Block Circulant Decomposition of Cross-Correlation Matrix for Transmit MIMO Beamforming

This paper deals with the design of transmit probing signal under the trade-off between good target discrimination (low cross-correlation beam pattern) and beam pattern design (desired auto-correlation beam pattern) in a Multiple-Input- Multiple-Output (MIMO) radar configuration. The quartic optimization problem, with a finite alphabet constraint on the probing signal and using Quadrature Phase Shift Keying (QPSK) in a multiplexed antenna system, is solved through a Fourier series approximation of the desired beam pattern by exploiting a block circulant property of the transmit signal matrix. The mean square error between an ideal and the proposed crosscorrelation beam pattern is -35 dB enhancing the attractiveness of the proposed approach

Closed Form Discrete Unimodular MIMO Waveform Design Using Block Circulant Decomposition

This paper deals with the waveform design under the constraint of discrete multiphase unimodular sequences. It relies on Block Circulant decomposition of the slow-time transmitted waveform. The presented closed-form solution is capable of designing orthogonal signals, such that the virtual MIMO paradigm is enabled leading to enhanced angular resolution. On the other hand, the proposed method is also capable of approximating any desired radiation pattern within the physical limits of the transmitted array size. Simulation results prove the effectiveness in terms computational complexity, orthogonal signal design and the transmit beam pattern design under constant modulus constraint

Space time transceiver design over multipath fading channels

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A Vector Channel Based Approach to MIMO Radar Waveform Design for Extended Targets

Radar systems have been used for many years for estimating, detecting, classifying, and imaging objects of interest (targets). Stealthier targets and more cluttered environments have created a need for more sophisticated radar systems to gain more precise information about the radar environment. Because modern radar systems are largely defined in software, adaptive radar systems have emerged that tailor system parameters such as the transmitted waveform and receiver filter to the target and environment in order to address this need. The basic structure of a radar system exhibits many similarities to the structure of a communication system. Recognizing the parallel composition of radar systems and information transmission systems, initial works have begun to explore the application of information theory to radar system design, but a great deal of work still remains to make a full and clear connection between the problems addressed by radar systems and communication systems. Forming a comprehensive definition of this connection between radar systems and information transmission systems and associated problem descriptions could facilitate the cross-discipline transfer of ideas and accelerate the development and improvement of new system design solutions in both fields. In particular, adaptive radar system design is a relatively new field which stands to benefit from the maturity of information theory developed for information transmission if a parallel can be drawn to clearly relate similar radar and communication problems. No known previous work has yet drawn a clear parallel between the general multiple-input multiple-output (MIMO) radar system model considering both the detection and estimation of multiple extended targets and a similar multiuser vector channel information transmission system model. The goal of this dissertation is to develop a novel vector channel framework to describe a MIMO radar system and to study information theoretic adaptive radar waveform design for detection and estimation of multiple radar targets within this framework. Specifically, this dissertation first provides a new compact vector channel model for representing a MIMO radar system which illustrates the parallel composition of radar systems and information transmission systems. Second, using the proposed framework this dissertation contributes a compressed sensing based information theoretic approach to waveform design for the detection of multiple extended targets in noiseless and noisy scenarios. Third, this dissertation defines the multiple extended target estimation problem within the framework and proposes a greedy signal to interference-plus-noise ratio (SINR) maximizing procedure based on a similar approach developed for a collaborative multibase wireless communication system to optimally design wave forms in this scenario

Polynomial matrix decomposition techniques for frequency selective MIMO channels

For a narrowband, instantaneous mixing multi-input, multi-output (MIMO) communications system, the channel is represented as a scalar matrix. In this scenario, singular value decomposition (SVD) provides a number of independent spatial subchannels which can be used to enhance data rates or to increase diversity. Alternatively, a QR decomposition can be used to reduce the MIMO channel equalization problem to a set of single channel equalization problems. In the case of a frequency selective MIMO system, the multipath channel is represented as a polynomial matrix. Thus conventional matrix decomposition techniques can no longer be applied. The traditional solution to this broadband problem is to reduce it to narrowband form by using a discrete Fourier transform (DFT) to split the broadband channel into N narrow uniformly spaced frequency bands and applying scalar decomposition techniques within each band. This describes an orthogonal frequency division multiplexing (OFDM) based system. However, a novel algorithm has been developed for calculating the eigenvalue decomposition of a para-Hermitian polynomial matrix, known as the sequential best rotation (SBR2) algorithm. SBR2 and its QR based derivatives allow a true polynomial singular value and QR decomposition to be formulated. The application of these algorithms within frequency selective MIMO systems results in a fundamentally new approach to exploiting spatial diversity. Polynomial matrix decomposition and OFDM based solutions are compared for a wide variety of broadband MIMO communication systems. SVD is used to create a robust, high gain communications channel for ultra low signal-to-noise ratio (SNR) environments. Due to the frequency selective nature of the channels produced by polynomial matrix decomposition, additional processing is required at the receiver resulting in two distinct equalization techniques based around turbo and Viterbi equalization. The proposed approach is found to provide identical performance to that of an existing OFDM scheme while supporting a wider range of access schemes. This work is then extended to QR decomposition based communications systems, where the proposed polynomial approach is found to not only provide superior bit-error-rate (BER) performance but significantly reduce the complexity of transmitter design. Finally both techniques are combined to create a nulti-user MIMO system that provides superior BER performance over an OFDM based scheme. Throughout the work the robustness of the proposed scheme to channel state information (CSI) error is considered, resulting in a rigorous demonstration of the capabilities of the polynomial approach

Channel Estimation in Half and Full Duplex Relays

abstract: Both two-way relays (TWR) and full-duplex (FD) radios are spectrally efficient, and their integration shows great potential to further improve the spectral efficiency, which offers a solution to the fifth generation wireless systems. High quality channel state information (CSI) are the key components for the implementation and the performance of the FD TWR system, making channel estimation in FD TWRs crucial. The impact of channel estimation on spectral efficiency in half-duplex multiple-input-multiple-output (MIMO) TWR systems is investigated. The trade-off between training and data energy is proposed. In the case that two sources are symmetric in power and number of antennas, a closed-form for the optimal ratio of data energy to total energy is derived. It can be shown that the achievable rate is a monotonically increasing function of the data length. The asymmetric case is discussed as well. Efficient and accurate training schemes for FD TWRs are essential for profiting from the inherent spectrally efficient structures of both FD and TWRs. A novel one-block training scheme with a maximum likelihood (ML) estimator is proposed to estimate the channels between the nodes and the residual self-interference (RSI) channel simultaneously. Baseline training schemes are also considered to compare with the one-block scheme. The Cramer-Rao bounds (CRBs) of the training schemes are derived and analyzed by using the asymptotic properties of Toeplitz matrices. The benefit of estimating the RSI channel is shown analytically in terms of Fisher information. To obtain fundamental and analytic results of how the RSI affects the spectral efficiency, one-way FD relay systems are studied. Optimal training design and ML channel estimation are proposed to estimate the RSI channel. The CRBs are derived and analyzed in closed-form so that the optimal training sequence can be found via minimizing the CRB. Extensions of the training scheme to frequency-selective channels and multiple relays are also presented. Simultaneously sensing and transmission in an FD cognitive radio system with MIMO is considered. The trade-off between the transmission rate and the detection accuracy is characterized by the sum-rate of the primary and the secondary users. Different beamforming and combining schemes are proposed and compared.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Propagation parameter estimation in MIMO systems

Multiple antenna techniques are in the heart of modern and next-generation wireless communications systems, such as 3GPP Long-Term Evolution (LTE), IEEE 802.16e (WiMAX), and IMT-Advanced (IMT-A). Such techniques are considered for the high link capacity gains that are achievable from spatial multiplexing, and also for the system capacity, link reliability, and coverage benefits that are possible from spatial diversity, beamforming, and spatial division multiple access techniques. Accurate spatial channel models play a key role on the characterization of the propagation environment and determination of which techniques provide higher gains in a given scenario. Such models are also fundamental tools in network planning, link and system performance studies, and transceiver development. Realistic channel models are based on measurements. Hence, there is a need for techniques that extract the relevant information from huge amount of data. This may be achieved by estimating model parameters from the data. Most estimation algorithms are based on the assumption that the channel can be modeled as a combination of a finite number of specular, highly-concentrated paths, requiring estimation of a very large number of parameters. In this thesis, estimators are derived for the parameters of the concentrated propagation paths and the diffuse scattering component that are frequently observed in Multiple-Input Multiple-Output (MIMO) channel sounding measurements. Low complexity methods are derived for efficient computation of the estimates. The derived methods are based on a stochastic channel model, leading to a lower-dimensional parameter set that allow a reduction in computational complexity and improved statistical performance compared to methods found in the literature. Simulation results demonstrate that high quality estimates are obtained. The large sample performance of the estimators are studied by establishing the Cramér-Rao lower bound (CRLB) and comparing it to the variances of the estimates. The simulations show that the variances of the proposed estimation techniques attain the CRLB for relatively small sample size for most parameters, and no bias is observed