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Minimizing the Transaction Time Difference forNOMA-Based Mobile Edge Computing

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Non orthogonal multiple access (NOMA) and mo-bile edge computing (MEC) are evolving as key enablers forfifth generation (5G) networks as this combination can providehigh spectral efficiency, improved quality-of-service (QoS), andlower latency. This letter aims to minimize the transactiontime difference of two NOMA paired users offloading datato MEC servers by optimizing their transmission powers andcomputational resources of severs using a successive convexapproximation method. The equalization of transaction timefor paired users reduces the wastage of both frequency andcomputational resources, and improves effective throughput ofthe system to 19% on average.
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Minimizing the Transaction Time Difference for
NOMA-Based Mobile Edge Computing
Anam Yasir Kiani, Syed Ali Hassan, Binbin Su, Haris Pervaiz and Qiang Ni
Abstract—Non orthogonal multiple access (NOMA) and mo-
bile edge computing (MEC) are evolving as key enablers for
fifth generation (5G) networks as this combination can provide
high spectral efficiency, improved quality-of-service (QoS), and
lower latency. This letter aims to minimize the transaction
time difference of two NOMA paired users offloading data
to MEC servers by optimizing their transmission powers and
computational resources of severs using a successive convex
approximation method. The equalization of transaction time
for paired users reduces the wastage of both frequency and
computational resources, and improves effective throughput of
the system to 19% on average.
I. INTRODUCTION
Some of the important concerns in today’s wireless net-
works are limited resources and media arbitration. The
medium used for the transmissions is being shared by mil-
lions of devices with heavy traffic, which is expected to
increase by 1000 folds in the next decade. This would result
in services requiring high connectivity, reliability, ultra-low
latency, improved fairness and high throughput, etc. Non-
orthogonal multiple access (NOMA) has been introduced to
muddle through the demands of the epoch. One of main
purposes of NOMA is to serve multiple users by utilizing
the same resource block. NOMA provides a balanced trade-
off between the system throughput and user fairness [1] and
is being envisioned as a key technology in 5G networks. 5G
enabled devices are also expected to have latency constraint
and have computationally complex applications running on
them. For such applications, limited power and computational
capacity of mobile devices pose a problem, which can be
solved by using mobile edge computing (MEC) [2]. MEC
offloads computationally intensive data to base stations (BSs)
and access points (APs) that are equipped with powerful
servers. Servers being available at the edges result in reduction
of delay and improvement of computational efficiency [3]. The
advantages of both techniques (i.e., NOMA and MEC) have
drawn considerable attention of the researchers recently. In
NOMA-MEC, paired users offload their data to MEC servers
by using the underlying NOMA principle.
A lot of work is being done in this context. For in-
stance, [4] formulated delay minimization for NOMA-MEC
data offloading as a form of fractional programming. Pure
NOMA is also compared with hybrid NOMA and orthogonal
multiple access (OMA) for data offloading purpose. In [5],
energy consumption of MEC users utilizing uplink NOMA is
reduced by optimizing user clustering, power, frequency and
computational resource allocation. [6] proved that the total
energy minimization is a convex problem and the authors
solved it by an iterative algorithm. [7] reduced the total
system energy by optimizing allocated power, transmission
time and offloaded task portions. In [8], energy consumption
is reduced by jointly optimizing power and time allocation by
formulating the problem to a form of geometric programming.
[9] studied energy harvesting for full duplex NOMA-MEC,
where total energy consumption is minimized by efficient
power allocation, time scheduling and computing resources
allocation.
The time taken to process data (offloaded to MEC servers) is
not equal for each NOMA paired user because it is dependent
upon the amount of offloaded data and the channel conditions
of the paired users, etc. This inequality leads to under-
utilization of resources and reduced spectral efficiency. In this
letter, we propose a scheme to optimize the transaction time
of paired users and to reduce the transaction time difference
between them to improve spectral efficiency and to conserve
both frequency and computational resources. Transaction time
is the sum of transmission time and computational time. The
difference between transaction times is reduced by equalizing
transmission time and computational time separately, which is
achieved by optimizing a) power allocation and b) computa-
tional resource allocation, respectively. When the transaction
time of paired users becomes closer, the difference between
the transaction times reduces and hence the wasteful resources.
In the sequel, we describe the NOMA-MEC model followed
by optimization of transaction time difference. The results and
conclusions are presented towards the end.
II. SY ST EM MO DE L
A single cell is considered with 2Nnumber of users, which
are served by a single BS. The BS is equipped with MEC
server having CTnumber of cores each with a computational
capability of fcycles/sec and total system bandwidth is BT.
Hybrid NOMA technique is used to pair the users into N
NOMA clusters, where each cluster has two users. A single
cluster with users u1and u2is considered for study. The user
u1is located at a distance dist1from BS whereas u2is located
at a distance dist2from BS, such that dist1<dist2. Without
the loss in generality, we assume that u1is a strong user with
allocated power p1and effective channel gain h1, however u2
is a weak user with allocated power p2and effective channel
gain h2, such that p1h1> p2h2. Let p1,max and p2,max are the
maximum transmission powers that can be allocated to u1and
u2, respectively. We assume that d1bits are offloaded by u1
and d2bits are offloaded by u2to the MEC server. Complete
offloading scheme is considered, where no local computation
is being performed. Each bit offloaded by u1requires c1
cycles and that of u2requires c2cycles for computation at
MEC server. The computational complexity of offloaded data
is dependent upon offloaded data type (i.e, video data requires
more CPU cycles as compared to text data). The total system
bandwidth is divided into Nnumber of frequency resource
blocks. A single frequency resource block with bandwidth
Bw=BT/N is allocated to a NOMA cluster and shared by
paired users, similarly the cores at MEC servers are divided
into Nnumber of computational resource blocks and are
allocated to NOMA clusters. The allocated computational
resources of a cluster (i.e., Ct=CT/N) are divided among
the paired users, depending upon the complexity and amount
of data being offloaded by them. Let u1and u2are allocated
with n1and n2cores, respectively.
The transaction time of the ith user is Ti=Ttxi+Tci, i
{1,2},where Ttxiis the transmission time and Tciis the com-
putational time of the ith user, respectively. The transmission
time for the ith user is Ttxi=di
Ri,where Riis the data rate
of the ith user. In this work, the 2-user uplink NOMA cluster
is considered in which u1and u2experience channel gains
of f
h1and f
h2such that the user u1signal will be decoded
first at BS. The achievable data rate of user u1will include
the interference from the user u2whereas the achievable data
rate of user u2will include noise only. The data rates are
dependent upon the effective channel gains (i.e., h1, h2) and
the allocated powers (i.e., p1, p2), such that [10]
R1=Bwlog21 + p1h1
p2h2+σ2,(1)
R2=Bwlog21 + p2h2
σ2,(2)
where σ2=Bw×σ2,σ2is the power spectral density of
noise and the effective channel gain for ith user is hi=
e
hi
distρ
i
, where e
hiis the exponential channel gain (corresponding to
Rayleigh fading) of ith user and ρis the path loss exponent.
The amount of data offloaded and the effective channel gains
are associated with the paired users, however, the powers
are optimized to reduce their transaction time difference.
Similarly, the computational time for the ith user is given by
Tci=dici
nif,where niis the number of cores allocated to the
user iand fis the computational capacity of each MEC core.
For a given paired users, di,ciand fare fixed. The number of
computational resources allocated to the ith user is optimized
to balance the load across the cores in order to reduce the
difference between transaction time. By manipulating, it is
inferred that T1is equal to T2if
d1
R1
+d1c1
n1f=d2
R2
+d2c2
n2f
n1fd1+d1c1R1
n1fR1
=d2n2f+d2c2R2
n2fR2
d1(n1f+c1R1)
n1R1
=d2(n2f+c2R2)
n2R2
d1
d2
=R1
R2n1n2f+n1c2R2
n1n2f+n2c1R1(3)
Fig. 1: Unequal Transmission Time and Wasteful Resources
From (3), we can divide the original formulated problem into
two independent sub-problems and reformulate it as T1is
equal to T2, if Ttx1is equal to Ttx2as well as Tc1is equal
to Tc2. It is evident from Fig. 1 that unequal transmission
time results in wastage of allocated frequency resources. It
can be seen that for δtx =|Ttx1Ttx2|amount of time, the
resources are under-utilized, i.e., a new NOMA signal cannot
be initiated. As this difference increases, the spectral efficiency
of the network decreases. The transmission time, Ttx, is equal
for both the users, if d1
d2=R1
R2,where R1and R2are the
data rates of both users u1and u2, respectively. Similarly,
the disparity in amount and computational complexity of data
offloaded by paired users (i.e., allocated with equal number of
cores) results in wastage of allocated computational resources.
The computational time difference δtc =|Ttc1Ttc2|for both
the users is zero, if d1c1
n1f=d2c2
n2f,which can be written in
simplified form as d1
d2=n1c2
n2c1.
III. PROB LE M FOR MU LATI ON
The original problem described in the previous section is
given by
(P) min λ, (4a)
s.t. d1
R1
+d1c1
n1d2
R2
+d2c2
n2λ,
(4b)
d2
R2
+d2c2
n2d1
R1
+d1c1
n1λ,
(4c)
λ0(4d)
0pipi,max i∈ {1,2}(4e)
0niCti∈ {1,2}(4f)
n1+n2Ct(4g)
where the problem (P) is subjected to constraints (4e), (4f)
and (4g) i.e., the power allocated to the individual user is
positive and less than respective maximum, the number of
cores allocated to the individual user is positive and less
than total number of allocated cores, moreover, sum of cores
allocated to both the users is less than or equal to the total
number of allocated cores. As can be seen from the formation
of problem (P), it can be decomposed into two independent op-
timization sub-problems. The results for original optimization
problem and sub-problems are equivalent. The objective of
the first optimization problem is to minimize the transmission
time difference of given paired users with known di’s, by
optimizing the power allocation. From equations (1) and (2),
we have
R1+R2=Bwlog21 + p1h1+p2h2
σ2(5a)
R1Bwlog21 + p1h1
p2h2+σ2
=Bwlog21 + p1h1+p2h2
σ2R2,
(5b)
R2Bwlog21 + p2h2
σ2,(5c)
For the objective with power allocation, we introduce a new
variable µand hence the sub-problem of minimizing the trans-
mission time difference of paired users can be reformulated
as
(P1) min µ, (6a)
s.t. d1
R1
d2
R2
µ, (6b)
d2
R2
d1
R1
µ, (6c)
µ0(6d)
0pipi,max i∈ {1,2}(6e)
where the objective function (6a) is subjected to data rate (5b,
5c) and power ( 6e) constraints. By manipulating (6b), we get
µR1R2µα1α2
2d1R2d2R1,(7)
where α1and α2are real valued variables, having values such
that inequality holds. The equation (7) is equivalent to
R1R2α1,(8a)
µ α2
α2α10,(8b)
α2
2d1R2d2R1,(8c)
where (8b) is a convex linear matrix inequality (LMI), and (8c)
is non-convex. The non-convex parts in left side of (8c) can
be approximated using the Taylor series expansion to get the
approximated lower bound. By applying the first Order Taylor
Approximation, the left side of (8c) can be approximated as
α22α(j)
22+ 2α(j)
2α2α(j)
2
α22α(j)
22+ 2α(j)
2α22α(j)
22
α222α(j)
2α2α(j)
22
(9)
The right side of Eq. (9) is the first order approximation around
the point α(j)
2. By substituting Eq. (9) into left side of (8c),
the (8c) can be rewritten as follows:
2α(j)
2α2(α(j)
2)2d1R2d2R1,(10)
where jshows the number of iteration, α(j)
2denotes the value
of α2during the jth iteration. The equation (8a) is rewritten
as
R1R2β2,(11)
where β2α1. The problem (P1) defined in Eq. (6a) subject
to the constraints defined in Eq. (8b), Eq. (10) and Eq. (11) is
a convex optimization problem and can be efficiently solved
using standard convex optimization tool such as CVX [11]. It
will provide a lower bound approximation solution [12], [13]
of (P1) due to the first order Taylor approximation in Eq. (10).
Similarly, the objective of the second optimization problem
is to minimize the computational time difference of given
paired users with known dis and ci’s by optimizing the core
allocation. By introducing a new variable ζ, the sub-problem
of the computational resource allocation can be transformed
as
(P2) min ζ, (12a)
s.t. d1c1
n1
d2c2
n2
ζ, (12b)
d2c2
n2
d1c1
n1
ζ, (12c)
ζ0,(12d)
0< ni< Ct,(12e)
n1+n2Ct(12f)
where the objective function (12a) is subject to constraints
(12e), the number of cores allocated to individual user is
greater than zero and less than total cores allocated to the
cluster and (12f), the sum of cores allocated to both the users
is less than or equal to total cores allocated to the cluster. The
number of cores allocated to individual user must be greater
than zero to ensure the minimum requirement of the user. The
integer constraint is relaxed for ni. By manipulating (12b), we
get
ζn1n2ζ γ1γ2
2(d1c1)n2(d2c2)n1,(13)
where γ1and γ2are variables with real values. The equation
(13) implies
n1n2γ1,(14a)
ζ γ2
γ2γ10,(14b)
γ2
2(d1c1)n2(d2c2)n1,(14c)
where (14b) is a convex LMI. The left side of (14c) can
be approximated using the Taylor series expansion to get the
approximated lower bound. By applying the first Order Taylor
Approximation, the left side of (14c) can be approximated as
γ222γ(j)
2γ2γ(j)
22
(15)
The right side of Eq. (15) is the first order approximation
around the point γ(j)
2. By substituting Eq. (15) into left
side of (14c), the (14c) can be rewritten as follows:
2γ(j)
2γ2γ(j)
22(d1c1)n2(d2c2)n1,(16)
Fig. 2: Transaction Time difference for Benchmark (or No Optimization) Case, Approaches A, B and C
where γi’s are updated in each iteration and jshows the
number of iteration. From (14a), we have
n1n2η2,(17)
where η2γ1. The problem (P2) defined in Eq. (12a) subject
to the constraints defined in Eq. (14b), Eq. (16) and Eq. (17)
is a convex optimization problem and can be efficiently solved
using standard convex optimization tool such as CVX [11].
For a given pair of users, we obtain optimal values of power
and number of cores once the optimization is performed.
These parameters result in minimization of transaction time
difference, which is illustrated in next section.
IV. PERFORMANCE EVALUATION
The maximum power for u1,p1,max, is 2W and of u2,p2,max,
is 4W. Initially p1is 1W and p2is 2W. The dist1and dist2are
200 m and 600 m, respectively. The cluster bandwidth is 200
kHz and the path loss exponent is 3.8. The transaction time
difference is considered for three different approaches namely:
Power Optimization with Equal Core Allocation (A), Power
Optimization with Random Core Allocation (B) and proposed
Power Optimization with Optimal Core Allocation (C). The
power and core optimization is achieved by successive convex
approximation as discussed in the Section III. In equal core
allocation, the cores are equally divided between the paired
users, i.e., n1=n2. In random core allocation, the cores are
randomly divided between the paired users n1=κCtand
n2= (1κ)Ct, where, κis from uniform random distribution
varying from 0 to 1. The ratio of offloaded data amount,
i.e., d2/d1and complexity, i.e., c2/c1is varied to study their
impact on the transaction time.
Fig. 2 depicts the transaction time difference without any
power optimization and equal number of core allocation, i.e.,
benchmark case, approach A, approach B and approach C
in contour plots from left to right. It can be observed in
all plots that for a fixed value of d2/d1, different values
of c2/c1result in different transaction time differences. The
larger the transaction time difference, the more the under-
utilized resources. It can be observed that the transaction
time difference for second plot (i.e., approach A) is overall
Fig. 3: Comparison of Simulation and Optimization Results
lesser than the previous. For the same ratios of d2/d1and
c2/c1, the transaction time difference is reduced by optimizing
only the power allocations. The transaction time difference
for Approach B (i.e., third plot) is lesser than the transaction
time difference for first plot, i.e., benchmark case. However,
this difference is comparable with Approach A, as the only
difference is in the core allocation. The transaction time
difference for proposed scheme (i.e., Approach C, fourth plot
in Fig. 2), where both the power and cores are optimized, is
minimum. It is also clear that the paired users have optimal
values of d2/d1and c2/c1for which the transaction time
difference is minimum. For instance in fourth plot Fig. 2, when
d2/d1= 1.2, the transaction time difference is 2 seconds for
c2/c1of 0.7. As d2/d1is increased to 1.7, the transaction time
gap jumps to 35 seconds for the same ratio of c2/c1. Similarly,
when d2/d1is decreased to 0.7, the transaction time difference
increases to 20 seconds.
To validate the proposed solution, the approaches A, B and
C are also solved heuristically by searching over the whole
solution space labelled as ”Simulation” and compared with the
results obtained for the approaches A, B and C using succes-
sive convex optimization method labelled as ”Optimization” in
Fig. 4: Effective System Throughput
Fig.3. The maximum number of iterations for the Optimization
results for the Approaches A, B and C is set to 100. In Fig.3
when d2/d1is 0.4, the transaction time difference without
optimization is 822 seconds, approximately 402 seconds for
both heuristic and optimized solutions of Approach A and 467
seconds for Approach B. The transaction time difference for
Approach C goes to 196.1 and 61.58 seconds for optimized
and heuristic solutions,respectively. The difference between
the simulation and optimization results is due to use of Taylor
series expansion for approximation.
We now illustrate the effect of reducing the transaction
time on the effective throughput of the system. The effective
throughput of the system is given by
Φeff =P2
i=1 Ri
max (T1, T2),(18)
where the numerator is the sum of achieved data rates by
the paired users while the denominator is the maximum of
transaction times of the paired users. As both the users are
paired, therefore, the resources allocated to them are free only
when both of them complete their transactions, hence the de-
nominator is characterized by the max(.) operator. A decrease
in effective transaction time increases the system’s effective
throughput. It is clear from Fig. 4 that for a fixed value of
c2/c1and a range of d2/d1, the system effective throughput
for proposed Approach C is greater than the other approaches.
It is also evident that larger is the offloaded data disparity,
the larger is the difference between the system’s effective
throughput for the compared schemes. The reason behind
this trend is the optimal core allocation. When the offloaded
data is same in characteristic (i.e., amount and complexity is
same), the cores allocation for the schemes are same (i.e.,
equal number of cores for no optimization, Approach A and
Approach C) and the difference in the throughput appears only
because of the power allocation. However, as the offloaded
data disparity increases, the proposed scheme outperforms
others. The average increase in the system effective throughput
is 19% for the case shown in Fig. 4.
V. CONCLUSION
In this letter, it has been shown that the transaction time
plays an important role in improving the overall resource
utilization and the transaction time difference of two users is
minimized by optimizing both the transmission powers and
computational resources allocation independently. The pro-
posed optimization resulted in increased effective throughput
of the system. As a future direction to this work, the joint
problem can also be investigated while considering correlation
both communication and computation resources. The approach
can be extended to multiple users in a NOMA cluster. A
data aware NOMA clustering scheme can be used where
the users are paired considering both the power disparity
as well as their data offloading requirements, which can
contribute further towards improvement of spectral efficiency
and system’s effective throughput.
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Thesis
With the tremendous increase in the number of mobile devices, and a plethora of multimedia services, there is a demand for the development of a new access schemes that can have properties of high capacity and spectral efficiency, low latency, and capabilities to accommodate a massive number of devices. Non-orthogonal multiple access (NOMA) is proposed as a promising access technology for beyond fifth generation (B5G) and sixth generation (6G) communication systems having all the desired properties. Unlike orthogonal multiple access (OMA), the same physical resource (e.g., frequency and time), but with different power is allocated to multiple users in NOMA, which greatly increases spectral efficiency. The combination of non-orthogonal multiple access (NOMA) and cooperative communications can be a suitable solution for fifth generation (5G) and beyond 5G (B5G) wireless systems with massive connectivity, because it can improved fairness compared to the noncooperative NOMA. This thesis offers a comprehensive approach to this recently emerging technology, from the fundamental concepts of NOMA, to its combination with space-time block codes (STBC) to the cooperate with users with weak channel conditions, as well as analysis of the effect of practical impairments such as timing offsets, imperfect successive interference cancellation (SIC) and imperfect channel state information (CSI). We derive closed-form expressions of the received signals in the presence of such realistic impairments and then use them to evaluate outage probability. Further, we provide intuitive insights into the impact of each impairment on the outage performance through asymptotic analysis at high transmit signal-to-noise ratio (SINR). We also compare the complexity of STBC-CNOMA with existing cooperative NOMA protocols for a given number of users. In addition, to meet the highly diverse quality-of-service (QoS) requirements of Internet of Things ( IoT) devices, we propose a novel Q-learning-based self-organizing and self-optimizing multiple access technique for radio resource allocation in NOMA systems. We optimize the sum-rate and spectral efficiency (SE) of the overall network by using a Q-learning algorithm that assigns optimal bandwidth and power to the users with the same range of data rate requirements. Simulation results show that the proposed algorithm can significantly enhance the overall system throughput and SE, while satisfying heterogeneous QoS requirements.
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The 6 Generation wireless network is expected to integrate terrestrial, sea and air communications into a robust network that is more reliable and fast and can support a huge number of devices with very low latency requirements. Multiple access (NOMA), small cell communication, fog/edge computing, etc., as the main technologies in achieving beyond 5G (B5G) and 6G communications. Researchers worldwide are proposing cutting-edge technologies such as artificial intelligence (AI) / machine learning (ML), quantum communication / quantum machine learning (QML), block chain, tera-Hertz, millimeter wave communication, tactile internet, and asymmetrical. In this article, we provide a detailed overview of the dimensions of the 6G network with the air interface and the potential technologies associated with it. More specifically, we highlight the use cases and applications of the proposed 6G networks in different dimensions. Furthermore, we also discuss key performance indicators (KPIs) for the B5G/6G network and the challenges and future research opportunities in this area.
... Furthermore, there are many advantages when applying MEC to WSNs, including decreasing traffic passing through infrastructure [10], reducing the latency for applications [11], [12], and raising the exactitude rate of task processing [14]. However, an MEC-WSN network deploying a billion SNs in the future will create a massive amount of IoT data transmitted in the network [15]. This results in satisfying service requirements such as connection density and extremely high throughput in tight latency constraints. ...
... Many researchers have proposed the non-orthogonal multiple access (NOMA) as a promising new scheme for the B5G/6G mobile networks [40][41][42] the users are allowed to access the complete resource (frequency band) simultaneously. Some researchers have suggested the rate-splitting multiple access (RSMA) as a new access technology for 6G communication systems [43][44][45]. ...
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The sixth-generation (6G) wireless communication network is expected to integrate the terrestrial, aerial, and maritime communications into a robust network which would be more reliable, fast, and can support a massive number of devices with ultra-low latency requirements. The researchers around the globe are proposing cutting edge technologies such as arti cial intelligence (AI)/machine learning (ML), quantum com- munication/quantum machine learning (QML), blockchain, tera-Hertz and millimeter waves communication, tactile Internet, non-orthogonal multiple access (NOMA), small cells communication, fog/edge computing, etc., as the key technologies in the realiza- tion of beyond 5G (B5G) and 6G communications. In this article, we provide a detailed overview of the 6G network dimensions with air interface and associated potential technologies. More speci cally, we highlight the use cases and applications of the proposed 6G networks in various dimensions. Furthermore, we also discuss the key performance indicators (KPI) for the B5G/6G network, challenges, and future research opportunities in this domain.
... Many researchers have proposed the non-orthogonal multiple access (NOMA) as a promising new scheme for the B5G/6G mobile networks [29,30,31]. In NOMA, all of the users are allowed to access the complete resource (frequency band) simultaneously. ...
Preprint
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The sixth-generation (6G) wireless communication network is expected to integrate the terrestrial, aerial, and maritime communications into a robust network which would be more reliable, fast, and can support a massive number of devices with ultra-low latency requirements. The researchers around the globe are proposing cutting edge technologies such as artificial intelligence (AI)/machine learning (ML), quantum communication/quantum machine learning (QML), blockchain, tera-Hertz and millimeter waves communication, tactile Internet, non-orthogonal multiple access (NOMA), small cells communication, fog/edge computing, etc., as the key technologies in the realization of beyond 5G (B5G) and 6G communications. In this article, we provide a detailed overview of the 6G network dimensions with air interface and associated potential technologies. More specifically, we highlight the use cases and applications of the proposed 6G networks in various dimensions. Furthermore, we also discuss the key performance indicators (KPI) for the B5G/6G network, challenges, and future research opportunities in this domain.
... The BackCom system, consisting of multiple BNs that reflect the incident CW signal originating from a single reader, needs an efficient multiple access mechanism to accommodate the maximum number of BNs without sacrificing spectral efficiency and user fairness. Non-orthogonal multiple access (NOMA) is the prime candidate to handle multiple access in such a one-to-many BackCom uplink communication system [9], [10]. Power domain NOMA exploits the channel differences among users for multiplexing and is envisaged as an essential technology for 5G systems because of its low latency and high spectral efficiency [11], [12]. ...
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... However, the issues of high peak-to-average power ratio (PAPR) and cyclic prefix, which carries no extra information, make OFDM less attractive for future systems. Therefore, non-orthogonal multiple access (NOMA) is proposed as a promising access technology for beyond 5G/6G systems due to its capabilities to accommodate a massive number of devices, high data rates, and low latency [1]- [3]. In a NOMA system, multiple users can access the same resource block (i.e., frequency and time) simultaneously, and users are differentiated in the power domain, where the user with the strongest channel condition (the strongest user) is al-located the least power, whereas the farthest user (the weakest user) is assigned highest power level. ...
Conference Paper
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This paper investigates the application of non-orthogonal multiple access (NOMA) in millimeter wave (mmWave) communications by exploiting beamforming, user scheduling and power allocation. Random beamforming is invoked for reducing the feedback overhead of considered systems. A non-convex optimization problem for maximizing the sum rate is formulated, which is proved to be NP-hard. The branch and bound (BB) approach is invoked to obtain the ϵ-optimal power allocation policy, which is proved to converge to a global optimal solution. To elaborate further, a low complexity suboptimal approach is developed for striking a good computational complexity-optimality tradeoff, where matching theory and successive convex approximation (SCA) techniques are invoked for tackling the user scheduling and power allocation problems, respectively. Simulation results reveal that: i) the proposed low complexity solution achieves a near-optimal performance; and ii) the proposed mmWave NOMA systems is capable of outperforming conventional mmWave orthogonal multiple access (OMA) systems in terms of sum rate and the number of served users.
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This correspondence considers non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC), where the power and time allocation is jointly optimized to reduce the energy consumption of computation offloading. Closed-form expressions for the optimal power and time allocation solutions are obtained and used to establish the conditions for determining whether the conventional orthogonal multiple access (OMA), pure NOMA or hybrid NOMA should be used for MEC offloading.
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The incorporation of dynamic voltage scaling technology into computation offloading offers more flexibilities for mobile edge computing. In this paper, we investigate partial computation offloading by jointly optimizing the computational speed of smart mobile device (SMD), transmit power of SMD, and offloading ratio with two system design objectives: energy consumption of SMD minimization (ECM) and latency of application execution minimization (LM). Considering the case that the SMD is served by a single cloud server, we formulate both the ECM problem and the LM problem as nonconvex problems. To tackle the ECM problem, we recast it as a convex one with the variable substitution technique and obtain its optimal solution. To address the nonconvex and nonsmooth LM problem, we propose a locally optimal algorithm with the univariate search technique. Furthermore, we extend the scenario to a multiple cloud servers system, where the SMD could offload its computation to a set of cloud servers. In this scenario, we obtain the optimal computation distribution among cloud servers in closed form for the ECM and LM problems. Finally, extensive simulations demonstrate that our proposed algorithms can significantly reduce the energy consumption and shorten the latency with respect to the existing offloading schemes.