Joint Subcarrier Pairing and Power Allocation for Cooperative Nonorthogonal Multiple Access

Li, Xunan; Li, Chong

doi:10.1109/tvt.2017.2753241

Cited by 12 publications

(15 citation statements)

References 15 publications

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“…In addition, Fig. 6 shows the SE versus the number of inner iterations when η EE = 0, i.e., solving problem (20). We find that 4 iterations are needed for convergence.…”

Section: Simulation Resultsmentioning

confidence: 96%

“…For problem (20), we need to optimize three variables, i.e., {V, P, z}. One can observe from (6) that V and P are coupled, Solve problem (19) and obtain L(η EE ),…”

Section: Proposed Solutionmentioning

confidence: 99%

“…ε o is the initial point for approximating the accuracy, ζ(ζ ∈ (0, 1]) is the stopping criterion, and ξ is used for updating the accuracy [42]. We assume that L inner iterations are needed, and the computational complexity of solving (20) is O(L([6M + 1] 3.5 + log((6M)/ε o ζ)/ log(ξ))). Since the computational complexity of the outer iteration is O(log 2 (1/ )), the total computational complexity of our proposed algorithm is O(log 2 (1/ )L([6M + 1] 3.5 + log((6M)/ε o ζ)/ log(ξ))).…”

Section: Analysis Of Computational Complexitymentioning

confidence: 99%

“…In Section III, we proposed an alternatively iterative V and P algorithm for solving (20). To decrease the computational complexity, in this section, we develop a ZF-based algorithm.…”

Section: Zf-based Low Complexity Algorithmmentioning

confidence: 99%

“…Unlike the works focusing on the arXiv:1909.06218v1 [cs.IT] 13 Sep 2019 downlink EE [5], [14], [18], the optimization of uplink EE is more challenging since the power allocation at users and beam design at the BS must be taken into consideration jointly. Although joint optimization problems in the downlink exist in the literature, such as joint subcarrier and BS power allocation [19], [20] as well as joint user access and BS power allocation [21], [22]. However, these optimization problems are clearly different from our joint optimization problem of the detection matrix at the BS and transmit power at the users.…”

Section: Introductionmentioning

confidence: 98%

See 4 more Smart Citations

Codebook-Based Max–Min Energy-Efficient Resource Allocation for Uplink mmWave MIMO-NOMA Systems

Hao

Zeng

Sun

et al. 2019

IEEE Trans. Commun.

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In this paper, we investigate the energy-efficient resource allocation problem in an uplink non-orthogonal multiple access (NOMA) millimeter wave system, where the fullyconnected-based sparse radio frequency chain antenna structure is applied at the base station (BS). To relieve the pilot overhead for channel estimation, we propose a codebook-based analog beam design scheme, which only requires to obtain the equivalent channel gain. On this basis, users belonging to the same analog beam are served via NOMA. Meanwhile, an advanced NOMA decoding scheme is proposed by exploiting the global information available at the BS. Under predefined minimum rate and maximum transmit power constraints for each user, we formulate a max-min user energy efficiency (EE) optimization problem by jointly optimizing the detection matrix at the BS and transmit power at the users. We first transform the original fractional objective function into a subtractive one. Then, we propose a two-loop iterative algorithm to solve the reformulated problem. Specifically, the inner loop updates the detection matrix and transmit power iteratively, while the outer loop adopts the bisection method. Meanwhile, to decrease the complexity of the inner loop, we propose a zero-forcing (ZF)-based iterative algorithm, where the detection matrix is designed via the ZF technique. Finally, simulation results show that the proposed schemes obtain a better performance in terms of spectral efficiency and EE than the conventional schemes.

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“…In addition, Fig. 6 shows the SE versus the number of inner iterations when η EE = 0, i.e., solving problem (20). We find that 4 iterations are needed for convergence.…”

Section: Simulation Resultsmentioning

confidence: 96%

“…For problem (20), we need to optimize three variables, i.e., {V, P, z}. One can observe from (6) that V and P are coupled, Solve problem (19) and obtain L(η EE ),…”

Section: Proposed Solutionmentioning

confidence: 99%

Section: Analysis Of Computational Complexitymentioning

confidence: 99%

“…In Section III, we proposed an alternatively iterative V and P algorithm for solving (20). To decrease the computational complexity, in this section, we develop a ZF-based algorithm.…”

Section: Zf-based Low Complexity Algorithmmentioning

confidence: 99%