Joint relay selection and energy‐efficient power allocation strategies in energy‐harvesting cooperative NOMA networks

Baidas, Mohammed W.; Alsusa, Emad; Hamdi, Khairi Ashour

doi:10.1002/ett.3593

Cited by 9 publications

(6 citation statements)

References 38 publications

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“…In the broadcasting phase, the BS broadcasts—via DL NOMA—the data symbol of each user in each cluster

𝒞_{m} \in 𝒞

, which is received at a relay

R_{k} \in ℛ

as 7

y_{m, k}^{D L} = h_{k, b s}^{D L} \sum_{U_{n} \in 𝒞_{m}} \sqrt{E_{n, m, k}^{D L}} x_{n}^{D L} + η_{k}^{D L},

where

η_{k}^{D L}

is the received AWGN sample.…”

Section: System Modelmentioning

confidence: 99%

“…Thus, the received signal at a user

U_{n} \in 𝒞_{m}

is expressed as 7

\begin{align} y_{n, m, k}^{D L} & = x_{m, k}^{D L} h_{n, m, k}^{D L} + η_{n, m, k}^{D L} \\ = \sqrt{E_{k}^{D L} G_{m, k}^{D L}} h_{n, m, k}^{D L} h_{k, b s}^{D L} \sum_{U_{n} \in 𝒞_{m}} \sqrt{E_{n, m, k}^{D L}} x_{n}^{D L} + \sqrt{E_{k}^{D L} G_{m, k}^{D L}} h_{n, m, k}^{D L} η_{k}^{D L} + η_{n, m, k}^{D L}, \end{align}

where

η_{n, m, k}^{D L}

is the received AWGN at user

U_{n} \in 𝒞_{m}

. According to the principle of NOMA, let the users in

𝒞_{m}

be ordered with respect to each relay

R_{k} \in ℛ

according to their channel gains, as

| h_{1,}

…”

Section: System Modelmentioning

confidence: 99%

“…By substituting (6) into (9), and after some manipulation, the SINR of the ordered user

U_{n} \in 𝒞_{m}

can be shown to be 7

\begin{align} γ_{n, m, k}^{D L} ((), {boldE}_{m, k}^{D L}) & = \frac{E_{n, m, k}^{D L} ξ_{n, m, k}^{D L} E_{k}^{D L} ξ_{k, b s}^{D L}}{E_{k}^{D L} ξ_{n, m, k}^{D L} ξ_{k, b s}^{D L} \sum_{U_{i} \in {\overset{𝒞}{true}}_{n, m, k}^{D L}} E_{i, m, k}^{D L} + ξ_{k, b s}^{D L} \sum_{U_{i} \in 𝒞_{m}} E_{i, m, k}^{D L} + E_{k}^{D L} ξ_{n, m, k}^{D L} + 1} ≜ \frac{{\overset{γ}{true}}_{n, m, k}^{D L} ((), {boldE}_{m, k}^{D L})}{{\underline{γ}}_{n, m, k}^{D L} ((), {boldE}_{m, k}^{D L})}, \end{align}

where

ξ_{...}

…”

Section: System Modelmentioning

confidence: 99%

“…Several works have focused on energy‐efficient relay communications in NOMA networks. For instance, in Reference 7, the problem of joint relay selection and energy‐efficient power allocation (J‐RS‐EE‐PA) in amplify‐and‐forward (AF) downlink (DL) EH‐NOMA networks is considered. Particularly, the authors study the maximization of global energy‐efficiency (GEE), minimum energy efficiency (MEE), product energy efficiency, and sum energy efficiency (SEE), and devise computationally efficient algorithms to solve the different EE power allocation strategies.…”

Section: Introductionmentioning

confidence: 99%

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Joint relay assignment and energy‐efficiency maximization in energy‐harvesting downlink/uplink clustered nonorthogonal multiple‐access networks

Baidas

2020

Trans Emerging Tel Tech

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In this article, the problem of joint relay assignment and energy-efficiency maximization (J-RA-EE-MAX) in energy-harvesting downlink (DL) and uplink (UL) clustered nonorthogonal multiple-access (NOMA) networks is considered. Specifically, the aim is to perform relay assignment to user clusters in the DL and UL directions, while simultaneously maximizing energy efficiency (EE) over each relay via multiobjective optimization, and satisfying users' quality-of-service (QoS) constraints. However, problem J-RA-EE-MAX happens to be nonconvex in each link direction, and thus is computationally prohibitive. Alternatively, a low-complexity solution procedure is devised to solve problem J-RA-EE-MAX in each link direction by: (i) optimally solving the energy-efficiency maximizing power allocation (EE-MAX-PA) for each (user cluster, relay) combination to construct the relays' preference profile, and (ii) performing relay assignment via Gale's top trading cycles (TTC) matching mechanism. In particular, the optimal solution of the EE-MAX-PA problem is obtained by transforming it into a concave maximization problem, while the TTC mechanism is executed in linear time-complexity so as to obtain a stable relay assignment. Simulation results are presented to validate the proposed solution procedure, which is shown to yield comparable user cluster sum-rate and relay EE to the J-RA-EE-MAX scheme, and superior to other schemes in both link directions, however, at lower computational complexity, while satisfying users' QoS constraints. Finally, this work sheds light on the importance of decoupling relay assignment in the DL/UL directions, which significantly improves user cluster sum-rate and relay EE in comparison to coupled relay assignment schemes, and thus serves the requirements of 5G networks and beyond. K E Y W O R D Senergy-efficiency, matching, nonorthogonal multiple-access, power allocation, relay assignment Trans Emerging Tel Tech. 2020;31:e3962.wileyonlinelibrary.com/journal/ett

show abstract

“…In the broadcasting phase, the BS broadcasts—via DL NOMA—the data symbol of each user in each cluster

𝒞_{m} \in 𝒞

, which is received at a relay

R_{k} \in ℛ

as 7

y_{m, k}^{D L} = h_{k, b s}^{D L} \sum_{U_{n} \in 𝒞_{m}} \sqrt{E_{n, m, k}^{D L}} x_{n}^{D L} + η_{k}^{D L},

where

η_{k}^{D L}

is the received AWGN sample.…”

Section: System Modelmentioning

confidence: 99%

“…Thus, the received signal at a user

U_{n} \in 𝒞_{m}

is expressed as 7

\begin{align} y_{n, m, k}^{D L} & = x_{m, k}^{D L} h_{n, m, k}^{D L} + η_{n, m, k}^{D L} \\ = \sqrt{E_{k}^{D L} G_{m, k}^{D L}} h_{n, m, k}^{D L} h_{k, b s}^{D L} \sum_{U_{n} \in 𝒞_{m}} \sqrt{E_{n, m, k}^{D L}} x_{n}^{D L} + \sqrt{E_{k}^{D L} G_{m, k}^{D L}} h_{n, m, k}^{D L} η_{k}^{D L} + η_{n, m, k}^{D L}, \end{align}

where

η_{n, m, k}^{D L}

is the received AWGN at user

U_{n} \in 𝒞_{m}

. According to the principle of NOMA, let the users in

𝒞_{m}

be ordered with respect to each relay

R_{k} \in ℛ

according to their channel gains, as

| h_{1,}

…”

Section: System Modelmentioning

confidence: 99%

“…By substituting (6) into (9), and after some manipulation, the SINR of the ordered user

U_{n} \in 𝒞_{m}

can be shown to be 7

\begin{align} γ_{n, m, k}^{D L} ((), {boldE}_{m, k}^{D L}) & = \frac{E_{n, m, k}^{D L} ξ_{n, m, k}^{D L} E_{k}^{D L} ξ_{k, b s}^{D L}}{E_{k}^{D L} ξ_{n, m, k}^{D L} ξ_{k, b s}^{D L} \sum_{U_{i} \in {\overset{𝒞}{true}}_{n, m, k}^{D L}} E_{i, m, k}^{D L} + ξ_{k, b s}^{D L} \sum_{U_{i} \in 𝒞_{m}} E_{i, m, k}^{D L} + E_{k}^{D L} ξ_{n, m, k}^{D L} + 1} ≜ \frac{{\overset{γ}{true}}_{n, m, k}^{D L} ((), {boldE}_{m, k}^{D L})}{{\underline{γ}}_{n, m, k}^{D L} ((), {boldE}_{m, k}^{D L})}, \end{align}

where

ξ_{...}

…”

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint relay assignment and energy‐efficiency maximization in energy‐harvesting downlink/uplink clustered nonorthogonal multiple‐access networks

Baidas

2020

Trans Emerging Tel Tech

Self Cite

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show abstract

“…The OP performance is investigated in [39] for cooperative NOMA network with CSI based AF relay, in which the hybrid diversity scheme maximum ratio transmission/RAS is employed in both hops with imperfect CSI. Moreover, the performance of the cooperative NOMA networks is also demonstrated with different relay selection schemes in [40–46]. Also, by applying opportunistic methods, which offer the best transmitter selection approaches based on either a near or a far‐away user, the OP of the NOMA network with unreliable wireless backhauls is examined in [47] considering imperfect CSI.…”

Section: Introductionmentioning

confidence: 99%

Majority based antenna selection schemes in downlink NOMA network with channel estimation errors and feedback delay

Aldababsa

Kucur

2020

IET Communications

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The antenna selection (AS) in non-orthogonal multiple access (NOMA) networks is still a challenging problem since finding optimal AS solution may not be available for all channel realizations and has quite computational complexity when it exists. For this reason, in this paper, we develop a new suboptimal solution, majority based transmit antenna selection (TAS-maj), with significant reduction in computational complexity. The TAS-maj basically selects the transmit antenna with the majority. It is more efficient when compared to previously proposed suboptimal AS algorithms, namely maxmax-max (A 𝟑 ) and max-min-max (AIA) because these schemes are merely interested in optimizing the performance of the strongest and weakest users, respectively at the price of worse performance for the remaining users. On the other hand, the TAS-maj scheme yields better performance for more than half of mobile users in the NOMA networks. In this paper, we consider a multiple-input multiple-output communication system, where all the nodes are equipped with multi-antenna. Besides the TAS-maj is employed at the base station, a maximal ratio combining (MRC) is also employed at each mobile user in order to achieve superior performance. The impact of the channel estimation errors (CEEs) and feedback delay (FD) on the performance of the TAS-maj/MRC scheme is studied in the NOMA network over Nakagami-m fading channels. The outage behavior of the network is investigated by deriving the exact outage probability (OP) expression in closed-form. In addition, the corresponding upper bound of the OP is obtained in the presence of the CEEs and FD. The OP expression in high signalto-noise ratio region is also provided to illustrate an error floor value in the presence of the CEEs and FD as well as diversity and array gains in the absence of the CEEs and FD. The analytical results in the presence and absence of the CEEs and FD are verified by the Monte Carlo simulations. The numerical results imply that the system performance is more sensitive to the CEE than FD and shows the superiority of the proposed TAS-maj/MRC scheme over both A 𝟑 /MRC and AIA/MRC schemes.

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Power‐domain non‐orthogonal multiple access based full‐duplex one‐way wireless relaying network

Özduran

Mahmood

Chergui

2021

Trans Emerging Tel Tech

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This study investigates power‐domain non‐orthogonal multiple access based wireless information exchange process. The investigation considers a dual‐hop non‐regenerative full‐duplex wireless one‐way relaying networks in the system model, where the source terminal transmits two different types of information and subtracts the interference signal at the destination by using successive interference cancellation technique. The outage probability, error probability, achievable rate, and ergodic rate of the considered system is analytically derived. In addition, optimum power allocation coefficients and relay terminal position are determined using the optimization techniques. Monte‐Carlo simulation results validate the analytical and asymptotic derivations. The derived analytical expressions are found closely in agreement with the system level numerical results.

show abstract

Joint relay selection and energy‐efficient power allocation strategies in energy‐harvesting cooperative NOMA networks

Cited by 9 publications

References 38 publications

Joint relay assignment and energy‐efficiency maximization in energy‐harvesting downlink/uplink clustered nonorthogonal multiple‐access networks

Joint relay assignment and energy‐efficiency maximization in energy‐harvesting downlink/uplink clustered nonorthogonal multiple‐access networks

Majority based antenna selection schemes in downlink NOMA network with channel estimation errors and feedback delay

Power‐domain non‐orthogonal multiple access based full‐duplex one‐way wireless relaying network

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