. Simulation results
prove that the proposed technique enhances the bit rate for users even the far ones from the base station.
Key-Words: - 5G, CDMA, NOMA, SIC.
Received: June 23, 2022. Revised: August 21, 2023. Accepted: September 19, 2023. Published: October 23, 2023.
1 Introduction
NOMA is an advanced multiple-access technique
that is used in wireless communication systems,
especially in 5G networks. It is designed to
improve spectral efficiency, increase capacity, and
enhance user fairness by allowing multiple users to
share communication channels at the same time and
using the same frequency.
In traditional multiple access schemes such as
Frequency Division Multiple Access (FDMA),
adopted in 1G, Time Division Multiple Access
(TDMA), used in 2G, and CDMA, adopted in 3G,
users are allocated separate frequency, time, or
code resources to avoid interference between them.
However, those technologies suffer from
limitations in terms of capacity and efficiency.
NOMA takes a different approach by allowing
users' signals to overlap in the power domain. This
means that multiple users can transmit their data
using the same time and frequency resources, with
their signals superimposed on each other. The key
idea behind NOMA is to allocate different power
levels and modulation schemes to different users
and enable their signals to coexist and be separated
at the receiver.
In NOMA, users with better channel conditions
are assigned lower power levels, while users with
poor channel conditions receive higher power
levels. The imbalance of power allows the receiver
to distinguish and separate users' signals using
advanced signal processing techniques. Interference
cancellation algorithms, such as Successive
Interference Cancellation (SIC), are employed to
decode the signals of different users one by one and
improve the overall system performance.
Although NOMA has witnessed high
performance in comparison to its counterpart of
Orthogonal Frequency Division Multiple Access
(OFDMA) and former techniques such as TDMA
and FDMA, it has some limitations and challenges.
This work highlights the NOMA technology,
its limitations, challenges, and benefits. In addition
to proposing a hybrid access technique that benefits
from both CDMA and NOMA to produce a higher
bit rate for the same transmitted power when
mobile nodes receive a signal from the base station
(downlink) for 5G networks. Matlab is used to
simulate the code. Simulation results show that the
proposed algorithm outperforms NOMA in terms
of bit rate.
The remaining of this article is organized as
follows: part 2 presents general information about
NOMA, part 3 presents the benefits of NOMA, part
4 presents NOMA transmission system, part 5
presents NOMA in MIMO, part 6 presents the
spectrum allocation in NOMA, part 7 presents
Successive Interference Cancellation (SIC), part 8
presents issues and challenges of NOMA, part 9
presents Code Division Multiple Access (CDMA),
part 10 presents general hybrid systems that can be
formed by NOMA and CDMA, part 11 presents the
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proposed solution and simulation results, and
finally, part 12 the conclusion.
2 What is NOMA
1G networks uses FDMA, [1], as the access
technique, it enables users to share the
communication channel by sending their signals at
the same time but using different frequency. 2G
networks use TDMA, [2], as the access technique,
it enables users to share the communication channel
by using the same frequency but each user is
allocated a different time slot, and because the
timing is very fast, it sounds like users are sending
and receiving at the same time. 3G adopted a
different technology, CDMA, [2], where users are
sending and receiving simultaneously, and using
the same frequency, but each user is allocated a
different code for its data to be distinguished and
extracted. 4G adopted the OFDMA, [1], a scheme
to achieve higher data rate and spectrum efficiency.
Figure 1 presents the differences between the
FDMA, TDMA, and CDMA.
NOMA, [3], uses a different approach than the
methods mentioned above.
Multiple users share the same time and
frequency resources for simultaneous transmission
and reception. The sharing of resources in NOMA
is achieved through power domain multiplexing,
where users' signals are allocated different power
levels to allow overlapping transmissions.
Users in NOMA are allocated different power
levels based on their channel conditions, quality of
service requirements, or other criteria. Weaker
users are assigned higher power levels, while
stronger users receive lower power levels. This
power allocation determines how the users' signals
are superimposed and shared in the power domain.
The signals of different users are superimposed
using different power levels. Users’ signals are
multiplied by their respective power allocation
coefficients and then added together, resulting in
the superimposed signal that is transmitted over the
shared resources.
The superimposed signal, which contains the
signals of multiple users, is transmitted at the same
time and frequency. The overlapping nature of the
transmissions is possible due to the varying power
levels assigned to each user, which allows their
signals to be separated at the receiver.
On the receiver side, sophisticated signal
processing techniques are employed to separate and
decode the individual user’s signal. The receiver
performs interference cancellation and detection
algorithms to differentiate and extract the signals of
different users from the received superimposed
signal.
Once the individual user signal is separated, it
is decoded using appropriate decoding algorithms
based on the modulation and coding schemes
employed by each user. The decoded signals
contain the users' intended information.
The network layer (layer 3) in a wireless
communication system is responsible for routing
and forwarding data packets between nodes and
handling the logical addressing of devices within
the network. The network layer operates
independently of the bit rate and modulation
techniques that are used at the physical layer, such
as NOMA, [4].
3 Benefits of NOMA
NOMA offers several benefits in wireless
communication systems, [5].
It significantly improves spectral efficiency by
allowing multiple users to share the same time and
frequency resources simultaneously. Unlike
traditional orthogonal multiple access schemes,
which allocate non-overlapping resources to users,
NOMA enables overlapping of users’ signals in the
power domain. This efficient utilization of
resources leads to higher data rates and capacity
within the available bandwidth.
NOMA increases the capacity of the system by
allowing multiple users to access the same
resources simultaneously. It enables more users to
be served within a given time and frequency
allocation. This is particularly valuable in scenarios
with a high density of users or when the spectrum
availability is limited.
It offers enhanced user fairness by dynamically
allocating power levels based on users' channel
FDMA TDMA CDMA
Fig. 1: The difference between FDMA, TDMA,
and CDMA
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conditions. Weaker users are allocated higher
power levels, allowing them to achieve better
performance and coverage. This ensures that all
users have access to the resources according to
their requirements, which results in a more
equitable distribution of system resources.
It provides flexibility in managing and
allocating resources. It can adapt to varying
channel conditions and quality of service demands
by adjusting power allocations and modulation
schemes for different users. This adaptability
allows NOMA to cater to the diverse needs of
different users, applications, and services.
NOMA can be seamlessly integrated with existing
multiple access technologies, such as OFDMA used
in 4G systems. It can be deployed as a
complementary technique for further enhancement
of the performance and efficiency of the system.
It can reduce latency in wireless communications
by enabling simultaneous transmission and
reception, such as real-time voice and video
communication, and IoT devices.
It can simplify the network design by reducing
the need for complex resource allocation and
scheduling algorithms. It depends on streamlining
rather than time in the allocation of resources to
users.
NOMA is adaptable and can be implemented in
different wireless communication technologies,
including 4G, 5G networks, and future wireless
standards. And so, it can support diverse services
with different QoS requirements, enabling the
coexistence of low-latency services with high
throughput in the same network.
NOMA is useful in Multi-User Multiple Input
Multiple Output (MU-MIMO) applications, where
multiple users share the same time and frequency
resources.
It is considered a key enabling technology for
future wireless communication systems, such as 5G
and beyond. It addresses the increasing demand for
high data rates, massive connectivity, and diverse
applications. NOMA provides a foundation for
accommodating the exponential growth of
connected devices and emerging communication
requirements.
4 NOMA Transmission System
NOMA transmission system, [6], involves several
algorithms to efficiently allocate resources, assign
power levels, and perform superposition coding
(Figure 2), while specific algorithms can vary
based on system design and optimization
objectives. By and large, the general algorithmic
flow for the NOMA transmission system is as
follows:
User Selection algorithm that measures the
channel conditions for all users ranks the users
based on channel quality or other criteria, and
selects the users to be multiplexed, considering
factors such as available resources and QoS
requirements.
Resource Allocation algorithm that divides the
available time-frequency resources into resource
blocks, allocates resource blocks to the selected
users, ensures efficient utilization of the available
spectrum, and considers factors such as channel
conditions, user requirements, and system
optimization objectives during resource allocation.
Power Allocation algorithm that assigns different
power levels to the signals of the multiplexed users,
considers the channel conditions, QoS
requirements, and fairness criteria, and optimizes
power allocation to maximize system performance,
such as maximizing the sum-rate or satisfying
target Signal to Interference and Noise Ratio
(SINR) requirements.
Signal Encoding algorithm that encodes the data of
each user using suitable encoding techniques (such
as error correction coding) for reliable
transmission. While the Modulation algorithm
maps the encoded data into a suitable modulation
scheme, such as Quadrature Amplitude Modulation
Encoder
Interleaving
and Scrambling
Blocks
Resource
Display
Blocks
New Modulator
New Interleave
New Scramble
and Interleave
I/P Signal
O/P Signal
Fig. 2: Transmission system in NOMA
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(QAM), [7], to generate the modulated symbols.
Superposition Coding algorithm that combines the
modulated symbols of the multiplexed users using
superposition coding, and linearly superimposes the
signals to create a composite signal that carries the
information of all multiplexed users
simultaneously.
The precoding algorithm is an optional
algorithm that applies precoding techniques, such
as beamforming or spatial multiplexing, to optimize
transmission performance when multiple antennas
are available, enhance signal quality, mitigate
interference, and improve spatial separation of the
users' signals.
Transmitting the Composite Signal algorithm
that transmits the composite signal over the
wireless channel using the assigned power levels
and modulation scheme, and utilizes advanced
techniques such as beamforming or multi-antenna
transmission to improve signal propagation and
coverage.
Specific algorithms are employed in the
NOMA transmission system that can vary based on
the network requirements, deployment situation,
and system optimization goals. Various
optimization algorithms, such as convex
optimization, game theory, or machine learning
techniques, may be utilized to enhance the
performance and efficiency of the NOMA
transmission system.
5 NOMA in MIMO
Multiple-Input Multiple-Output (MIMO), [8], is a
technology that utilizes multiple antennas at both
the transmitter and receiver to improve the capacity
and reliability of wireless communication systems.
It takes advantage of the spatial diversity and
multiplexing gain which are provided by multiple
antennas.
Both technologies together can further enhance
the performance of wireless networks. NOMA can
be applied to a MIMO system in different
approaches such as:
Spatial NOMA, where in a MIMO system,
different users can be assigned different spatial
resource blocks by using beamforming techniques.
NOMA can be applied by allowing users in the
same spatial resource block to share the same time-
frequency resources, this allows for increased
spectral efficiency and capacity.
Power-domain NOMA, where in a MIMO
system, users with better channel conditions can be
assigned higher power levels in comparison to
users with weaker channel conditions. By applying
power-domain NOMA, multiple users can share the
same time-frequency resources using different
power levels, this enables better utilization of the
available resources and improves the overall
system capacity.
Hybrid NOMA-MIMO, [9], where a hybrid
approach can be used by combining the concepts of
NOMA and MIMO. This involves using both
power-domain and spatial-domain NOMA
techniques in a MIMO system. By utilizing
multiple antennas and allocating different power
levels to users, both gains of spatial and power
domain multiplexing can be achieved
simultaneously.
6 Spectrum Allocation and Power
Management
Spectrum allocation in NOMA, [10], plays a crucial
role in determining how resources such as time,
frequency, and power are allocated among multiple
users. Unlike traditional orthogonal multiple access
schemes where each user is allocated separate
orthogonal resources, NOMA allows multiple users
to share the same time-frequency resources non-
orthogonally.
In NOMA, users are distinguished based on
their channel conditions, and they are assigned
different power levels and data streams to enable
simultaneous transmission and reception. The
spectrum allocation in NOMA involves different
key aspects.
Users in NOMA are allocated different power
levels based on their channel conditions. Users with
better channel conditions are assigned higher power
Fig. 3: Downlink NOMA, single cell, one BS and
two users, [11]
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levels, while users with weaker channel conditions
are allocated lower power levels. This power
allocation ensures that users with poorer channel
conditions can still decode their signals by treating
other users’ signals as interference (Figure 3).
Users are typically allocated the same time-
frequency resource blocks, which can be
subcarriers in the frequency domain or subframes
in the time domain. Each user's data is superposed
and transmitted in the same resource block, which
allows for simultaneous transmission and reception.
The allocation of resource blocks can be based on
scheduling algorithms that consider the channel
conditions, user priorities, and QoS requirements.
NOMA often employs user grouping
techniques to further enhance system performance.
Users with similar channel conditions are grouped
and assigned to the same resource blocks. This
grouping facilitates the power-domain multiplexing
gain and helps to improve the overall spectral
efficiency of the system.
Accurate channel estimation is vital in NOMA
to determine the user’s specific power levels and
mitigate inter-user interference. Robust channel
estimation techniques are used to estimate the
Channel State Information (CSI) of each user,
which is then utilized for power allocation and
interference cancellation.
The specific approach of spectrum allocation in
NOMA can vary depending on the system design,
deployment situation, and performance objectives.
Advanced algorithms and optimization techniques
are employed to maximize the system capacity,
throughput, and fairness while considering the QoS
requirements of different users.
7 Successive Interference
Cancellation (SIC)
SIC, [12], is a signal processing technique used in
wireless communication systems to separate
multiple signals transmitted simultaneously over
the same frequency band.
When multiple users transmit their signals over the
same frequency using different power levels, those
signals are superimposed and transmitted to the
receiver. To decode each user's data, the receiver
needs to separate the signals from each other and
cancel the interference caused by stronger signals.
The algorithm of SIC is as follows:
1. The receiver detects the signal with the
strongest power level and decodes its data
using an advanced signal processing algorithm.
2. The decoded data is subtracted from the
received signal, which removes the interference
caused by the strongest signal.
3. The remaining signal, which includes the
signals from the other users, is then processed
to detect the signal from the user with the next
strongest power level. The detected signal is
decoded, and its data is subtracted from the
received signal, which again removes the
interference caused by the second strongest
signal.
4. This process continues until all the signals have
been detected and decoded.
SIC algorithm in a wireless communication system
can be represented mathematically as follows:
Assuming n users are transmitting their signals over
the same frequency band in a NOMA system. The
received signal at the receiver can be expressed
using (1):
Pr =�√P_k ∗h_k ∗x_k + N
n
i=1 (1)
where Pr is the received signal, P_k is the power
allocated to user n, h_k is the channel gain of user
n, x_k is the signal transmitted by user k, and N is
the additive noise.
To decode the signal from user 1 with the strongest
power level, the receiver estimates its signal using
maximum likelihood detection, as shown in (2).
R_1 = Max (|Pr - √P_1 * h_1 * x_1|^2) (2)
where R_1 is the estimated signal of user 1.
The estimated signal R_1 is then subtracted from
the received signal Pr, which results in the
interference cancellation, see (3)
Pr_1 = Pr - √P_1 * h_1 * R_1 (3)
The receiver then estimates the signal from user 2,
which can be represented using (4).
R_2 = Max (|Pr_1 - √P_2 * h_2 * x_2|^2) (4)
The estimated signal R_2 is subtracted from Pr_1
to obtain Pr_2 using (5)
Pr_2 = Pr_1 - √P_2 * h_2 * R_2 (5)
This process continues until all the signals from all
users have been detected and decoded.
By canceling out the interference caused by
stronger signals, SIC enables the receiver to detect
weaker signals accurately, which improves the
overall performance of the wireless communication
system.
8 Issues and Challenges
Although NOMA provides several advantages,
there are some challenges and issues that need to be
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addressed for its successful implementation, [13],
[14].
NOMA depends on the concept that users
consider other signals as interference. Managing
this interference is crucial to maintaining reliable
communication. Interference cancellation
techniques, such as SIC, are used to mitigate the
interference effects. However, the effectiveness of
interference cancellation depends on accurate
channel estimation, receiver complexity, and the
number of users who are sharing the same resource.
Accurate Channel State Information (CSI), [15], is
essential for successful NOMA operation. Channel
estimation becomes challenging in NOMA due to
the presence of co-channel interference and the
need to estimate the channels of multiple users in
the same resource block. Estimating channels
accurately becomes more difficult in cases with fast
fading, mobility, and varying channel conditions.
NOMA depends on appropriate user pairing and
grouping to maximize system performance.
Selecting the optimal user pairs and groups is based
on channel conditions, which is a complex task.
Incorrect pairing or grouping decisions can lead to
degraded performance and unfair resource
allocation.
NOMA introduces additional complexity in
terms of interference cancellation, power
allocation, and receiver design in comparison to
traditional orthogonal multiple access schemes. The
increased complexity can impact system
implementation, receiver hardware, and
computational requirements. Moreover, the
signaling overhead associated with channel
estimation, power control, and user grouping can
reduce the available resources and increase the
system overhead.
Ensuring fairness among users and providing
satisfactory QoS for all users are challenging in
NOMA. Users with better channel conditions
receive more power allocations, potentially causing
performance imbalance and reduced fairness.
Managing QoS requirements while maximizing
system capacity requires careful resource allocation
strategies and scheduling algorithms.
Implementing NOMA in existing wireless
communication systems can be challenging due to
compatibility issues. NOMA often requires
modifications to the physical layer and higher-layer
protocols, which may not be compatible with
legacy devices or infrastructure. Ensuring
backward compatibility or gradual migration to
NOMA-based systems can be a significant
challenge.
Addressing these issues in NOMA requires
advanced algorithm design, optimization
techniques, and system-level considerations.
9 Code Division Multiple Access
CDMA, [2], is a multiple-access technique used in
wireless communication systems (3G). It allows
multiple users to share the same frequency band
simultaneously by assigning unique codes to each
user.
CDMA assigns a unique spreading code to
each user, which is used to spread the user's signal
over a wide frequency band. The spreading codes
are designed to have good correlation properties,
ensuring that different users' signals can be
separated at the receiver. The spreading codes are
typically pseudorandom binary sequences.
Multiple users can transmit their signals
simultaneously within the same frequency band.
The unique spreading codes assigned to each user
allow their signals to coexist and be distinguished
at the receiver based on the correlation properties
of the codes.
CDMA systems often employ power control
mechanisms to regulate the transmitted power of
each user. Power control is essential to mitigate
interference and maintain a desired Signal-to-
signal-to-interference ratio (SIR) at the receiver.
Power control algorithms adjust the transmitted
power levels based on the channel conditions and
system requirements.
It exhibits a soft capacity limit that the system
capacity gradually decreases when more users are
added. CDMA can accommodate more users by
reducing the data rate or allocating less power to
each user. The flexibility of CDMA allows for
more dynamic resource allocation and adaptation to
varying traffic demands.
CDMA systems can exploit the inherent
interference rejection capability that is provided by
the spreading codes. Correlation at the receiver side
can separate and recover the desired user's signal
while rejecting interference from other users.
However, the performance of interference rejection
depends on the code properties, Signal-to-Noise
Ratio (SNR), and interference levels.
CDMA has robust behavior against multipath
fading and interference. The spreading of signals
over a wide bandwidth helps combat fading effects
by spreading the signal energy across multiple
frequencies. This allows for improved signal
reception even in challenging radio propagation
environments.
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While CDMA offers several advantages, there are
some challenges and issues associated with its
implementation.
The near-far problem occurs when users in
close proximity to the Base Station (BS) transmit at
high power levels, causing interference to users
farther away. This phenomenon can degrade the
system performance and impact the capacity of
CDMA systems. Power control algorithms are
employed to mitigate the near-far problem by
adjusting the transmitted power levels of users
based on their channel conditions. However,
achieving efficient power control across all users in
dynamic environments can be challenging.
CDMA systems are susceptible to interference
from other CDMA users operating in the same
frequency band. This includes both co-channel
interference from other users and adjacent-channel
interference. The presence of interference can
degrade the signal quality and increase the Bit
Error Rate (BER) for CDMA users. Advanced
receiving techniques, such as Multiuser Detection
(MUD) algorithms, [15], are used to mitigate
interference and improve system performance.
However, the complexity and computational
requirements of MUD algorithms can be
significant.
Although CDMA has a soft capacity limit, the
achievable capacity is still influenced by factors
such as the available bandwidth, signal quality, and
interference levels. As the number of active users
increases, the capacity of CDMA systems gradually
decreases due to the limited spreading gain and
increased interference. Efficient resource allocation
and interference management techniques are
required to maximize the system's capacity while
maintaining satisfactory performance.
CDMA systems [16] involve complex receiver
design, particularly for MUD and interference
cancellation.
CDMA systems require accurate timing and
synchronization between the transmitter and
receiver to maintain orthogonality and maximize
system performance. Timing and synchronization
errors can cause interference among users and
degrade the system's overall performance.
Achieving precise synchronization can be
challenging, especially in wireless environments
with multipath propagation, fading, and mobility.
CDMA operates in a wide frequency band due
to its spreading technique, but it still requires a
specific band allocation for deployment. This can
lead to limited flexibility in spectrum allocation and
potential conflicts with other communication
systems or services.
10 Hybrid System - NOMA and
CDMA
Creating a hybrid system between NOMA and
CDMA involves combining these two multiple
access techniques to leverage their advantages.
NOMA allows multiple users to share the same
frequency and time resources, while CDMA
assigns a unique code to each user to distinguish its
signal. a hybrid NOMA-CDMA system may
include the following:
10.1 System Architecture and Design
This model defines the overall architecture of a
hybrid system, including the number of users, base
stations, and available frequency bands. It decides
how NOMA and CDMA will be integrated, for
example, NOMA could be used for certain users
and CDMA for others.
10.2 User Grouping
This model divides users into different groups
based on their channel conditions and QoS
requirements.
10.3 Transmission Process
This model encodes the signals using superposition
coding, where the base station sends multiple
signals simultaneously to all users. Then, signals
are decoded at the receiver using SIC or other
advanced detection techniques.
On the other hand, CDMA assigns unique
spreading codes to each user to separate their
signals. While the receiver uses correlation-based
decoding to recover the original data.
10.4 Interference Management:
Both NOMA and CDMA involve managing
interference and designing interference mitigation
techniques to ensure efficient signal recovery for all
users.
NOMA users can be considered using advanced
interference cancellation techniques, while CDMA
users can be considered interference rejection
techniques.
The only hybrid system that uses NOMA and
CDMA is proposed by, [17], which differentiates
inter-cluster users based on spreading codes, while
intra-cluster users are differentiated based on
different power levels.
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11 Proposed Solution and Simulation
Results
Integrating CDMA into NOMA can be challenging,
as these multiple access schemes are fundamentally
different. CDMA depends on orthogonal spreading
codes to separate users, while NOMA depends on
power domain separation and SIC. However, it's
possible to adapt CDMA principles to a NOMA
framework in certain scenarios. Next, a simplified
mathematical framework is provided to illustrate
the integration of CDMA into a NOMA system:
In a typical CDMA system, each user is
assigned a unique orthogonal spreading code (Ci).
In this integration scenario, power and modulating
data symbols are allocated based on codes (Ci).
Consider a user NOMA system as an example.
Different power levels are allocated to the users
based on their channel conditions and QoS
requirements, whereas P1 and P2 are the allocated
powers to user 1 and user 2, respectively.
Considering the Quadrature Phase Shift Keying
(QPSK) as modulation schemes to map users’ data
symbols onto complex symbols (s1 and s2) for user
1 and user 2, respectively.
Creating the superimposed signal for
transmission, which is the sum of the individual
user signals weighted by their allocated power, (6)
x = √(P1)*s1+√(P2)*s2 (6)
The transmitted signal (x) is affected by channel
conditions as it propagates to the receiver until it is
received as shown in (7).
y = h1*x + h2*x + n (7)
Where h1 and h2 represent the channel gains for
user 1 and user 2, and n is the additive white
Gaussian noise.
On the receiver side, SIC is applied to decode
the signals as described previously in section 7.
Considering user 1 has a stronger signal than user
2, then the user 1 signal will be decoded first.
y1=h1*√(P1)*s1+h1*√(P2)*s2 + n (8)
Then, subtract the signal of user 1 from the
received one to decode the signal of user 2 as
shown in equation 9.
y2 = (y1=h1*√P1*s1 + h1*√P2*s2 + n) - h1*√P1
*s1 (9)
Finally, proper demodulation and decoding are
used to extract the original data symbols of users
from (y1 and y2).
In the next scenario, each user is assigned k-
codes (k=2 as an example) using the CDMA
principle, and so, the user can receive k-times
several bits using the same power level in NOMA,
knowing that detection and extraction of the user’s
data in CDMA is done by pure mathematical
calculation (correlation), that does not need heavy
processing or energy consumption, and so, does not
reflect negatively on the draining power from the
battery of mobile node.
Then, the BS sends two messages to users 1
(far from BS) and user 2 (near to BS), the power
allocation factors α1 and α2 are allocated to user 1
and user 2 respectively, given that (α1 + α2 = 1).
Simulation results using Matlab represent the bit
rate for both users when NOMA is adopted as
shown in Figure 4.
Fig. 4: Capacity in NOMA vs transmitted power
After applying CDMA to NOMA as a hybrid
system with the same conditions applied before,
and two codes are assigned to each user, simulation
results show that the data rate is increased when
two CDMA codes are allocated to each user for the
same transmitted power, as shown in Figure 5.
The previous results show that CDMA can
enhance the bit rate of users, especially those far
from BS based on the number of allocated CDMA
codes.
In communication systems such as 5G, the
numerical stability is not associated with the
calculation of the received bit rate, it is generally,
related to the stability and accuracy of numerical
methods and algorithms used in computational
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DOI: 10.37394/23204.2023.22.12
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simulations and mathematical calculations. And so,
in the context of the calculation of the received bit
rate, stability is more related to signal processing
and system performance rather than numerical
stability.
Fig. 5: Capacity in Hybrid NOMA-CDMA vs
transmitted power
However, to calculate the received bit rate in a
communication system, factors such as the
modulation scheme, channel conditions, SNR,
bandwidth, and noise. The received bit rate can be
calculated using Shannon's equation:
C = B*log2(1+SNR) (10)
Where B is the channel bandwidth
12 Conclusion
This work presents comprehensive and detailed
information about CDMA, NOMA, and related
information to NOMA such as SIC, its relation to
MIMO, etc. It is clear that NOMA has very high
advantages over other access methods such as
CDMA and OFDMA, but still faces issues and
challenges. Nevertheless, it provides high capacity,
low latency, high throughput, and improves
efficiency. This work illustrates all related topics
about NOMA in addition to proposing a hybrid
access method; that merges between CDMA and
NOMA to increase the bit rate in case of a
downlink from base station to mobile node.
Simulation results show that the proposed
technique enhances the bit rate of users even when
the user is far away from the base station.
In the future, work will focus on minimizing the
consumed energy in processing the received signal
and so increasing the lifetime of mobile battery.
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Volume 22, 2023
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Yahia Jazyah as a single author, carried out all the
stages of the research including the letirature
review, implementation of the algorithm in Matlab,
simulation and optimization, testing and collecting
the results, and writing the manuscript.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflict of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.e
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DOI: 10.37394/23204.2023.22.12
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Volume 22, 2023