A new MAP based channel estimation technique for multiple-input multiple-output (MIMO) systems

Multiple-Input Multiple-Output (MIMO) systems that provide significant increase in channel capacity is rapidly emerging as the new frontier of wireless industry. MIMO systems require the simultaneous use of multiple transmit and receive antennas to dramatically increase data rates and to improve performance reliability. An effective and practical way to approach the capacity promised by MIMO systems is to employ space-time coding (STC). It elegantly combines temporal and spatial correlation into the transmitted symbols to realize diversity and coding gains. Most STC schemes are designed for known quasi-static channels however this assumption is not always justified. MIMO channels often undergo frequency selective fading that leads to intersymbol interference (ISI), which limits the performance of MIMO systems. The effect of imperfect channel estimation on the bit error rate (BER) of MIMO systems utilizing STC is investigated. An analysis and comparison into the BER degradations of simple transmit diversity (STD) and maximal ratio combining (MRC) schemes due to multipath channel estimation errors are presented. Closed form expressions are derived for the BER performances of the schemes that employ an equalization process to mitigate the ISI caused by the multipath in frequency selective channel. BER curves show that the performance deterioration in the MIMO scheme outweighs the benefits achieved over the single antenna case when the channel estimation errors are large. Results expose the deleterious effects of inaccurate channel estimation on the performance of MIMO systems. Hence, the development of practical and novel channel estimation approaches are desired for MIMO systems using STC. This dissertation introduces a new MAP based channel estimation technique that is amenable to STC scheme employing two transmit antennas and operating in multipath bandlimited channel. The complex channel parameters are treated as two real-valued tap coefficients; each taking one of M possible amplitude levels with equal probability. The proposed estimation technique is based on an iterative procedure derived through the maximum a posteriori (MAP) probability approach. Unlike classic estimation techniques, we iterate on the probabilities of the different coefficients rather than on the values of the coefficients. Two low complexity algorithms based on the developed channel estimation technique and simple to implement in practical MIMO systems are also introduced. The performances of the two algorithms are assessed by combined analysis and simulation. Results are presented and compared against the performance of conventional channel estimation techniques. Results show that the required performance can be achieved with less number of iterations using the proposed algorithms compared to conventional techniques

Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments

Hybrid massive MIMO structures with reduced hardware complexity and power consumption have been widely studied as a potential candidate for millimeter wave (mmWave) communications. Channel estimators that require knowledge of the array response, such as those using compressive sensing (CS) methods, may suffer from performance degradation when array-inherent impairments bring unknown phase errors and gain errors to the antenna elements. In this paper, we design matrix completion (MC)-based channel estimation schemes which are robust against the array-inherent impairments. We first design an open-loop training scheme that can sample entries from the effective channel matrix randomly and is compatible with the phase shifter-based hybrid system. Leveraging the low-rank property of the effective channel matrix, we then design a channel estimator based on the generalized conditional gradient (GCG) framework and the alternating minimization (AltMin) approach. The resulting estimator is immune to array-inherent impairments and can be implemented to systems with any array shapes for its independence of the array response. In addition, we extend our design to sample a transformed channel matrix following the concept of inductive matrix completion (IMC), which can be solved efficiently using our proposed estimator and achieve similar performance with a lower requirement of the dynamic range of the transmission power per antenna. Numerical results demonstrate the advantages of our proposed MC-based channel estimators in terms of estimation performance, computational complexity and robustness against array-inherent impairments over the orthogonal matching pursuit (OMP)-based CS channel estimator.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Channel Estimation and Symbol Detection In Massive MIMO Systems Using Expectation Propagation

The advantages envisioned from using large antenna arrays have made massive multiple- input multiple-output systems (also known as massive MIMO) a promising technology for future wireless standards. Despite the advantages that massive MIMO systems provide, increasing the number of antennas introduces new technical challenges that need to be resolved. In particular, symbol detection is one of the key challenges in massive MIMO. Obtaining accurate channel state information (CSI) for the extremely large number of chan- nels involved is a difficult task and consumes significant resources. Therefore for Massive MIMO systems coherent detectors must be able to cope with highly imperfect CSI. More importantly, non-coherent schemes which do not rely on CSI for symbol detection become very attractive. Expectation propagation (EP) has been recently proposed as a low complexity algo- rithm for symbol detection in massive MIMO systems , where its performance is evaluated on the premise that perfect channel state information (CSI) is available at the receiver. However, in practical systems, exact CSI is not available due to a variety of reasons in- cluding channel estimation errors, quantization errors and aging. In this work we study the performance of EP in the presence of imperfect CSI due to channel estimation er- rors and show that in this case the EP detector experiences significant performance loss. Moreover, the EP detector shows a higher sensitivity to channel estimation errors in the high signal-to-noise ratio (SNR) regions where the rate of its performance improvement decreases. We investigate this behavior of the EP detector and propose a Modified EP detector for colored noise which utilizes the correlation matrix of the channel estimation error. Simulation results verify that the modified algorithm is robust against imperfect CSI and its performance is significantly improved over the EP algorithm, particularly in the higher SNR regions, and that for the modified detector, the slope of the symbol error rate (SER) vs. SNR plots are similar to the case of perfect CSI. Next, an algorithm based on expectation propagation is proposed for noncoherent symbol detection in large-scale SIMO systems. It is verified through simulation that in terms of SER, the proposed detector outperforms the pilotbased coherent MMSE detector for blocks as small as two symbols. This makes the proposed detector suitable for fast fading channels with very short coherence times. In addition, the SER performance of this detec- tor converges to that of the optimum ML receiver when the size of the blocks increases. Finally it is shown that for Rician fading channels, knowledge of the fading parameters is not required for achieving the SER gains. A channel estimation method was recently proposed for multi-cell massive MIMO sys- tems based on the eigenvalue decomposition of the correlation matrix of the received vectors (EVD-based). This algorithm, however, is sensitive to the size of the antenna array as well as the number of samples used in the evaluation of the correlation matrix. As the final work in this dissertation, we present a noncoherent channel estimation and symbol de- tection scheme for multi-cell massive MIMO systems based on expectation propagation. The proposed algorithm is initialized with the channel estimation result from the EVD- based method. Simulation results show that after a few iterations, the EP-based algorithm significantly outperforms the EVD-based method in both channel estimation and symbol error rate. Moreover, the EP-based algorithm is not sensitive to antenna array size or the inaccuracies of sample correlation matrix

MIMO MC-CDMA WITH DIFFERENTIAL UNITARY SPACE TIME FREQUENCY MODULATION FOR HIGH MOBILITY SCENARIO

Future generation communication systems require high data rate and high mobility communication which has good performance, good resistance to errors and good spectral efficiency. The recent multicarrier scheme although it can give high data rate communication system but the performance is poor while being used in high mobility scenario. The current multicarrier scheme require channel estimation to decode the received signal, but there are some conditions when the channel state information practically can be acquired, for example where the channel condition change very fast when user in high mobility condition. To solve this problem, non-coherent transmission system without channel estimation is the answer. In this thesis the non-coherent transmission scheme that used is Differential Unitary Space Time Frequency Modulation (DUSTFM) for 2x2 MIMO and 4x4 MIMO are proposed. The proposed schemes are combined with MC-CDMA to give better performance in high mobility condition. The combined methods exploit the advantage of each scheme (MIMO, MC-CDMA, differential modulation, STFC), in order to achieve a high data rate communication system which is robust against frequency selective fading, multipath fading, and fast fading. The proposed DUSTFM scheme for 2x2 MIMO can give better performance compared to the main reference that have been proposed by Tran when BPSK is used as mapper but the performance of the proposed system while QPSK is used as mapper, is significantly decline due to the inability of the symbol detection to separate the symbols effectively. The proposed DUSTFM for 4x4 MIMO can give better performance until 8dB of gain compared to the 2x2 MIMO with DUSTFM when both of them BPSK is used as mapper. However the 4x4 MIMO with DUSTFM model only can work well when every antenna is using different frequency band, this means the spectral efficiency of the 4x4 MIMO with DUSTFM model is very bad compared to the 2x2 MIMO with DUSTFM model

An improved channel estimation approach for MIMO-OFDM systems

University of Technology, Sydney. Faculty of Engineering and Information Technology.In wireless environments, signals bounce off many obstacles such as mountains, buildings, trees, etc. as they propagate between transmitters and receivers. The resultant signal at the receive antenna is, therefore, often the sum of the attenuated transmitted signal and one or more delayed versions of the transmitted signal. The received signal also suffers from intersymbol interference which degrades the quality of signal to a certain extent. However, MIMO-OFDM systems are designed to take advantage of the multi-path properties in wireless communications and are capable of improving transmission rate, range and reliability simultaneously. MIMO-OFDM attracts a good deal of research and commercial interest because of the perceived benefits, and has been adopted in many wireless standards such as IEEE 802.1 In, IEEE 802.16e. Such systems are also potential candidates for fourth-generation (4G) systems. However, practical problems still exist in implementing MIMO-OFDM, for example, in the estimation of channel state information (CS1). This thesis studies the issues of MIMO, OFDM and the relevant techniques of MIMO-OFDM, and focuses on proposing a practical, low complexity and accurate channel estimation method for such systems. In a MIMO-OFDM system, CSI is required at the receiver to perform space-time decoding or diversity combining. In many practical wireless applications, the propagation environment is both complex and time-variant, leading to CSI estimation errors and overall system performance degradation. A variety of channel estimation approaches have been proposed in the literature to address this problem. One of the most important parameters of CSI is the number of significant or dominant propagation paths, also referred to as the number of channel taps. However, in most existing estimation schemes for MIMO-OFDM, there is an assumption that the number of channel taps is known at the receiver. In reality, in order to perform space-time decoding, the receiver needs to estimate the number of channel taps from the received signal with this estimation process sometimes aided by the insertion of pilot tones into the transmitted signal. In this thesis, a pilot-assisted, conditional model-order estimation (CME) based channel estimation algorithm is presented. The approach can be utilised to detect both the number of channel resolvable paths and channel gains for MIMO-OFDM systems. The performance of the proposed algorithm is compared with the commonly used minimum description length (MDL) algorithm by mean of simulation in the context of a 2x2 MIMO-OFDM system. Results indicate that the new algorithm is superior to the MDL algorithm in channel order estimation over an unknown, noisy, multipah fading channel with limited pilot assistance. Furthermore, the proposed scheme is tested in both fixed and mobile broadband MIMO-OFDM systems based on WiMAX techniques in Matlab simulation, and its capacity is verified again for those near practical broadband MIMO- OFDM systems in the absence of prior knowledge of model parameters. Finally, with the purpose to “make the thing work in practice”, a 2x2 MIMO baseband platform is built in order to demonstrate the proposed scheme. The platform consists of two DSP based, real-time development boards called SignalWAVe, produced by Lyrtech. Given the existing hardware components, the whole platform is built based on a fixed MIMO-OFDM system according to WiMAX standard, and the results demonstrate that the proposed algorithm is a valid approach in practice

Millimeter wave link configuration in practical scenarios

Acquiring channel state information (CSI) for link configuration in wideband millimeter wave (mmWave) massive multiple-input-multiple-output (MIMO) systems with hybrid architectures is challenging, due to the high dimensions of the channel matrices, the low signal-to-noise ratio (SNR) before beamforming, the various hardware constraints and the high mobility in the vehicular context. Previous work in this area exploits channel sparsity, statistical priors or side information to reduce the overhead associated to initial channel estimation or channel tracking. These works consider, however, a system model that neglects hardware imperfections. In addition, many of the proposed solutions are unable to operate in some realistic scenarios, such as vehicle-to-everything (V2X) communications. In this dissertation, we develop new signal processing solutions that can enable low-overhead mmWave link configuration under various disturbances and practical limitations, e.g., hardware impairments, calibration errors, beam squint effect, channel blockage, high mobility, to name a few. In the first part of this dissertation, we focus on the problem of wideband channel estimation for mmWave MIMO systems with different hardware imperfections. We first design a dictionary learning aided channel estimation strategy for wideband mmWave MIMO systems by explicitly considering the hardware uncertainties and calibration errors, and then derive algorithms that learn the optimal sparsifying dictionaries for channel representation and estimation. In a second contribution of this part, we further develop a dictionary learning aided compressive channel estimation scheme for mmWave MIMO systems by incorporating beam squint into the model of array responses. Numerical results show the proposed solutions can adapt to the practical scenarios and help reduce the overhead associated with channel estimation significantly. In the second part of this dissertation, we deal with the problem of wideband channel tracking for mmWave MIMO systems with or without the impact of blockage. We first introduce statistical channel models that include the evolution models for channel gains and angles of arrival/departure, as well as the statistics of blockage events. Then, we design novel blockage detection schemes and efficient Bayesian channel tracking algorithms to facilitate the low-overhead tracking with or without blockage. Numerical results corroborate that the proposed solutions achieve better channel tracking performance even in mobile scenarios that suffer from highly dynamic blockage events.Electrical and Computer Engineerin

Dispensing with Channel Estimation…

In this article, we investigate the feasibility of noncoherent detection schemes in wireless communication systems as a low-complexity alternative to the family of coherent schemes. The noncoherent schemes require no channel knowledge at the receiver for the detection of the received signal, while the coherent schemes require channel inherently complex estimation, which implies that pilot symbols have to be transmitted resulting in a wastage of the available bandwidth as well as the transmission power