218 research outputs found
Adaptive OFDM Radar for Target Detection and Tracking
We develop algorithms to detect and track targets by employing a wideband orthogonal frequency division multiplexing: OFDM) radar signal. The frequency diversity of the OFDM signal improves the sensing performance since the scattering centers of a target resonate variably at different frequencies. In addition, being a wideband signal, OFDM improves the range resolution and provides spectral efficiency. We first design the spectrum of the OFDM signal to improve the radar\u27s wideband ambiguity function. Our designed waveform enhances the range resolution and motivates us to use adaptive OFDM waveform in specific problems, such as the detection and tracking of targets. We develop methods for detecting a moving target in the presence of multipath, which exist, for example, in urban environments. We exploit the multipath reflections by utilizing different Doppler shifts. We analytically evaluate the asymptotic performance of the detector and adaptively design the OFDM waveform, by maximizing the noncentrality-parameter expression, to further improve the detection performance. Next, we transform the detection problem into the task of a sparse-signal estimation by making use of the sparsity of multiple paths. We propose an efficient sparse-recovery algorithm by employing a collection of multiple small Dantzig selectors, and analytically compute the reconstruction performance in terms of the -constrained minimal singular value. We solve a constrained multi-objective optimization algorithm to design the OFDM waveform and infer that the resultant signal-energy distribution is in proportion to the distribution of the target energy across different subcarriers. Then, we develop tracking methods for both a single and multiple targets. We propose an tracking method for a low-grazing angle target by realistically modeling different physical and statistical effects, such as the meteorological conditions in the troposphere, curved surface of the earth, and roughness of the sea-surface. To further enhance the tracking performance, we integrate a maximum mutual information based waveform design technique into the tracker. To track multiple targets, we exploit the inherent sparsity on the delay-Doppler plane to develop an computationally efficient procedure. For computational efficiency, we use more prior information to dynamically partition a small portion of the delay-Doppler plane. We utilize the block-sparsity property to propose a block version of the CoSaMP algorithm in the tracking filter
Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits
The use of large-scale antenna arrays can bring substantial improvements in
energy and/or spectral efficiency to wireless systems due to the greatly
improved spatial resolution and array gain. Recent works in the field of
massive multiple-input multiple-output (MIMO) show that the user channels
decorrelate when the number of antennas at the base stations (BSs) increases,
thus strong signal gains are achievable with little inter-user interference.
Since these results rely on asymptotics, it is important to investigate whether
the conventional system models are reasonable in this asymptotic regime. This
paper considers a new system model that incorporates general transceiver
hardware impairments at both the BSs (equipped with large antenna arrays) and
the single-antenna user equipments (UEs). As opposed to the conventional case
of ideal hardware, we show that hardware impairments create finite ceilings on
the channel estimation accuracy and on the downlink/uplink capacity of each UE.
Surprisingly, the capacity is mainly limited by the hardware at the UE, while
the impact of impairments in the large-scale arrays vanishes asymptotically and
inter-user interference (in particular, pilot contamination) becomes
negligible. Furthermore, we prove that the huge degrees of freedom offered by
massive MIMO can be used to reduce the transmit power and/or to tolerate larger
hardware impairments, which allows for the use of inexpensive and
energy-efficient antenna elements.Comment: To appear in IEEE Transactions on Information Theory, 28 pages, 15
figures. The results can be reproduced using the following Matlab code:
https://github.com/emilbjornson/massive-MIMO-hardware-impairment
Partial update blind adaptive channel shortening algorithms for wireline multicarrier systems
In wireline multicarrier systems a cyclic prefix is generally used to facilitate simple channel equalization at the receiver. The choice of the length of the cyclic prefix is a trade-off between maximizing the length of the channel for which inter-symbol interference is eliminated and optimizing the transmission efficiency. When the length of the channel is greater than the cyclic prefix, adaptive channel shorteners can be used to force the effective channel length of the combined channel and channel shortener to be within the cyclic prefix constraint. The focus of this thesis is the design of new blind adaptive time-domain channel shortening algorithms with good convergence properties and low computational complexity. An overview of the previous work in the field of supervised partial update adaptive filtering is given. The concept of property-restoral based blind channel shortening algorithms is then introduced together with the main techniques within this class of adaptive filters. Two new partial update blind (unsupervised) adaptive channel shortening algorithms are therefore introduced with robustness to impulsive noise commonly present in wireline multicarrier systems. Two further blind channel shortening algorithms are proposed in which the set of coefficients which is updated at each iteration of the algorithm is chosen deterministically. One of which, the partial up-date single lag autocorrelation maximization (PUSLAM) algorithm is particularly attractive due to its low computational complexity. The interaction between the receiver matched filter and the channel shortener is considered in the context of a multi-input single-output environment. To mitigate the possibility of ill-convergence with the PUSLAM algorithm an entirely new random PUSLAM (RPUSLAM) algorithm is proposed in which randomness is introduced both into the lag selection of the cost function underlying SLAM and the selection of the particular set of coefficients updated at each algorithm. This algorithm benefits from robust convergence properties whilst retaining relatively low computational complexity. All algorithms developed within the thesis are supported by evaluation on a set of eight carrier serving area test loop channels.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Uncoded space-time labelling diversity : data rate & reliability enhancements and application to real-world satellite broadcasting.
Doctoral Degree. University of KwaZulu-Natal, Durban.Abstract available in PDF
Low-complexity user selection for rate maximization in MIMO broadcast channels with downlink beamforming
We present in this work a low-complexity algorithm to solve the sum rate maximization problem in multiuser MIMO broadcast
channels with downlink beamforming. Our approach decouples the user selection problem from the resource allocation problem
and its main goal is to create a set of quasiorthogonal users. The proposed algorithm exploits physical metrics of the wireless
channels that can be easily computed in such a way that a null space projection power can be approximated efficiently. Based on the
derived metrics we present a mathematical model that describes the dynamics of the user selection process which renders the user
selection problem into an integer linear program. Numerical results show that our approach is highly efficient to form groups of
quasiorthogonal users when compared to previously proposed algorithms in the literature. Our user selection algorithm achieves
a large portion of the optimum user selection sum rate (90%) for a moderate number of active users
Discrete interferences optimum beamformer in correlated signal and interfering noise
This paper introduces a significant special situation where the noise is a collection of D-plane interference signals and the correlated noise of D+1 is less than the number of array components. An optimal beamforming processor based on the minimum variance distortionless response (MVDR) generates and combines appropriate statistics for the D+1 model. Instead of the original space of the N-dimensional problem, the interference signal subspace is reduced to D+1. Typical antenna arrays in many modern communication networks absorb waves generated from multiple point sources. An analytical formula was derived to improve the signal to interference and noise ratio (SINR) obtained from the steering errors of the two beamformers. The proposed MVDR processor-based beamforming does not enforce general constraints. Therefore, it can also be used in systems where the steering vector is compromised by gain. Simulation results show that the output of the proposed beamformer based on the MVDR processor is usually close to the ideal state within a wide range of signal-to-noise ratio and signal-to-interference ratio. The MVDR processor-based beamformer has been experimentally evaluated. The proposed processor-based MVDR system significantly improves performance for large interference white noise ratio (INR) in the sidelobe region and provide an appropriate beam pattern
Pilot sequence based IQ imbalance estimation and compensation
Abstract. As modern radio access technologies strive to achieve progressively higher data rates and to become increasingly more reliable, minimizing the effects of hardware imperfections becomes a priority. One of those imperfections is in-phase quadrature imbalance (IQI), caused by amplitude and phase response differences between the I and Q branches of the IQ demodulation process. IQI has been shown to deteriorate bit error rates, possibly compromise positioning performance, amongst other effects. Minimizing IQI by tightening hardware manufacturing constraints is not always a commercially viable approach, thus, baseband processing for IQI compensation provides an alternative.
The thesis begins by presenting a study in IQI modeling for direct conversion receivers, we then derive a model for general imbalances and show that it reproduces the two most common models in the bibliography. We proceed by exploring some of the existing IQI compensation techniques and discussing their underlying assumptions, advantages, and possible relevant issues.
A novel pilot-sequence based approach for tackling IQI estimation and compensation is introduced in this thesis. The idea is to minimize the square Frobenius norm of the error between candidate covariance matrices, which are functions of the candidate IQI parameters, and the sample covariance matrices, obtained from measurements. This new method is first presented in a positioning context with flat fading channels, where IQI compensation is used to improve the positioning estimates mean square error. The technique is then adapted to orthogonal frequency division multiplexing (OFDM) systems,including an version that exploits the 5G New Radio reference signals to estimate the IQI coefficients. We further generalize the new approach to solve joint transmitter and receiver IQI estimation and discuss the implementation details and suggested optimization techniques.
The introduced methods are evaluated numerically in their corresponding chapters under a set of different conditions, such as varying signal-to-noise ratio, pilot sequence length, channel model, number of subcarriers, etc. Finally, the proposed compensation approach is compared to other well-established methods by evaluating the bit error rate curves of 5G transmissions. We consistently show that the proposed method is capable of outperforming these other methods if the SNR and pilot sequence length values are sufficiently high. In the positioning simulations, the proposed IQI compensation method was able to improve the root mean squared error (RMSE) of the position estimates by approximately 25 cm. In the OFDM scenario, with high SNR and a long pilot sequence, the new method produced estimates with mean squared error (MSE) about a million times smaller than those from a blind estimator. In bit error rate (BER) simulations, the new method was the only compensation technique capable of producing BER curves similar to the curves without IQI in all of the studied scenarios
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