Channel estimation for layered space-time systems

A novel training-based channel estimation scheme is presented for an uncoded layered space-time system that operates in a wideband frequency selective fading environment of a time-varying multiple-input multiple-output (MIMO) channel. The method uses a pilot matrix consisting of pilot symbols derived from the Paley-Hadamard matrix to estimate the MIMO channel impulse response. The orthogonality of the pilot matrix resolves both the intersymbol interference and the cochannel interference of the wideband MIMO channel. The Toeplitz-like structure of the Paley-Hadamard matrix is exploited to minimise the length of the pilot sequence, reducing pilot symbol overhead. A decision feedback algorithm is used to track the time-varying MIMO channel. Results are presented which demonstrate the performance of the proposed MIMO channel estimator in a layered space-time system for different mobile velocities and system configurations.

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.

Inherent inter-symbol and inter-carrier interference elimination ability of cyclic prefixed OFDM transmission fails for the case of multipath fading channels when the channel impulse response (CIR) length exceeds the duration of cyclic prefix (CP). Conventional channel estimation and equalization schemes, if applied to this case of insufficient CP, suffer significant performance degradation. We propose, in this paper, a channel estimation scheme that enables estimation of the complete CIR even beyond the CP length. We then design an optimal MMSE based equalizer for the suppression of insufficient CP generated interference. A robust and low complexity version of this equalizer is also derived. Simulation results for the proposed schemes show significant performance gain at low SNRs and drastic reduction of the error floors at high SNRs and more importantly, as opposed to earlier schemes, without any loss in transmission efficiency.

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

We study how to design a worst-case robust training sequence for multiple-input multiple-output (MIMO) channel estimation. We consider mean-squared error of channel estimates as the figure of merit which is a function of second-order statistics of the MIMO channel, i.e., channel covariance matrix, in order to optimize training sequences under a total power constraint. In practical applications, the channel covariance matrix is not known perfectly. Thus the main aspect of our design is to improve robustness of the training sequences against possible uncertainties in the available channel covariance matrix. Using a deterministic uncertainty model, we formulate a robust training sequence design as a minimax optimization problem where we take such imperfections into account. We investigate the robust design problem assuming the general case of an arbitrarily correlated MIMO channel and a non-empty compact convex uncertainty set. We prove that such a problem admits a globally optimal solution by exploiting the convex-concave structure of the objective function, and propose numerical algorithms to address the robust training design problem. We proceed the analysis by considering multiple-input single-output (MISO) channels and Kronecker structured MIMO channels along with unitarily-invariant uncertainty sets. For these scenarios, we show that the problem is diagonalized by the eigenvectors of the nominal covariance matrices so that the robust design is significantly simplified from a complex matrix-variable problem to a real vector-variable power allocation problem. For the MISO channel, we provide closed-form solutions for the robust training sequences with the uncertainty sets defined by the spectral norm and nuclear norm.

The performance of the widely applied time‐domain channel estimation for SISO‐ and MIMO‐OFDM systems strongly depends on the preciseness of information regarding maximum channel impulse response (CIR) length. In practice the required CIR length is usually approximated by the length of the cyclic prefix (CP) which is an upper bound for most of the actual instantaneous CIR length. In this article, we introduce an appropriate channel length estimation method—named as Frequency Domain Channel Length Indicator (FCLI) which estimates the CIR length minimising the estimated mean square error (MSE) of an instantaneous channel estimate. The simulation results present that the FCLI outperforms the approximation of CP length. In addition, we investigate two applications of FCLI on (1) CP adaptation OFDM systems in which we minimise the overhead of the CP by the estimated CIR length and (2) CP‐free OFDM systems in which we directly re‐construct the structure of cyclic repetition with reduced error. Compared to the traditional channel estimation with the approximation of CP length, the overall system throughput of either CP adaptation or CP‐free OFDM transmission scheme is significantly enhanced regardless of perfect synchronisation or practical distribution of time offsets. Copyright © 2007 John Wiley & Sons, Ltd.

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.

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.

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.

In this paper, we consider a generic model of space-time bit-interleaved coded modulation (ST-BICM) on a multiple-input multiple-output (MIMO) Rayleigh fading multipath channel. A practical low-complexity receiver structure performing iteratively MIMO data detection, channel decoding and channel estimation, is presented. The MIMO data detection, employing a reduced-state list-type soft output Viterbi algorithm enables to cope with severe channel intersymbol interference (ISI) without MIMO prefiltering. Among other results, simulations show that our approach can dramatically improve the downlink performance of time-division multiple access (TDMA) systems with high order modulation, keeping a reasonable complexity at the receiver side.

A improved algorithm to perform channel estimation and equalization for multiple input multiple output(MIMO) systems in frequency and time-selective fading environments is put forward. lt contains two steps: training and tracking. In training phase, improved Kalman filtering, namely robust Kalman filtering(RKF), is exploited to estimate channel impulse response(CIR). After that, in tracking stage,the RKF and minimum mean-square error feedback decision equalizer (MMSE-DFE) cooperate to track the time-varying channel. The RKF recursions is presented and a closed-form solution for baud rate MIMO MMSE-DFE under perfect knowledge of CIR and correct past decisions conditions is derived. In addition, it regards unknown dc-offset due to zero intermediate frequency(IF) at the receiver as the mean of measurement noise, which is estimated as a byproduct through robust Kalman filters. Finally, it is compared with well-known ones,such as least mean-square (LMS), recursive least square (RLS), Kalman filtering (KF). All these show that the proposal exhibits better performance.

This paper deals with optimized Multiple-Input Multiple Output (MIMO) channel estimation and ensuing information throughput conveyed by pilot-based multi-antenna systems affected by both spatially colored Multiple Access Interference (MUI) and errors in the available channel estimates. The architecture of the Minimum Mean Square Error (MMSE) MIMO channel estimator is derived and the related analytical conditions for the optimal design of space-time training sequences are provided. Afterwards, closed form expressions for the maximum information throughput sustained by the considered systems for Gaussian distributed input signals are given and, then, a novel powers’ allocation algorithm for the asymptotical achievement of the system capacity is developed. Considerations about optimized space-division MAC strategies are also provided.

A 2-D shooting-and-bouncing (SBR) ray-tracing method considering ray reflect, transmission and diffraction is used to analyze antenna spacing effect on capacity complementary cumulative distribution functions(CCDFS) of indoor non-line-of-sight (NLOS) MIMO channel and indoor multiple-input multiple-output (MIMO) channel with line-of-sight (LOS) path. The results show that the capacities of MIMO channel with LOS path are less than NLOS MIMO channel capacities in general. But whether in indoor MIMO channel with LOS path or in NLOS indoor MIMO channel, capacities CCDFS of indoor MIMO systems with antenna spacing of 5/spl times/ can reach the theoretical independent and identically distribution (i.i.d) Rayleigh capacities nearly fully. With the decrease of antenna spacing, the capacities dropped also. And the less antenna spacing, the more difference between indoor NLOS MIMO channel capacities and capacities of MIMO channel with LOS path.

Conventional correlation models fail to accurately represent the correlation properties of ultrawideband (UWB) multiple-input multiple-output (MIMO) channels. In our previous work, a framework for constructing correlated UWB MIMO channel models was proposed, where spatial correlation was introduced into both the multipath amplitude and time-of-arrival (ToA) in the channel impulse response. Based on this framework, in this paper we first present a simplified UWB MIMO channel model that represents a reasonable compromise between analytical tractability and model accuracy. We show that this model yields a structured space-frequency (SF) channel covariance matrix, making it suitable for theoretical system performance analysis. To illustrate the merit of this model, we apply it to evaluate the maximum diversity order in SF coded UWB MIMO systems. In contrast to previous work based on conventional wideband MIMO correlated channel models, we show that the maximum diversity order of a UWB MIMO system is not limited by the sum of the ranks of multipath amplitude correlation matrices.

In the emerging high mobility vehicle-to-everything (V2X) communications using millimeter wave (mmWave) and sub-THz, multiple-input multiple-output (MIMO) channel estimation is an extremely challenging task. At mmWaves/sub-THz frequencies, MIMO channels exhibit few leading paths in the space-time (ST) domain (i.e., directions or arrival/departure and delays). Algebraic low-rank (LR) channel estimation exploits ST channel sparsity through the computation of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">position-dependent</i> MIMO channel eigenmodes leveraging recurrent training vehicle passages in the coverage cell. LR requires vehicles’ geographical positions and tens to hundreds of training vehicles’ passages for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">each</i> position, leading to significant complexity and control signaling overhead. Here, we design a deep-learning (DL)-based LR channel estimation method to infer MIMO channel eigenmodes in V2X urban settings, starting from a single least squares (LS) channel estimate and without needing vehicle’s position information. Numerical results show that the proposed method attains comparable mean squared error (mse) performance as the position-based LR. Moreover, we show that the proposed model can be trained on a reference scenario and be effectively transferred to urban contexts with different ST channel features, providing comparable mse performance without an explicit transfer learning procedure. This result eases the deployment in arbitrary dense urban scenarios.