A Pre-FFT OFDM adaptive antenna array with eigenvector combining

Abstract-This paper proposes a novel pre-FFT type OFDM adaptive array antenna called "Eigenvector Combining." The eigenvector combining is a realization of a post-FFT type OFDM adaptive array antenna through a pre-FFT signal processing, so it can achieve excellent performance with less computational complexity and shorter training symbols. Numerical results demonstrate that the proposed eigenvector combining shows excellent bit error rate performance close to the lower bound just with two training symbols. I. INTRODUCTION Orthogonal frequency division multiplexing (OFDM), which is a pre-distortion or an equalization technique at transmit side in a sense, is an efficient technique for high-speed digital transmission over severe multipath fading channels To maintain high-speed reliable wireless communications systems, the use of multiple antennas at receive side has been considered as an effective tool not only for gain enhancement, increased spectral efficiency[3] but also for interference suppressio

Space time transceiver design over multipath fading channels

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Receive Soft Antenna Selection for Noise-Limited/Interference MIMO Channels

Although the Multi-Input and Multi-Output (MIMO) communication systems provide very high data rates with low error probabilities, these advantages are obtained at the expense of having high signal processing tasks and the hardware cost, e.g. expensive Analog-to-Digital (A/D) converters. The increased hardware cost is mainly due to having multiple Radio Frequency (RF) chains (one for each antenna element). Antenna selection techniques have been proposed to lower the number of RF chains and provide a low cost MIMO system. Among them, due to a beamforming capability Soft Antenna Selection (SAS) schemes have shown a great performance improvement against the traditional antenna sub-set selection methods for the MIMO communication systems with the same number of RF chains. A SAS method is basically realized by a pre-processing module which is located in RF domain of a MIMO system. In this thesis, we investigate on the receive SAS-MIMO, i.e. a MIMO system equipped with a SAS module at the receiver side, in noise-limited/interference channels. For a noise-limited channel, we study the SAS-MIMO system for when the SAS module is implemented before Low Noise Amplifier (LNA), so-called pre-LNA, under both spatial multiplexing and diversity transmission strategies. The pre-LNA SAS module only consists of passive elements. The optimality of the pre-LNA SAS method is investigated under two di erent practical cases of either the external or internal noise dominates. For the interference channel case, the post-LNA SAS scheme is optimized based on Power Angular Spectrum (PAS) of the received interference signals. The analytical derivations for both noise-limited and interference channels are verified via the computer simulations based on a general Rician statistical MIMO channel model. The simulation results reveal a superiority of the post-LNA SAS to the post-LNA SAS at any condition. Moreover, using the simulations performed for the interference channels we show that the post-LNA SAS is upper bounded by the full-complexity MIMO. Since in both above-mentioned channels, noise-limited and interference, the channel knowledge is needed for the SAS optimization, in this thesis we also propose a two-step channel estimation method for the SAS-MIMO. This channel estimation is based on an Orthogonal Frequency-Division Multiplexing (OFDM) MIMO system. Two di erent estimators of Least-Square (LS) and Minimum-Mean-Square- Error (MMSE) are applied. Simulation results show a superiority of the MMSE method to the LS estimator for a MIMO system simulated under the 802.16 framing strategy. Moreover, a 802.11a framing based SAS-MIMO is simulated using MATLAB SIMULINK to verify the two-step estimation procedure. Furthermore, we also employ a ray-tracing channel simulation to assess di erent SAS configurations, i.e. realized by active (post-LNA) and/or passive (pre-LNA) phased array, in terms of signal coverage. In this regard, a rigorous Signal to Noise Ratio (SNR) analysis is performed for each of these SAS realizations. The results show that although the SAS method performance is generally said to be upperbounded by a full-complexity MIMO, it shows a better signal coverage than the full-complexity MIMO

MAC and baseband processors for RF-MIMO WLAN

The article describes hardware solutions for the IEEE 802.11 medium access control (MAC) layer and IEEE 802.11a digital baseband in an RF-MIMO WLAN transceiver that performs the signal combining in the analogue domain. Architecture and implementation details of the MAC processor including a hardware accelerator and a 16-bit MACphysical layer (PHY) interface are presented. The proposed hardware solution is tested and verified using a PHY link emulator. Architecture, design, implementation, and test of a reconfigurable digital baseband processor are described too. Description includes the baseband algorithms (the main blocks being MIMO channel estimation and Tx-Rx analogue beamforming), their FPGA-based implementation, baseband printed-circuit-board, and real-time test

Cyclic Prefix-Free MC-CDMA Arrayed MIMO Communication Systems

The objective of this thesis is to investigate MC-CDMA MIMO systems where the antenna array geometry is taken into consideration. In most MC-CDMA systems, cyclic pre xes, which reduce the spectral e¢ ciency, are used. In order to improve the spectral efficiency, this research study is focused on cyclic pre x- free MC-CDMA MIMO architectures. Initially, space-time wireless channel models are developed by considering the spatio-temporal mechanisms of the radio channel, such as multipath propaga- tion. The spatio-temporal channel models are based on the concept of the array manifold vector, which enables the parametric modelling of the channel. The array manifold vector is extended to the multi-carrier space-time array (MC-STAR) manifold matrix which enables the use of spatio-temporal signal processing techniques. Based on the modelling, a new cyclic pre x-free MC- CDMA arrayed MIMO communication system is proposed and its performance is compared with a representative existing system. Furthermore, a MUSIC-type algorithm is then developed for the estimation of the channel parameters of the received signal. This proposed cyclic pre x-free MC-CDMA arrayed MIMO system is then extended to consider the effects of spatial diffusion in the wireless channel. Spatial diffusion is an important channel impairment which is often ignored and the failure to consider such effects leads to less than satisfactory performance. A subspace-based approach is proposed for the estimation of the channel parameters and spatial spread and reception of the desired signal. Finally, the problem of joint optimization of the transmit and receive beam- forming weights in the downlink of a cyclic pre x-free MC-CDMA arrayed MIMO communication system is investigated. A subcarrier-cooperative approach is used for the transmit beamforming so that there is greater flexibility in the allocation of channel symbols. The resulting optimization problem, with a per-antenna transmit power constraint, is solved by the Lagrange multiplier method and an iterative algorithm is proposed

MIMO Systems

In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Reciprocity Calibration for Massive MIMO: Proposal, Modeling and Validation

This paper presents a mutual coupling based calibration method for time-division-duplex massive MIMO systems, which enables downlink precoding based on uplink channel estimates. The entire calibration procedure is carried out solely at the base station (BS) side by sounding all BS antenna pairs. An Expectation-Maximization (EM) algorithm is derived, which processes the measured channels in order to estimate calibration coefficients. The EM algorithm outperforms current state-of-the-art narrow-band calibration schemes in a mean squared error (MSE) and sum-rate capacity sense. Like its predecessors, the EM algorithm is general in the sense that it is not only suitable to calibrate a co-located massive MIMO BS, but also very suitable for calibrating multiple BSs in distributed MIMO systems. The proposed method is validated with experimental evidence obtained from a massive MIMO testbed. In addition, we address the estimated narrow-band calibration coefficients as a stochastic process across frequency, and study the subspace of this process based on measurement data. With the insights of this study, we propose an estimator which exploits the structure of the process in order to reduce the calibration error across frequency. A model for the calibration error is also proposed based on the asymptotic properties of the estimator, and is validated with measurement results.Comment: Submitted to IEEE Transactions on Wireless Communications, 21/Feb/201

Eigenvector-based multidimensional frequency estimation : identifiability, performance, and applications.

Multidimensional frequency estimation is a classic signal processing problem that has versatile applications in sensor array processing and wireless communications. Eigenvalue-based two-dimensional (2-D) and N -dimensional ( N -D) frequency estimation algorithms have been well documented, however, these algorithms suffer from limited identifiability and demanding computations. This dissertation develops a framework on eigenvector-based N -D frequency estimation, which contains several novel algorithms that estimate a structural matrix from eigenvectors and then resolve the N -D frequencies by dividing the elements of the structural matrix. Compared to the existing eigenvalue-based algorithms, these eigenvector-based algorithms can achieve automatic pairing without an extra frequency pairing step, and tins the computational complexity is reduced. The identifiability, performance, and complexity of these algorithms are also systematically studied. Based on this study, the most relaxed identifiability condition for the N -D frequency estimation problem is given and an effective approach using optimized weighting factors to improve the performance of frequency estimation is developed. These results are applied in wireless communication for time-varying multipath channel estimation and prediction, as well as for joint 2-D Direction-of-arrival (DOA) tracking of multiple moving targets