A New Method For Increasing the Accuracy of EM-based Channel Estimation

It was recently shown that the detection performance can be significantly improved if the statistics of channel estimation errors are available and properly used at the receiver. Although in pilot-only channel estimation it is usually straightforward to characterize the statistics of channel estimation errors, this is not the case for the class of data-aided (semi-blind) channel estimation techniques. In this paper, we focus on the widely-used data-aided channel estimation techniques based on the expectation-maximization (EM) algorithm. This is achieved by a modified formulation of the EM algorithm which provides the receiver with the statistics of the estimation errors and properly using this additional information. Simulation results show that the proposed data-aided estimator outperform its classical counterparts in terms of accuracy, without requiring additional complexity at the receiver

A Hybrid BP-EP-VMP Approach to Joint Channel Estimation and Decoding for FTN Signaling over Frequency Selective Fading Channels

This paper deals with low-complexity joint channel estimation and decoding for faster-than-Nyquist (FTN) signaling over frequency selective fading channels. The inter-symbol interference (ISI) imposed by FTN signaling and the frequency selective channel are intentionally separated to fully exploit the known structure of the FTN-induced ISI. Colored noise due to the faster sampling rate than that of the Nyquist signaling system is approximated by autoregressive process. A Forney style factor graph representation of the FTN system is developed and Gaussian message passing is performed on the graph. Expectation propagation (EP) is employed to approximate the message from channel decoder to Gaussian distribution. Since the inner product between FTN symbols and channel coefficients is infeasible by belief propagation (BP), we propose to perform variational message passing (VMP) on an equivalent soft node in factor graph to tackle this problem. Simulation results demonstrate that the proposed low-complexity hybrid BP-EP-VMP algorithm outperforms the existing methods in FTN system. Compared with the Nyquist counterpart, FTN signaling with the proposed algorithm is able to increase the transmission rate by over 40%, with only negligible BER performance loss

Channel Acquisition for HF Skywave Massive MIMO-OFDM Communications

In this paper, we investigate channel acquisition for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first introduce the concept of triple beams (TBs) in the space-frequency-time (SFT) domain and establish a TB based channel model using sampled triple steering vectors. With the established channel model, we then investigate the optimal channel estimation and pilot design for pilot segments. Specifically, we find the conditions that allow pilot reuse among multiple user terminals (UTs), which significantly reduces pilot overhead. Moreover, we propose a channel prediction method for data segments based on the estimated TB domain channel. To reduce the complexity, we are able to formulate the channel estimation as a sparse signal recovery problem due to the channel sparsity in the TB domain and then obtain the channel by the proposed constrained Bethe free energy minimization (CBFEM) based channel estimation algorithm, which can be implemented with low complexity by exploiting the structure of the TB matrix together with the chirp z-transform (CZT). Simulation results demonstrate the superior performance of the proposed channel acquisition approach.Comment: 30 pages, 4 figure

Channel Estimation, Carrier Recovery, and Data Detection in the Presence of Phase Noise in OFDM Relay Systems

Due to its time-varying nature, oscillator phase noise can significantly degrade the performance of channel estimation, carrier recovery, and data detection blocks in high-speed wireless communication systems. In this paper, we analyze joint channel, carrier frequency offset (CFO), and phase noise estimation plus data detection in orthogonal frequency division multiplexing (OFDM) relay systems. To achieve this goal, a detailed transmission framework involving both training and data symbols is presented. In the data transmission phase, a combtype OFDM symbol consisting of both pilots and data symbols is proposed to track phase noise over an OFDM frame. Next, a novel algorithm that applies the training symbols to jointly estimate the channel responses, CFO, and phase noise based on the maximum a posteriori criterion is proposed. Additionally, a new hybrid Cramér-Rao lower bound for evaluating the performance of channel estimation and carrier recovery algorithms in OFDM relay networks is derived. Finally, an iterative receiver for joint phase noise estimation and data detection at the destination node is derived. Extensive simulations demonstrate that the application of the proposed estimation and receiver blocks significantly improves the performance of OFDM relay networks in the presence of phase noise