One-bit quantization can significantly reduce the massive multiple-input and multiple-output (MIMO) system hardware complexity, but at the same time it also brings great challenges to the system algorithm design. Specifically, it is difficult to recover information from the highly distorted samples as well as to obtain accurate channel estimation without increasing the number of pilots. In this paper, a novel inference algorithm called variational approximate message passing (VAMP) for one-bit quantized massive MIMO receiver is developed, which attempts to exploit the advantages of both the variational Bayesian inference algorithm and the bilinear generalized approximated message passing algorithm to accomplish joint channel estimation and data detection in a closed form with first-order complexity. Asymptotic state evolution analysis indicates the fast convergence rate of VAMP and also provides a lower bound for the data detection error. Moreover, through extensive simulations, we show that VAMP can achieve excellent detection performance with low pilot overhead in a wide range of scenarios.

One-Bit Quantized Massive MIMO Detection Based on Variational Approximate Message Passing

In this work, the problem of receiver design for phase noise estimation and data detection in the presence of oscillator phase noise in a point-to-point multiple-input multiple-output (MIMO) system is addressed. First, we discuss some interesting and challenging aspects in receiver design for MIMO systems in the presence of Wiener phase noise. Then, using the variational Bayesian (VB) framework, a joint iterative phase noise estimator and symbol detector are developed based on inverse Gibbs or variational free energy maximization. Further, the symbol error probability (SEP) of the newly proposed iterative scheme is compared with the optimal maximum likelihood (ML) detector with perfect phase information for 16-phase shift keying (PSK) and 16-quadrature amplitude modulation (QAM) schemes.

http://publications.lib.chalmers.se/records/fulltext/153458/local_153458.pdf

Variational Bayesian framework for receiver design in the presence of phase noise in MIMO systems

https://ir.nctu.edu.tw:443/bitstream/11536/14656/1/000296114000008.pdf

Energy-efficient uplink resource allocation for IEEE 802.16j transparent-relay networks

A method and system for multiple input, multiple output (MIMO) detection and channel decoding comprising: decomposing a channel complex gain matrix into a unitary matrix and an upper right hand triangular matrix; providing a received signal to a complex conjugate transpose of the unitary matrix, thereby creating a plurality of signals; normalizing a last of the plurality of signals; channel decoding the normalized last of the plurality of signals, thereby recovering a last codeword signal; encoding the last codeword signal; utilizing the encoded last codeword signal to recover a second last codeword signal; and repeating the utilizing until all codeword signals are recovered Also, a method and system for providing an imbalanced modulation and coding scheme for successive interference cancellation

Hybrid-qrd-sic and imbalanced mcs system and method for mimo

This paper introduces a robust variational bayes (Robust-VB) receiver algorithm for joint signal detection, noise covariance matrix estimation and channel impulse response (CIR) tracking in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems over time varying channels. The variational bayes (VB) framework and turbo principle are combined to accomplish the parameter estimation and data detection. In the proposed Robust-VB receiver, a modified linear minimum mean-square-error interference cancellation (LMMSE-IC) soft detector is developed based on the VB theory, which adaptively sets the log-likelihood ratio (LLR) clipping value according to the reliability of detection on each subcarrier to mitigate the error propagation. Following the signal detection, an adaptive noise covariance matrix estimator is derived for the effective noise covariance estimation. Furthermore, in order to track time varying channels, a VB soft-input Kalman filter (VB-Soft-KF) is first derived. However, unreliable soft symbols introduce outliers, which degrade the performance of VB-Soft-KF. To tackle this problem, we propose a robust VB soft-input Kalman filter (VB-Robust-KF) based on the Huber M estimation theory. Finally, the performance of the proposed algorithm is assessed via simulations, showing the superior performance of the Robust-VB receiver compared to the other benchmark receiver algorithms.

Variational-Bayes-Assisted Joint Signal Detection, Noise Covariance Estimation, and Channel Tracking in MIMO-OFDM Systems

The emerging IEEE 802.16j mobile multi-hop relay (MMR) network is currently being developed to increase the user throughput and extend the service coverage as an enhancement of existing 802.16e standard. In 802.16j, the intermediate relay stations (RSs) help the base station (BS) communicate with those mobile stations (MSs) that are either too far away from the BS or placed in an area where direct communication with BS experiences unsatisfactory level of service. In this paper, we investigate the uplink Erlang capacity of a two-hop 802.16j relay system supporting both voice and data traffics with adaptive modulation and coding (AMC) scheme applied in the physical layer. We first develop analytical models to calculate the blocking probability in the access zone and the outage probability in the relay zone, respectively. Then a joint algorithm is proposed to determine the bandwidth distribution between the access zone and the relay zone, and to derive the Erlang capacity region of the system. The numerical examples show that some capacity gains can be obtained with a relay-enhanced 802.16j system compared to the conventional single-hop 802.16e system.

/pdf/uplink-capacity-of-multi-class-ieee-802-16j-relay-networks-4c0kna28gf.pdf

Uplink Capacity of Multi-Class IEEE 802.16j Relay Networks with Adaptive Modulation and Coding

The invention discloses a multi-strategy-combined OFDM (Orthogonal Frequency Division Multiplexing) frame synchronization method, which can be used for effectively resisting wrong synchronization caused by interferences of noise, carrier frequency offsets, same-frequency signals and the like and meets the requirement of frame synchronization of an OFDM system. The method comprises the following steps of: (1) performing symbol cross-correlation operation on a received signal sequence and a local training sequence, performing time delay auto-correlation operation on the received signal sequence, and performing energy operation on a time delay received signal sequence; (2) judging and detecting a symbol cross-correlation operation result and a preset cross-correlation judgment threshold T1, judging and detecting a delay auto-correlation operation result and a preset auto-correlation judgment threshold T2, and judging and detecting the delay auto-correlation operation result and a self-adaptive judgment threshold T3; (3) when frame arrival is detected, searching for a peak value of the delay auto-correlation operation result to obtain a timing position; and (4) adjusting the position of the received signal sequence according to the timing position to obtain a frame synchronization output signal.

Multi-strategy-combined OFDM (Orthogonal Frequency Division Multiplexing) frame synchronization method

The invention discloses an OFDM (Orthogonal Frequency Division Multiplexing) peak-to-average power ratio lowering method based on constellation linear expansion, which has the advantages of capability of greatly lowering a peak-to-average ratio, small influence on a system error rate, no need of transmitting sideband information, no need of changing a receiving end, low calculation amount and low hardware implementation cost. The method comprises the following steps of: (1) mapping binary data to be transmitted into a constellation point; (2) establishing a constellation expansion OFDM signal peak-to-average ratio lowering model; (3) training a constellation proportional factor ki; and (4) performing expansion changing on the constellation point by using the trained constellation proportional factor ki to obtain constellation mapping with a low peak-to-average ratio, i.e., multiplying a frequency domain complex signal of bearing information by the proportional factor for performing IFFT (Inverse Fast Fourier Transform) calculation to obtain an output time domain complex signal.

OFDM (Orthogonal Frequency Division Multiplexing) peak-to-average power ratio lowering method based on constellation linear expansion

Based on the Variational Bayes expectation-maximization (VBEM) algorithm, a low complexity semi-blind Bayesian iterative receiver with joint signal detection and channel tracking is proposed in this paper for MIMO-OFDM systems over time-varying multi-path channels. Since the VBEM algorithm provides distribution estimation of all parameters, the detection performance can be improved by taking the channel estimation error into account. In addition, with the aid of the soft information provided by the signal detector, the recursive VBEM (RVBEM) algorithm is introduced to track the time-varying channels. Due to the high complexity of the RVBEM algorithm, a novel time-frequency domain recursive VBEM (TF-LCRVBEM) algorithm with low complexity is further proposed. The TFLCRVBEM algorithm simply predicts the channel impulse responses (CIRs) on time domain and recursively refines them on all subcarriers. The complexity analysis results demonstrate that the TF-LCRVBEM algorithm totally avoids computation of matrix inversion and obtains linear complexity. Moreover, the simulation results show that the proposed receiver not only dramatically outperforms the conventional receiver, but also provides performance close to the optimal receiver with perfect channel state information (PCSI).

Low Complexity Semi-Blind Bayesian Iterative Receiver for MIMO-OFDM Systems

In this paper, a new joint symbol detection and channel tracking algorithm is proposed for the coded MIMO-OFDM systems over time-varying frequency-selective fading channels. The iterative detection/decoding and channel estimation are iteratively employed based on the Variational Bayes expectation-maximization (VBEM) algorithm to improve the system performance. A modified list sphere decoder (LSD) is derived to make the data detection feasible for large systems, which takes into account the statistical information about the channel uncertainty and provides soft symbols for channel estimation. With the autoregressive process channel model and the soft symbols calculated from detector, the time-varying channel impulse responses are tracked by the Kalman smoother. The VBEM iterations are embedded in the turbo-processing of the receiver for incorporation of the coding constraints. Simulation results demonstrate that the proposed algorithm has robust performance over time-varying channels.

Chunlin Xiong

Papers

Uplink Capacity of Multi-Class IEEE 802.16j Relay Networks with Adaptive Modulation and Coding

Multi-strategy-combined OFDM (Orthogonal Frequency Division Multiplexing) frame synchronization method

OFDM (Orthogonal Frequency Division Multiplexing) peak-to-average power ratio lowering method based on constellation linear expansion

Low Complexity Semi-Blind Bayesian Iterative Receiver for MIMO-OFDM Systems

Joint symbol detection and channel tracking for MIMO-OFDM systems via the variational bayes EM algorithm