A Low-Complexity Multiuser Coding Scheme With Near-Capacity Performance

IEEE Trans. Signal Process.

Chi

Yuen

et al. 2019

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.

Section: B Ber Performance With Optimized Ira Codesmentioning

confidence: 99%

Section: B Ber Performance With Optimized Ira Codesmentioning

confidence: 99%

Capacity-Achieving MIMO-NOMA: Iterative LMMSE Detection

IEEE Trans. Signal Process.

Chi

Yuen

et al. 2019

“…We acknowledge that the selection of such multiuser code may not be optimal, hbut since the focus of this paper is on the design of gaussian message passing detection, the multiuser code design is beyond the scope of this paper. For more information on the latter, the readers may refer to [21], [56] on the optimization of multiuser SCM (superposition coded modulation), multiuser LDPC or multiuser IRA codes. Fig.…”

Section: F Sa-gmp With Digital Modulationmentioning

confidence: 99%

Gaussian Message Passing for Overloaded Massive MIMO-NOMA

IEEE Trans. Wireless Commun.

Yuen

Guan

et al. 2019

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.

“…Fig. 3 shows the asymptotic EXIT process between the LMMSE detector and the MU-IRA decoder, where the EXIT function of the MU-IRA decoder is obtained by using the similar method in [17]. According to Algorithm 1, a priori variance v ℓ k and extrinsic variance v e,ℓ k of LMMSE detector are updated according to Eq.…”

Section: B Optimization Of Coding Schemementioning

confidence: 99%

“…To confirm the importance of matching between the LMMSE detector and the proposed code in the perspective of EXIT analysis, we present a rate-0.08 MAC-IRA code [16], [17] for comparison, whose parameters are λ(x) = 0.063021x + 0.228288x 2 + 0.111951x 9 + 0.226877x 29 + 0.369864x 49 , α = 1, and q = 5. As shown in Fig.…”

Section: Asymptotic Performance Analysismentioning

confidence: 99%

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

Chi

IEEE Trans. Wireless Commun.

Song

et al. 2018

Self Cite

MIMO-NOMA combines Multiple-Input Multiple-Output (MIMO) and Non-Orthogonal Multiple Access (NOMA), which can address heterogeneous challenges, such as massive connectivity, low latency, and high reliability. In this paper, a practical coded MIMO-NOMA system with capacityapproaching performance as well as low implementation complexity is proposed. Specifically, the employed receiver consists of a multi-user Linear Minimum Mean-Square Error (LMMSE) detector and a bank of single-user message-passing decoders, which decompose the overall signal recovery into distributed low-complexity calculations. An asymptotic extrinsic information transfer analysis is proposed to estimate the performance of iterative receiver, where practical channel codes that match with the LMMSE detector in the iterative decoding perspective are constructed. As a result, the proposed coded MIMO-NOMA system achieves asymptotic performances within 0.2 dB from the theoretical capacity. Simulation results validate the reliability and robustness of the proposed system in practical settings, including various system loads, iteration numbers, code lengths, and channel conditions.