Search

Decentralized baseband processing (DBP) architecture, which partitions the base station antennas into multiple antenna clusters, has been recently proposed to alleviate the excessively high interconnect bandwidth, chip input/output data rates, and detection complexity for massive multi-user multiple-input multiple-output (MU-MIMO) systems. In this paper, we propose a novel decentralized Gaussian message passing (GMP) detection for the DBP architecture. Based on the message passing rule, each antenna cluster iteratively calculated the local means and variances which are fused to generate the global symbol beliefs for demodulation. The state evolution framework of the decentralized GMP algorithm is presented under the assumptions of large-system limit and Gaussian sources. Analytical results corroborated by simulations demonstrate that the nonuniform antenna cluster partition scheme exhibits a higher convergence rate than the uniform counterpart. Simulation results illustrate that the proposed decentralized GMP detection outperforms the recently proposed decentralized algorithms. [ABSTRACT FROM AUTHOR]

In this work, we introduce the pattern-domain pilot design paradigm based on a “superposition of orthogonal-building-blocks” with significantly larger contention space to enhance the massive machine-type communications (mMTC) random access (RA) performance in massive multiple-input multiple-output (MIMO) systems. Specifically, the pattern-domain pilot is constructed based on the superposition of $L$ cyclically-shifted Zadoff-Chu (ZC) sequences. The pattern-domain pilots exhibit zero correlation values between non-colliding patterns from the same root and low correlation values between patterns from different roots. The increased contention space, i.e., from N to $\binom{N}{L}$ , where $\binom{N}{L}$ denotes the number of all L-combinations of a set N, and low correlation values lead to a significantly lower pilot collision probability without compromising excessively on channel estimation performance for mMTC RA in massive MIMO systems. We present the framework and analysis of the RA success probability of the pattern-domain based scheme with massive MIMO systems. Numerical results demonstrate that the proposed pattern division random access (PDRA) scheme achieves an appreciable performance gain over the conventional one, while preserving the existing physical layer virtually unchanged. The extension of the “superposition of orthogonal-building-blocks” scheme to “superposition of quasi-orthogonal-building-blocks” is straightforward. [ABSTRACT FROM AUTHOR]

We introduce a joint weighted Neumann series (WNS) and Gauss–Seidel (GS) approach to implement an approximated linear minimum mean-squared error (LMMSE) detector for uplink massive multiple-input multiple-output (M-MIMO) systems. We first propose to initialize the GS iteration by a WNS method, which produces a closer-to-LMMSE initial solution than the conventional zero vector and diagonal-matrix based scheme. Then the GS algorithm is applied to implement an approximated LMMSE detection iteratively. Furthermore, based on the WNS, we devise a low-complexity approximate log-likelihood ratios (LLRs) computation method whose performance loss is negligible compared with the exact method. Numerical results illustrate that the proposed joint WNS-GS approach outperforms the conventional method and achieves near-LMMSE performance with significantly lower computational complexity. [ABSTRACT FROM AUTHOR]

Practical data detectors must achieve low error-rate at low-complexity for massive multiple-input multiple-output (MIMO) wireless systems. Since near-optimal minimum mean square error (MMSE) detection exhibits high computational complexity for such wireless systems, various iterative methods as well as their enhanced variants have been proposed. The efficiency of iterative approaches, however, is not satisfying due to slow convergence rate and reduced accuracy. This paper proposes a novel semi-iterative data detection algorithm for massive MIMO systems that simultaneously achieves a large improvement in convergence and accuracy with respect to the corresponding iterative method. We provide a general three-term recurrence expression for semi-iterative detection and incorporate it with Richardson method as a show case. To further improve the convergence rate of the proposed technique, a new trace-diagonal based low-complexity initial method is developed. A sufficient condition for convergence of the proposed method is also provided. We analyze the associated complexity of semi-iterative detector, and provide numerical results which demonstrate that the proposed detector is superior to recently introduced iterative detectors in terms of both the error-rate performance and computational complexity. [ABSTRACT FROM AUTHOR]

The recent concept of beamspace multiple input multiple output (MIMO) with discrete lens array can utilize beam selection to reduce the number of radio-frequency chains (RF) required in terahertz (THz) massive MIMO systems. However, to achieve the capacity-approaching performance, beam selection requires information on a beamspace channel of large size. This is difficult to obtain since the user mobility usually leads to the fast variation of THz beamspace channels, and the conventional real-time channel estimation schemes involve unaffordable pilot overhead. To solve this problem, in this paper, we propose the a priori aided (PA) channel tracking scheme. Specifically, by considering a practical user motion model, we first excavate a temporal variation law of the physical direction between the base station and each mobile user. Then, based on this law and the special sparse structure of THz beamspace channels, we propose to utilize the obtained beamspace channels in the previous time slots to predict the prior information of the beamspace channel in the following time slot without channel estimation. Finally, aided by the obtained prior information, the time-varying beamspace channels can be tracked with low pilot overhead. Simulation results verify that to achieve the same accuracy, the proposed PA channel tracking scheme requires much lower pilot overhead and signal-to-noise ratio (SNR) than the conventional schemes. [ABSTRACT FROM AUTHOR]

We propose a statistical information-based approach to enhance the performance of the noniterative quasi-maximum-likelihood (QML) receiver (detector plus decoder) recorded at the output of the channel decoder for coded multiple-input–multiple-output (MIMO) systems. As an illustrative example, the QR decomposition with an $M$-algorithm (QRD-M)-based noniterative QML receiver is employed in this paper. We first illustrate that there is a large discrepancy between the amplitude of the raw log-likelihood ratio (LLR) generated by the complexity-constrained QRD-M detector and its reliability, particularly for LLRs with high amplitudes. The discrepancy is shown to be the main underlying cause of degraded performance in coded MIMO systems. Then, we propose a reliability-aware transformation technique, which exploits the statistical relationship between the raw LLR's amplitude and its reliability to adjust the raw LLR's amplitude to a level commensurate with its reliability. By using the proposed statistical knowledge-based technique, the performance of the receiver (detector plus decoder) recorded at the output of the channel decoder is significantly enhanced for high-order modulations. The extension of the proposed method to other QML receivers such as the list sequential (LISS) detector and list sphere detector (LSD)-based receivers is straightforward. [ABSTRACT FROM AUTHOR]

An iterative detection and decoding (IDD) technique has recently received much attention due to its capacity approaching performance and manageable complexity for (parallel) turbo-coded systems. The existing IDD receivers process indiscriminately the a priori information of the systematic bits and the parity bits generated by the turbo decoder in the front-end detection. In this paper, we first show that the parity bits of the parallel turbo codes are inherently less protected than the systematic bits, which leads to suboptimality for the IDD receivers. Based on this observation, we then propose an unequal-error-correcting-capability-aware technique to mitigate the associated performance loss. As an illustrative example, the minimum-mean-square-error (MMSE) filtering and soft interference cancelation (MMSE-SIC)-aided iterative receiver for parallel-turbo-coded multiple-input–multiple-output (MIMO) systems is employed in this paper. Numerical results manifest that the MMSE-SIC-aided iterative receiver based on the proposed paradigm achieves noticeable performance gains over the conventional unequal-error-correcting-capability-agnostic one even with reduced computational complexity. The extension of the proposed principle to other iterative algorithms for parallel-turbo-coded data transmission is straightforward. [ABSTRACT FROM PUBLISHER]

This paper proposes a rotation and scaling based transformation method to simplify the (quasi-)maximum-likelihood (ML) detectors (such as QRD-M, sphere decoding (SD), etc.) for the square quadrature amplitude modulation (S-QAM) modulated multiple-input and multiple-output (MIMO) systems with no performance degradation. The key idea of the simplification is to utilize the symmetric shape of the S-QAM constellation so that after rotation and scaling, real multiplication and addition with certain constellation points in symbol replica generation simply transform into bit shifting or even vanish. As an illustrative example, the QRD-M detector is employed in this work. By using the proposed method, the number of real multiplications of the symbol replica generation required for the QRD-M detector is reduced by 50% and 25% for 4-QAM and 16-QAM, respectively. The numbers of real addition reduction is twice the amount of multiplication. The underlying principle of the proposed rotation and scaling transformation can be straightforwardly extended to amplitude and phase-shift keying (APSK) modulated MIMO systems employing the QRD-M, SD, list sequential sphere decoder (LISS), list sphere decoding (LSD), and the full ML detectors. [ABSTRACT FROM AUTHOR]

A companding-based transformation method is introduced to quasi-maximum-likelihood (ML) detectors, such as the QR-decomposition-based M-algorithm (QRD-M) and list sphere decoding, for coded multiple-input–multiple-output (MIMO) systems in this paper. The key idea of the proposed companding technique is to compress (i.e., down-weight) the dubious observation of the accumulated branch metric by taking into account its statistical characteristics so that, after companding, the estimation error of the unreliable detected information bits due to insufficient candidate size and/or channel estimation error is significantly mitigated without disproportionate compromise of the reliable information bits. By employing the proposed companding method, the original leptokurtically distributed log-likelihood ratio of the detected information bits becomes more Gaussian distributed. As an illustrative example, the QRD-M detector is employed in this paper. Numerical results show that the QRD-M detector based on the proposed companding paradigm achieves significant performance gain over the conventional method and approaches the performance of the ML detector for a 16-ary quadrature-amplitude-modulated (16-QAM) \4 \times \4 MIMO multiplexing system with lower-than-linear-detector computational complexity. [ABSTRACT FROM PUBLISHER]