An Improved Coalition Game Approach for MIMO-NOMA Clustering Integrating Beamforming and Power Allocation

Ding, Jiefei; Cai, Jun; Yi, Changyan

doi:10.1109/tvt.2018.2889694

Cited by 48 publications

(34 citation statements)

References 26 publications

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“…We note that the first term 1 M B total in (20) accounts for the uniform feedback allocation for all clusters. Thus, the second term in (20) indicates how much the optimal allocation must deviate from the uniform allocation, and is a function of M , M 2 , R req , and the maximum and the minimum of c 2α k . We note that as the number of 2-user clusters or M 2 increases, the feedback rate for 1-user clusters (B 1 ) must increase to combat larger interference from 2-user clusters.…”

Section: On Minimizing the Maximum Outage Probabilitymentioning

confidence: 99%

“…We note that as the number of 2-user clusters or M 2 increases, the feedback rate for 1-user clusters (B 1 ) must increase to combat larger interference from 2-user clusters. The maximum of the two factors in the last term in (20) comes from the comparison between the maximum outage probability of the stronger users in 2-user clusters and that of the weaker users. The detailed derivation for (20) and 21is in Appendix B.…”

Section: On Minimizing the Maximum Outage Probabilitymentioning

confidence: 99%

“…The maximum of the two factors in the last term in (20) comes from the comparison between the maximum outage probability of the stronger users in 2-user clusters and that of the weaker users. The detailed derivation for (20) and 21is in Appendix B. We remark that the approximation in Proposition 1 is valid when SNR is high.…”

Section: On Minimizing the Maximum Outage Probabilitymentioning

confidence: 99%

“…Proposition 2: Applying Lemma 3, we approximate the optimal feedback rates that minimize the maximum outage probability among active users in a cell with a partial load (M < N t ), large SNR, and high feedback rate. The expressions of the approximate feedback rates are the same as those for a cell with full load (M = N t ) in (20) and (21), and are applicable for R req in (19).…”

Section: Lemmamentioning

confidence: 99%

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On Optimizing Feedback-Rate Allocation for Downlink MIMO-NOMA With Quantized CSIT

Mamat

Santipach

2020

IEEE Open J. Commun. Soc.

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We consider multiple-input multiple output (MIMO) non-orthogonal multiple access (NOMA) downlink channels with zeroforcing beamforming transmission. The base station has multiple transmit antennas while all mobile devices have single receive antenna. With a limited feedback rate, channel direction information (CDI) needs to be quantized and fed back from mobile users to a base station. Thus, the accuracy of the quantized channel state information at the transmitter (CSIT) will depend on the feedback rate. To increase spectral efficiency, 2 active users in some clusters share the same beamforming vector and thus, will interfere fully with each other. Given a total-feedback rate, we analyze the feedback allocation for clusters with single active user and 2 active users. The objective is to either minimize the maximum outage probability or maximize the minimum rate among all active users in a cell. We show that the proposed feedback allocation is close to the optimum. Some numerical examples show that the resulting rate performance with the proposed feedback allocation is increased by 100% over that with the uniform feedback allocation. Index Terms-Non-orthogonal multiple access (NOMA), multiple-input multiple-output (MIMO), channel state information at transmitter (CSIT), random vector quantization (RVQ), zeroforcing, beamforming, feedback.

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Section: On Minimizing the Maximum Outage Probabilitymentioning

confidence: 99%

Section: On Minimizing the Maximum Outage Probabilitymentioning

confidence: 99%

Section: On Minimizing the Maximum Outage Probabilitymentioning

confidence: 99%

Section: Lemmamentioning

confidence: 99%

See 2 more Smart Citations

On Optimizing Feedback-Rate Allocation for Downlink MIMO-NOMA With Quantized CSIT

Mamat

Santipach

2020

IEEE Open J. Commun. Soc.

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“…In [29], the optimal and low complexity suboptimal power allocation schemes have been proposed to maximize the ergodic capacity of MIMO-NOMA system with statistical channel state information in the transmitter over Rayleigh fading channel. Also, the authors in [30] have proposed a cluster beamforming strategy which can optimize beamforming vectors and power allocation coefficients in MIMO-NOMA system to decrease the total power. In addition, NOMA can improve the achievable rate greatly in the D2D-aided cooperative relaying system in the literature [31].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis for Downlink MIMO-NOMA in Millimeter Wave Cellular Network with D2D Communications

Wang

et al. 2019

Wireless Communications and Mobile Computing

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Enabling nonorthogonal multiple access (NOMA) in device-to-device (D2D) communications under the millimeter wave (mmWave) multiple-input multiple-output (MIMO) cellular network is of critical importance for 5G wireless systems to support low latency, high reliability, and high throughput radio access. In this paper, the closed-form expressions for the outage probability and the ergodic capacity in downlink MIMO-NOMA mmWave cellular network with D2D communications are considered, which indicates that NOMA outperforms TDMA. The influencing factors of performance, such as transmission power and antenna number, are also analyzed. It is found that higher transmission power and more antennas in the base station can decrease the outage probability and enhance the ergodic capacity of NOMA.

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Nonorthogonal Multiple Access (NOMA): How It Meets 5G and Beyond

et al. 2019

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Due to massive connectivity and increasing demands of various services and data‐hungry applications, a full‐scale implementation of the fifth‐generation (5G) wireless systems requires more effective radio access techniques. In this regard, nonorthogonal multiple access (NOMA) has recently gained ever‐growing attention from both academia and industry. Compared to orthogonal multiple access (OMA) techniques, NOMA is superior in terms of spectral efficiency and is thus appropriate for 5G and beyond. In this article, we provide an overview of NOMA principles and applications. Specifically, the article discusses the fundamentals of power‐domain NOMA with single and multiple antennas in both uplink and downlink settings. In addition, the basic principles of code‐domain NOMA are elaborated. Further, the article explains various resource allocation techniques such as user pairing and power allocation for NOMA systems; discusses the basic form of cooperative NOMA and its variants; and addresses several opportunities and challenges associated with the compatibility of NOMA with other advanced communication paradigms such as heterogeneous networks and millimeter wave communications.

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An Improved Coalition Game Approach for MIMO-NOMA Clustering Integrating Beamforming and Power Allocation

Cited by 48 publications

References 26 publications

On Optimizing Feedback-Rate Allocation for Downlink MIMO-NOMA With Quantized CSIT

On Optimizing Feedback-Rate Allocation for Downlink MIMO-NOMA With Quantized CSIT

Performance Analysis for Downlink MIMO-NOMA in Millimeter Wave Cellular Network with D2D Communications

Nonorthogonal Multiple Access (NOMA): How It Meets 5G and Beyond

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