Non‐orthogonal multiple access schemes with partial relay selection

Lee, Sun Young; Costa, Daniel Benevides da; Vien, Quoc-Tuan; Duong, Trung Q.; Sousa, Rafael T. de

doi:10.1049/iet-com.2016.0836

Cited by 113 publications

(102 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Assuming perfect SIC, the signal‐to‐interference‐plus‐noise ratio (SINR) at the n th ordered user

U_{n} \in {scriptU}_{q}

is determined as

γ_{q, k, n} = \frac{a_{q, k, n} {normalΩ}_{q, k, n}}{{normalΩ}_{q, k, n} ā_{q, k, n} + {normalΓ}_{q, k} \underset{U_{n} \in {scriptU}_{q}}{false\sum} {scriptI}_{q, k, n} a_{q, k, n} + {normalΥ}_{q, k, n}},

where

ā_{q, k, n} = \sum_{j = n + 1}^{false| {scriptU}_{q} false|} {scriptI}_{q, k, j} a_{q, k, j}

and

ā_{q, k, false| {scriptU}_{q} false|} = 0

. Moreover, Ω q , k , n is defined as

{normalΩ}_{q, k, n} = {normalΓ}_{q, k} {normalΓ}_{q, k, n},

where

{normalΓ}_{q, k} = \frac{P g_{q, k}}{N_{0}} and {normalΓ}_{q, k, n} = \frac{P_{q, k} g_{q, k, n}}{N_{0}} .

…”

Section: Network Modelmentioning

confidence: 99%

“…whereā q,k,n, = ∑| q,k, | =n+1 a q,k, , . Moreover, Ω q,k,n, = Γ q,k Γ q,k,n, , where Γ q,k,n, = P q,k g q,k,n, ∕N 0 , whereas Υ q,k,n, = Γ q,k,n, + 1 (see (8), (9), and (10)). Additionally, the achievable rate constraint in (14e) can be transformed to ℝ q,k,n, (a q,k, ) ≥ r T , which is expressed as…”

Section: Max-min Energy-efficient Power Allocationmentioning

confidence: 99%

See 1 more Smart Citation

Joint relay selection and max‐min energy‐efficient power allocation in downlink multicell NOMA networks: A matching‐theoretic approach

Baidas

Bahbahani

El-Sharkawi

et al. 2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

In this paper, the problem of joint relay selection and max‐min energy‐efficient power allocation (J‐RS‐MMEE‐PA) in downlink multicell nonorthogonal multiple access (NOMA) networks is studied. In particular, the goal is to perform relay selection for each cellular user within each cell, so as to achieve max‐min energy efficiency while satisfying quality‐of‐service (QoS) constraints. However, the formulated J‐RS‐MMEE‐PA problem happens to be nonconvex (ie, computationally intensive). In turn, a solution procedure for max‐min energy‐efficient power allocation is devised to determine the energy efficiency of each user per potential relay while meeting the target minimum rate per user. After that, the relay selection problem is modeled as a student‐project allocation with preferences over projects matching problem. A polynomial‐time complexity stable matching (SM) algorithm is proposed, which takes into account the maximum number of users that can select a relay as well as the number of users that can be associated with a base station. However, the proposed SM algorithm can only guarantee an SM size that is at least half the size of the maximum “optimal” cardinality SM. Thus, the improved SM (I‐SM) algorithm with polynomial‐time complexity is devised, so as to guarantee an SM size that is at least two‐thirds of that of the optimal matching. Lastly, simulation results are presented to compare the proposed matching algorithms to the J‐RS‐MMEE‐PA scheme, where it has been shown that the I‐SM algorithm is superior to its SM counterpart and yields comparable energy efficiency per cellular user to the J‐RS‐MMEE‐PA scheme while satisfying QoS constraints.

show abstract

“…Assuming perfect SIC, the signal‐to‐interference‐plus‐noise ratio (SINR) at the n th ordered user

U_{n} \in {scriptU}_{q}

is determined as

γ_{q, k, n} = \frac{a_{q, k, n} {normalΩ}_{q, k, n}}{{normalΩ}_{q, k, n} ā_{q, k, n} + {normalΓ}_{q, k} \underset{U_{n} \in {scriptU}_{q}}{false\sum} {scriptI}_{q, k, n} a_{q, k, n} + {normalΥ}_{q, k, n}},

where

ā_{q, k, n} = \sum_{j = n + 1}^{false| {scriptU}_{q} false|} {scriptI}_{q, k, j} a_{q, k, j}

and

ā_{q, k, false| {scriptU}_{q} false|} = 0

. Moreover, Ω q , k , n is defined as

{normalΩ}_{q, k, n} = {normalΓ}_{q, k} {normalΓ}_{q, k, n},

where

{normalΓ}_{q, k} = \frac{P g_{q, k}}{N_{0}} and {normalΓ}_{q, k, n} = \frac{P_{q, k} g_{q, k, n}}{N_{0}} .

…”

Section: Network Modelmentioning

confidence: 99%

Section: Max-min Energy-efficient Power Allocationmentioning

confidence: 99%

Joint relay selection and max‐min energy‐efficient power allocation in downlink multicell NOMA networks: A matching‐theoretic approach

Baidas

Bahbahani

El-Sharkawi

et al. 2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…The Zhong and Zhang analyze the performance of a full‐duplex (FD) NOMA system with cooperative relaying. Recently, the performance of NOMA with cooperative relaying has been widely investigated . Kim and Lee study the capacity performance of relay based NOMA system.…”

Section: Introductionmentioning

confidence: 99%

“…6,[20][21][22][23][24][25][26] Kim and Lee 20 study the capacity performance of relay based NOMA system. A cooperative NOMA with simultaneous wireless information and power transfer has been proposed in the study of Liu et al 23 The performance of NOMA with partial relay selection (RS) has been analyzed in the studies of Lee et al 21,22 In the study of Ding et al, 6 a cooperative NOMA system has been proposed, where a user with stronger channel gain is employed as a relay for another user experiencing poorer channel conditions. In the study of Xu et al, 24 two optimal RS schemes known as two-stage weighted max-min (WMM) and max-weighted harmonic mean (MWHM) have been investigated for cooperative downlink NOMA networks with multiple relays.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Performance analysis of nonorthogonal multiple access‐based underlay cognitive relay network

Aswathi

Babu

2019

Int J Communication

View full text Add to dashboard Cite

Summary This paper considers cooperative non‐orthogonal multiple access (NOMA) scheme in an underlay cognitive radio (CR) network. A single‐cell downlink cooperative NOMA system has been considered for the secondary network, consisting of a base station (BS) and two secondary users, ie, a far user and a near user. The BS employs NOMA signaling to send messages for the two secondary users where the near user is enabled to act as a half‐duplex decode‐and‐forward (DF) relay for the far user. We derive exact expressions for the outage probability experienced by both the users and the outage probability of the secondary system assuming the links to experience independent, nonidentically distributed Rayleigh fading. Further, we analyze the ergodic rates of both the users and the ergodic sum rate of the secondary network. The maximum transmit power constraint of the secondary nodes and the tolerable interference power constraint at the primary receiver are considered for the analysis. Further, the interference caused by the primary transmitter (PT) on the secondary network is also considered for the analysis. The performance of the proposed CR NOMA network has been observed to be significantly better than a CR network that uses conventional orthogonal multiple access (OMA) scheme. The analytical results are validated by extensive simulation studies.

show abstract

Outage and ergodic sum‐rate performance of cooperative MIMO‐NOMA with imperfect CSI and SIC

Aldababsa

Kucur

2020

Int J Communication

View full text Add to dashboard Cite

Summary In this paper, we examine a half‐duplex cooperative multiple‐input multiple‐output non‐orthogonal multiple access system with imperfect channel state information (CSI) and successive interference cancelation. The base station (BS) and mobile users with multi‐antenna communicate by the assistance of a CSI based or fixed gain amplify‐and‐forward (AF) relay with a single antenna. The diversity schemes, transmit antenna selection, and maximal ratio combining are applied at the BS and mobile users, respectively. We study the system performance in terms of outage probability (OP) and ergodic sum‐rate. Accordingly, the exact OP expressions are first derived jointly for the CSI based and fixed gain AF relay cases in Nakagami‐m fading channels. Next, the corresponding lower and upper bound expressions of the OP are obtained. The high signal‐to‐noise ratio analyses are also carried out to demonstrate the error floor value resulted in the practical case and achievable diversity order and array gain in the ideal case. Moreover, the lower and upper bounds of the ergodic sum‐rate expressions are derived together for the CSI based and fixed gain AF relay cases. Finally, the Monte‐Carlo simulations are used to verify the correctness of the analytical results.

show abstract

Non‐orthogonal multiple access schemes with partial relay selection

Cited by 113 publications

References 25 publications

Joint relay selection and max‐min energy‐efficient power allocation in downlink multicell NOMA networks: A matching‐theoretic approach

Joint relay selection and max‐min energy‐efficient power allocation in downlink multicell NOMA networks: A matching‐theoretic approach

Performance analysis of nonorthogonal multiple access‐based underlay cognitive relay network

Outage and ergodic sum‐rate performance of cooperative MIMO‐NOMA with imperfect CSI and SIC

Contact Info

Product

Resources

About