Optimal Training Design for Channel Estimation in MIMO Single/Multi Carrier Block Transmission Systems

This paper develops a novel data dependent superimposed training technique for channel estimation in generic block transmission (BT) systems comprising of single/multi-carrier (SC/MC) and zero-padded (ZP)/ cyclic prefix (CP) systems. The training sequence comprises of the summation of a known training sequence and a data-dependent sequence that is not known to the receiver. A unique aspect of the scheme is that the channel estimation is not affected by the use of a data-dependent sequence. The pilot design framework is conceived in order to minimize the Bayesian Cramér-Rao bound (BCRB) associated with channel estimation error. Simulation results are provided to exhibit the performance of the proposed scheme for single and multi carrier zero-padded and cyclic prefixed systems.

This paper considers pilot-based channel estimation in large-scale multiple-input multiple-output (MIMO) communication systems, also known as “massive MIMO”. Unlike previous works on this topic, which mainly considered the impact of inter-cell disturbance due to pilot reuse (so-called pilot contamination), we are concerned with the computational complexity. The conventional minimum mean square error (MMSE) and minimum variance unbiased (MVU) channel estimators rely on inverting covariance matrices, which has cubic complexity in the multiplication of number of antennas at each side. Since this is extremely expensive when there are hundreds of antennas, we propose to approximate the inversion by an L-order matrix polynomial. A set of low-complexity Bayesian channel estimators, coined Polynomial ExpAnsion CHannel (PEACH) estimators, are introduced. The coefficients of the polynomials are optimized to yield small mean square error (MSE). We show numerically that near-optimal performance is achieved with low polynomial orders. In practice, the order L can be selected to balance between complexity and MSE. Interestingly, pilot contamination is beneficial to the PEACH estimators in the sense that smaller L can be used to achieve near-optimal MSEs.

Multiple-input multiple-output (MIMO) signal processing combined with orthogonal frequency division multiplexing (OFDM) is a widely accepted solution to achieve the high bit rates that new communication standards require in frequency-selective wireless channels. In recent years, a new frequency-domain equalized (FDE) cyclic-prefixed single-carrier (CPSC) block transmission system has been proven to achieve similar performance to OFDM for coded systems with the same overall complexity, avoiding its main drawbacks and becoming a candidate for future wireless standards. A FDE CPSC spatial multiplexing system is analyzed and compared to OFDM. The implementation of different frequency-domain MIMO equalization and detection schemes is evaluated. Simulation-based performance results prove the efficiency of FDE CPSC based spatial multiplexing systems and highlight that these transmission schemes can outperform OFDM in uncoded and high coding rate MIMO systems, as it has been shown in the literature for the SISO case.

Cyclic prefixed single carrier (CP-SC) transmission over frequency selective channels is considered. In conventional CP-SC systems, the cyclic prefix is discarded at the receiver side. This suboptimal strategy allows simple frequency domain equalization. In this paper, a new CP-SC receiver exploiting the whole received signal (including the cyclic prefix) for data estimation is proposed. With reasonable additional complexity, the proposed architecture outperforms the conventional CP-SC receiver, in terms of bit error rate. Simulations results show that the proposed scheme also improves the goodput, i.e. error-free rate, of the communication link

Multiple Input Multiple Output (MIMO) system with orthogonal frequency division multiplexing (OFDM) is a promising physical layer candidate for the current and future wireless standards. MIMO increases system throughput while OFDM maximizes spectrum efficiency. To reduce the impacts of frequency selective fading and increase the capacity, MIMO requires precise channel state information (CSI). The presence of carrier frequency offset (CFO) introduces intercarrier interference (ICI) among the subcarriers which degrades the system performance. MIMO channel estimation is done under the assumption of perfect frequency synchronisation between the transmitter and receiver, or zero CFO. However, such an assumption is incorrect for time-varying and frequency-selective wireless channels, and the presence of CFO has an impact on channel estimation accuracy. In the existing methods, CFO and channel estimations are carried out independently using separate training sequences which reduces the spectral efficiency. As a result, the pilot aided interpolation approach is proposed in this paper for the joint estimate and compensation of CFO and channel effects using the same set of pilot tones. Theresults show that the proposed method offers precise CFO and channel estimation which improves the bit error rate performance and carrier to interference power ratio (CIR) of MIMO-OFDM system.

This paper reports on investigations into the effects of spatial correlation on channel estimation and capacity of a multiple input multiple output (MIMO) system. Minimum mean square error (MMSE) method is applied for channel estimation under a correlated channel scenario. Simulation results for a 4 times 4 MIMO system with the Jakes model describing a channel between a base station and a mobile station demonstrate a trade-off between the effects of spatial correlation, channel estimation and capacity. For low values of signal to noise ratio (SNR), spatial correlation decreases channel estimation errors and improves capacity. For high values of SNR, spatial correlation has a less pronounced effect on improving channel estimation and thus the MIMO capacity is adversely affected by the decreased channel matrix rank.

After a few decades’ evolution of wireless communication systems, to ensure reliable highspeed communication over unreliable wireless channels is still one of the major challenges facing researchers and engineers. The use of multiple antennas at transmitter and receiver, known as multiple-input multiple-output (MIMO) communications, is one promising technology delivering desired wireless services. The main goal of this thesis is to study two important issues in wireless MIMO communication systems: receiver design for coded MIMO systems, and diversity-multiplexing tradeoff analysis in general fading channels. In the first part of this thesis, we decompose the receiver design problem into two subproblems: MIMO channel estimation and MIMO detection. For the MIMO channel estimation, we develop an expectation-maximization (EM) based semi-blind channel and noise covariance matrix estimation algorithm for space-time coding systems under spatially correlated noise. By incorporating the proposed channel estimator into the iterative receiver structure, both the channel estimation and the error-control decoding are improved significantly. We also derive the modified Cramer-Rao bounds (MCRB) for the unknown parameters as the channel estimation performance metric, and demonstrate that the proposed channel estimation algorithm can achieve the MCRB after several iterations. For the MIMO detection, we propose a novel lowcomplexity MIMO detection algorithm, which has only cubic order computational complexity, but with near-optimal performance. For a 4×4 turbo-coded system, we show that the proposed detector had the same performance as the maximum a posteriori (MAP) detector for BPSK modulation, and 0.1 dB advantage over the approximated MAP detector (list sphere decoding algorithm) for 16-QAM modulation at BER = 10−4. In the second part of this thesis, we derive the optimal diversity-multiplexing tradeoff for

This paper presents a novel iterative detection and channel estimation scheme that combines the effort of superimposed training (ST) and pilot-aided training (PAT) for multiple-input multiple-output (MIMO) flat fading channels. The proposed method, hereafter known as joint mean removal ST and PAT (MRST-PAT), implements an iterative detection and channel estimation that achieves the performance of data-dependent ST (DDST) algorithm, with the difference that the data arithmetic cyclic mean is estimated and removed from data at the receiver’s end. It is demonstrated that this iterative and cooperative detection and channel estimator algorithm surpasses the effects of data detection identifiability condition that DDST has shown when higher orders of modulation are used. Theoretical performance of the MRST-PAT scheme is provided and corroborated by numerical simulations. In addition, the performance comparison between the proposed method and different MIMO channel estimation techniques is analyzed. The joint effort between ST and PAT shows that MRST-PAT is a solid candidate in communications systems for multiamplitude constellations in Rayleigh fading channels, while achieving high-throughput data rates with manageable complexity and bit-error rate (BER) as a figure of merit.

In this thesis we consider the performance degradation of three telecommunication transceivers with respect to parameters uncertainties. The major contribution of our analysis is the derivation of simple and informative approximations to the excess mean square error, which uncover the basic factors that determine the performance of each transceiver under the pres- ence of parameter uncertainties. In Chapter 1, we brie°y present the channel models and the transceiver structures used in this thesis. In Chapters 2 and 3, we consider the case of the Multiple-Input Multiple-Output (MIMO) °at fading channel and two widely known transceiver schemes, i.e., the trans- mit MIMO Wiener ?ltering and the Tomlinson-Harashima (TH) precoding. For the case of the transmit MIMO Wiener ?ltering, degradation is due to channel estimation errors and time-variations, and noise second-order statistics estimation errors. For the case of the TH precoder, degradation is due to channel estimation errors and time-variations. For both cases, our ?nal expressions uncover the factors that determine the performance degradation in practice, and the relative importance between di®erent error sources. In Chapter 4, we consider the case of the Single-Input Single-Output (SISO) frequency selective channel and the minimum mean square error (MMSE) linear equalizer. The error sources that cause the system degradation is the channel and CFO estimation errors. Our aim is to uncover the relative importance of these error sources. It turns out that the CFO estimation error is much more important than the channel estimation error. This fact leads to useful conclusions concerning the optimal training sequence design for joint CFO and channel estimation.

This paper describes investigations into the antenna mutual coupling (MC) effect on channel estimation and capacity of a multiple-input multiple-output (MIMO) wireless communication system. The presented investigations close the gap existing in the previous works which assessed the effect of mutual coupling on MIMO capacity under the assumption of availability of perfect channel state information (CSI) at the receiver. The new approach assumes that the perfect CSI is not available due to channel estimation errors. The investigations are carried out for different spacing between array antenna elements producing a varying effect of mutual coupling on the channel estimation and the resulting MIMO channel capacity.

Precoding is a technique which exploits transmission diversity by weighting the information stream, i.e. the transmitter sends the coded information to the receiver to achieve pre-knowledge of the channel. Visible light communication (VLC) channels inherently exhibit frequency-selective and multi-path fading effects that result in intersymbol interference (ISI) and thus reduces the data rate. To this end, optical multiple input multiple output (MIMO) can provide spatial diversity and thus achieve a high data rate. Inspired by these facts, we investigate different precoding and equalization schemes for $2 \times 2$ MIMO for the vehicle to vehicle (V2V-VLC) system. In this work, we consider three detection schemes, Zero Forcing (ZF), Maximum likelihood (ML), and Minimum Mean Squared Error (MMSE). Also, we consider flickering/dimming control and the nature of outdoor VLC channel as a frequency selective channel. Moreover, we investigate a transceiver considering perfect CSI based on different detection algorithms. Simulation results of the investigated transceiver demonstrate improvements in Bit Error Rate (BER) and Mean Squared Error (MSE) for the MMSE scheme.

Block transmission has recently been considered as an alternative to the conventional continuous transmission technique. In particular, block transmission techniques with zero padding (ZP) and cyclic prefix (CP) are becoming attractive procedures for their ability to eliminate both inter-symbol interference (ISI) and inter-block interference (IBI). In this paper, we present a unified approach to blind channel estimation and interference suppression for block transmission using ZP or CP in both single-carrier (SC) and multicarrier (MC) systems. Our approach uses a generalized multichannel minimum variance principle to design an equalizing filterbank. Channel estimate is then obtained from an asymptotically tight lower bound of the filterbank output power. Numerical examples show that the proposed schemes approach the CRB as the signal-to-noise ratio (SNR) increases. Additionally, they exhibit low sensitivity to unknown narrowband interference and compare favorably with subspace blind channel estimators.

A non-coherent multiple input multiple output (MIMO) coded communication system over a frequency selective (FS) block fading channel is considered. A theoretical limit for the channel estimation error is established via a closed form derivation of the modified Cramer-Rao bound (MCRB) (more precisely, a tight lower bound to it) for the underlying channel model and equi-power signal constellations. Furthermore, it is shown that, for a practical coded MIMO system that employs turbo coding and an appropriate iterative decoding, the resulting mean squared error (MSE) in channel estimation approaches very closely to the derived theoretical limit.

To achieve high system capacity over frequency-selective fading channel, multi-carrier code-division-multiple-access (MC-CDMA) system with spatial diversity using space-time block coding (STBC) is considered. We present a subspace-based semi-blind channel estimation method under multiuser environment to resolve the inherent scalar ambiguity when blind estimation technique is used. Mean-square-error (MSE) expression for the proposed channel estimation technique is derived. Bit-error-rate (BER) when using linear zero-forcing (ZF) and minimum-mean-square-error (MMSE) multiuser detection techniques taking the channel estimation error into consideration are obtained through simulation. The correctness of the BERs is further verified through estimating the signal-to-interference and noise-ratio (SINR) at the multiuser detectors.

<p> Precoding is a technique which exploits transmission diversity by weighting the information stream, i.e. the transmitter sends the coded information to the receiver to achieve pre-knowledge of the channel. Visible light communication (VLC) channels inherently exhibit frequencyselective and multi-path fading effects that result in intersymbol interference (ISI) and thus reduces the data rate. To this end, optical multiple input multiple output (MIMO) can provide spatial diversity and thus achieve a high data rate. Inspired by these facts, we investigate different precoding and equalization schemes for 2 × 2 MIMO for the vehicle to vehicle (V2V-VLC) system. In this work, we consider three detection schemes, Zero Forcing (ZF), Maximum likelihood (ML), and Minimum Mean Squared Error (MMSE). Also, we consider flickering/dimming control and the nature of outdoor VLC channel as a frequency selective channel. Moreover, we investigate a transceiver considering perfect CSI based on different detection algorithms. Simulation results of the investigated transceiver demonstrate improvements in Bit Error Rate (BER) and Mean Squared Error (MSE) for the MMSE scheme </p>