Practical MIMO-NOMA: Low Complexity and Capacity-Approaching Solution

Chi, Yuhao; Liu, Lei; Song, Guanghui; Yuen, Chau; Guan, Yong Liang; Liu, Ying

doi:10.1109/twc.2018.2858222

Cited by 76 publications

(62 citation statements)

References 54 publications

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“…For example, the optimal Multiuser Detector (MUD) for the Multiuser MIMO (MU-MIMO), such as the Maximum A-posteriori Probability (MAP) detection or Maximum Likelihood (ML) detection, was proven to be NP-hard [26]- [28]. Low-complexity uplink detection for MIMO-NOMA is hence desirable [20]- [22]. In [21], a low-complexity iterative Linear Minimum Mean Square Error (LMMSE) detection was proposed to approach the sum capacity of the MIMO-NOMA system, but the complexity of LMMSE detection is still too high due to the need of performing large matrix inversion [29].…”

Section: A Motivation and Related Workmentioning

confidence: 99%

Gaussian Message Passing for Overloaded Massive MIMO-NOMA

Liu

Yuen

Guan

et al. 2019

IEEE Trans. Wireless Commun.

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This paper considers a low-complexity Gaussian Message Passing (GMP) scheme for a coded massive Multiple-Input Multiple-Output (MIMO) systems with Non-Orthogonal Multiple Access (massive MIMO-NOMA), in which a base station with N s antennas serves N u sources simultaneously in the same frequency. Both N u and N s are large numbers, and we consider the overloaded cases with N u > N s . The GMP for MIMO-NOMA is a message passing algorithm operating on a fully-connected loopy factor graph, which is well understood to fail to converge due to the correlation problem. In this paper, we utilize the largescale property of the system to simplify the convergence analysis of the GMP under the overloaded condition. First, we prove that the variances of the GMP definitely converge to the mean square error (MSE) of Linear Minimum Mean Square Error (LMMSE) multi-user detection. Secondly, the means of the traditional GMP will fail to converge when N u /N s < ( √ 2 − 1) −2 ≈ 5.83. Therefore, we propose and derive a new convergent GMP called scale-andadd GMP (SA-GMP), which always converges to the LMMSE multi-user detection performance for any N u /N s > 1, and show that it has a faster convergence speed than the traditional GMP with the same complexity. Finally, numerical results are provided to verify the validity and accuracy of the theoretical results presented.

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Section: A Motivation and Related Workmentioning

confidence: 99%

Gaussian Message Passing for Overloaded Massive MIMO-NOMA

Liu

Yuen

Guan

et al. 2019

IEEE Trans. Wireless Commun.

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“…Recently, Non-Orthogonal Multiple Access (NOMA), where all the users can be served con-currently in the same time/frequency/code domain, has been identified as one of the key radio access technologies to increase the spectral efficiency and reduce latency in 5G mobile networks [8]- [17]. As opposed to OMA, the key concepts behind NOMA are summarized as follows [16]- [20].…”

Section: Introductionmentioning

confidence: 99%

Capacity-Achieving MIMO-NOMA: Iterative LMMSE Detection

Liu

Chi

Yuen

et al. 2019

IEEE Trans. Signal Process.

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This paper considers a low-complexity iterative Linear Minimum Mean Square Error (LMMSE) multi-user detector for the Multiple-Input and Multiple-Output system with Non-Orthogonal Multiple Access (MIMO-NOMA), where multiple single-antenna users simultaneously communicate with a multiple-antenna base station (BS). While LMMSE being a linear detector has a low complexity, it has suboptimal performance in multi-user detection scenario due to the mismatch between LMMSE detection and multi-user decoding. Therefore, in this paper, we provide the matching conditions between the detector and decoders for MIMO-NOMA, which are then used to derive the achievable rate of the iterative detection. We prove that a matched iterative LMMSE detector can achieve (i) the optimal capacity of symmetric MIMO-NOMA with any number of users, (ii) the optimal sum capacity of asymmetric MIMO-NOMA with any number of users, (iii) all the maximal extreme points in the capacity region of asymmetric MIMO-NOMA with any number of users, (iv) all points in the capacity region of two-user and three-user asymmetric MIMO-NOMA systems. In addition, a kind of practical low-complexity error-correcting multiuser code, called irregular repeat-accumulate code, is designed to match the LMMSE detector. Numerical results shows that the bit error rate performance of the proposed iterative LMMSE detection outperforms the state-of-art methods and is within 0.8dB from the associated capacity limit.

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“…The Gaussian capacity of the MIMO system can be obtained by using iterative linear minimum mean square error (LMMSE) detection and applying the coding design of LMMSE method . In Liu et al, the constrained capacity of MIMO systems using modulation techniques such as phase‐shift keyings (PSK) and quadrature amplitude modulations (QAMs) can be obtained with approximate message passing (AMP) at the receiver.…”

Section: Introductionmentioning

confidence: 99%

Network‐coded MIMO‐NOMA systems with FEC codes in two‐way relay networks

Hlaing

Farzamnia

Mariappan

et al. 2020

Int J Communication

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Summary This paper assumes two users and a two‐way relay network with the combination of 2×2 multi‐input multi‐output (MIMO) and nonorthogonal multiple access (NOMA). To achieve network reliability without sacrificing network throughput, network‐coded MIMO‐NOMA schemes with convolutional, Reed‐Solomon (RS), and turbo codes are applied. Messages from two users at the relay node are network‐coded and combined in NOMA scheme. Interleaved differential encoding with redundancy (R‐RIDE) scheme is proposed together with MIMO‐NOMA system. Quadrature phase‐shift keying (QPSK) modulation technique is used. Bit error rate (BER) versus signal‐to‐noise ratio (SNR) (dB) and average mutual information (AMI) (bps/Hz) versus SNR (dB) in NOMA and MIMO‐NOMA schemes are evaluated and presented. From the simulated results, the combination of MIMO‐NOMA system with the proposed R‐RIDE‐Turbo network‐coded scheme in two‐way relay networks has better BER and higher AMI performance than conventional coded NOMA system. Furthermore, R‐RIDE‐Turbo scheme in MIMO‐NOMA system outperforms the other coded schemes in both MIMO‐NOMA and NOMA systems.

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Practical MIMO-NOMA: Low Complexity and Capacity-Approaching Solution

Cited by 76 publications

References 54 publications

Gaussian Message Passing for Overloaded Massive MIMO-NOMA

Gaussian Message Passing for Overloaded Massive MIMO-NOMA

Capacity-Achieving MIMO-NOMA: Iterative LMMSE Detection

Network‐coded MIMO‐NOMA systems with FEC codes in two‐way relay networks

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