Advanced mobile communication systems are characterized by a dense deployment of different types of wireless access points. Since these systems are primarily limited by interference, multiple-input multiple-output (MIMO) techniques as well as coordinated transmission and detection schemes are necessary to mitigate this limitation. Thus, mobile communication systems become more complex which requires that also the mathematical tools for their theoretical analysis must evolve. These must be able to take the most important system characteristics into account, such as fading, path loss, and interference. The aim of this thesis is to develop such tools based on large random matrix theory and to demonstrate their usefulness with the help of several practical applications, such as the performance analysis of network MIMO and large-scale MIMO systems, the design of low-complexity polynomial expansion detectors, and the study of random beamforming techniques as well as multi-hop relay and double-scattering channels. The methods developed in this work provide deterministic approximations of the system performance which become arbitrarily tight in the large system regime with an unlimited number of transmitting and receiving devices. This leads in many cases to simple and close approximations of the finite-size system performance and allows one to draw relevant conclusions about the most significant parameters. One can think of these methods as a way to provide a deterministic abstraction of the physical layer which substantially reduces the system complexity. Due to this complexity reduction, it is possible to carry out a system optimization which would be otherwise intractable.

Conventional multicell wireless systems operate with out-of-cell interference treated as background noise; consequently, their performance faces two major limitations: 1)Signal processing is performed on a per-cell basis; and 2)Intercell interference detection is infeasible as intercell interference, although significantly above the noise level, is typically quite weak. In this thesis, we consider a multicell downlink scenario, where base-stations are equipped with multiple transmit antennas, the remote users are equipped with a single antenna, and multiple remote users are active simultaneously via spatial division multiplexing. We propose solutions for the above limitations by considering techniques for mitigating interference. The first part of the thesis proposes solutions for the first limitation. It considers the benefit of coordinating base-stations across multiple cells, where multiple base-stations may jointly optimize their respective beamformers to improve the overall system performance. It focuses on the design criteria of minimizing either the total weighted transmitted power or the maximum per-antenna power across the base-stations subject to signal-to-interference-and-noise-ratio (SINR) constraints at the remote users. The main contribution of this part is an efficient algorithm for finding the joint globally optimal beamformers across all base-stations. The proposed algorithm is based on a generalization of uplink-downlink duality to the multicell setting using the Lagrangian duality theory. An important feature is that it naturally leads to a distributed implementation in time-division duplex (TDD) systems. Simulation results suggest that coordinating the beamforming vectors alone already provides appreciable performance improvements as compared to the conventional per-cell optimized network. The second part of the thesis considers the transmission of both private and common messages for the sole purpose of intercell interference mitigation. It solves the issues of the second limitation mentioned above. It considers the benefit of designing decodable interference signals by allowing common-private message splitting at the transmitter and common message decoding by users in adjacent cells. It solves a network optimization problem of jointly determining the appropriate users in adjacent cells for rate splitting, the optimal beamforming vectors for both common and private messages, and the optimal common-private rates to minimize the total transmit power across the base-stations subject to service rate requirements for remote users. Observe that for fixed user selection and fixed common-private rate splitting, the optimization of beamforming vectors can be performed using a semidefinite programming approach. Further, this part of the thesis proposes a heuristic user-selection and rate splitting strategy to maximize the benefit of common message decoding. This part proposes a heuristic algorithm to characterize the improvement in the feasible rates with common-message decoding. Simulation results show that common message decoding can significantly improve both the total transmit power and the feasibility region for cell-edge users when base-stations are closely spaced from each other.

Conventional multicell wireless systems operate with out-of-cell interference treated as background noise; consequently, their performance faces two major limitations: 1)Signal processing is performed on a per-cell basis; and 2)Intercell interference detection is infeasible as intercell interference, although significantly above the noise level, is typically quite weak. In this thesis, we consider a multicell downlink scenario, where base-stations are equipped with multiple transmit antennas, the remote users are equipped with a single antenna, and multiple remote users are active simultaneously via spatial division multiplexing. We propose solutions for the above limitations by considering techniques for mitigating interference. The first part of the thesis proposes solutions for the first limitation. It considers the benefit of coordinating base-stations across multiple cells, where multiple base-stations may jointly optimize their respective beamformers to improve the overall system performance. It focuses on the design criteria of minimizing either the total weighted transmitted power or the maximum per-antenna power across the base-stations subject to signal-to-interference-and-noise-ratio (SINR) constraints at the remote users. The main contribution of this part is an efficient algorithm for finding the joint globally optimal beamformers across all base-stations. The proposed algorithm is based on a generalization of uplink-downlink duality to the multicell setting using the Lagrangian duality theory. An important feature is that it naturally leads to a distributed implementation in time-division duplex (TDD) systems. Simulation results suggest that coordinating the beamforming vectors alone already provides appreciable performance improvements as compared to the conventional per-cell optimized network. The second part of the thesis considers the transmission of both private and common messages for the sole purpose of intercell interference mitigation. It solves the issues of the second limitation mentioned above. It considers the benefit of designing decodable interference signals by allowing common-private message splitting at the transmitter and common message decoding by users in adjacent cells. It solves a network optimization problem of jointly determining the appropriate users in adjacent cells for rate splitting, the optimal beamforming vectors for both common and private messages, and the optimal common-private rates to minimize the total transmit power across the base-stations subject to service rate requirements for remote users. Observe that for fixed user selection and fixed common-private rate splitting, the optimization of beamforming vectors can be performed using a semidefinite programming approach. Further, this part of the thesis proposes a heuristic user-selection and rate splitting strategy to maximize the benefit of common message decoding. This part proposes a heuristic algorithm to characterize the improvement in the feasible rates with common-message decoding. Simulation results show that common message decoding can significantly improve both the total transmit power and the feasibility region for cell-edge users when base-stations are closely spaced from each other.

Orthogonal frequency division multiplexing (OFDM) is the major technique for next generation wireless communication system because of its high spectral efficiency. In addition, multiple-input multiple-output (MIMO) technique is usually used to further increase system capacity. There are two major coding schemes adopted in MIMO-OFDM systems, i.e. space-time block code (STBC) and space-frequency block code (SFBC). This thesis investigates the orthogonal-space-frequency block code OFDM (OSFBC-OFDM) system. In SFBC-OFDM systems, the channel frequency response is usually assumed to be the same for adjacent subcarriers. However, this assumption is not valid in frequency-selective fading environment. Therefore, the orthogonality of code structure is destroyed, leading to substantial increase in interference and significant decrease in system performance. This thesis proposes a receiver equalizer which adopts an interference cancellation (IC) mechanism to maximize the signal to interference plus noise ratio (SINR). Both the Lagrange multiplier method and eigenvalue method are adopted in the interference cancellation. Simulation experiments are conducted to verify the system performance and results demonstrate that the SINR performance is dramatically improved.

In this thesis study, an overview of MIMO radar is presented. The differences in radar cross section, channel and received signal models in different MIMO radar configurations are examined. The performance improvements that can be achieved by the use of waveform diversity in coherent MIMO radar and by the use of angular diversity in statistical MIMO radar are investigated. The optimal detector under Neyman-Pearson criterion for Coherent MIMO radar when the interfering signal is white Gaussian noise is developed. Detection performance of phased array radar, coherent MIMO radar and Statistical MIMO radar are compared through numerical simulations. A detector for MIMO radar that contains the space time codes explicitly is also examined.

In multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system which was used space frequency block coding (SFBC) method. It order to reduce the peak-to-average power ratio in several transmit antennas. We proposed two new architectures to simply the computational complexity on transmitter. According to the characteristics of SFBC structure which have M transmitter antennas. We can decomposed the interleaving subcarrier groups by used conversion vector to circular convolution with signal vector and shrink the inverse fast Fourier transform (IFFT) points. Therefore it can do the SFBC coding operation in time domain. By using combination of different cyclic shifts and phase rotations in U subcarrier groups can generate the P candidate signals. And it wouldn¡¦t increase the number of IFFT. The proposed transmitter architectures can improve the major drawback of high computational complexity in traditional selected mapping (SLM). The traditional SLM generate the P candidate signals needs MP IFFT units. Then in the condition of lose a little PAPR reduction performance, we can save the most of computational complexity.

Contemporary research in digital signal processing (DSP) is focused on issues of computational complexity, very high data rate and large quantum of data. Thus, the success in newer applications and areas hinge on handling these issues. Conventional ways to address these challenges are to develop newer structures like Multirate signal processing, Multiple Input Multiple Output(MIMO), bandpass sampling, compressed domain sensing etc. In the implementation domain, the approach is to look at floating point over fixed point representation and / or longer wordlength etc., related to number representations and computations. Of these, a simple approach is to look at number representation, perhaps with a simple integer. This automatically guarantees accuracy and zero quantization error as well as longer wordlength. Thus, it is necessary and interesting to explore viable DSP alternatives that can reduce complexity and yet match the required performance. The main aim of this work is to highlight the importance, use and analysis of integer sequences. Firstly, the thesis explores the use of integer sequences as windowing functions. The results of these investigations show that integer sequences and their convolution, indeed, outperform many of the classical real valued window functions in terms of mainlobe width, sidelobe attenuation etc. Secondly, the thesis proposes techniques to approximate discrete Gaussian distribution using integer sequences. The key idea is to convolve symmetrized integer sequences and examine the resulting profiles. These profiles are found to approximate discrete Gaussian distribution with a mean square error of the order of 10−8 or less. While looking at integer sequences to approximate discrete Gaussian, Fibonacci sequence was found to exhibit some interesting properties. The third part of the thesis proves certain fascinating optimal probabilistic limit properties (mean and variance) of Fibonacci sequence. The thesis also provides complete generalization of these properties to probability distributions generated by second order linear recurrence relation with integer coefficients and any kth order linear recurrence relation with unit coefficients. In addition to the above, the thesis also throws light on possible architectural implications of using integer sequences in DSP applications and ideas for further exploration.

The objectives of this work were to investigate, design and implement Multiple-Input Multiple-Output (MIMO) antenna arrays for mobile phones. Several MIMO antennas were developed and tested over various wireless-communication frequency bands. The radiation performance and channel capacity of these antennas were computed and measured: the results are discussed in the context of the frequency bands of interest. A comprehensive study of MIMO antenna configurations such as 2 × 1, 3 × 1, 2 × 2 and 3 × 3, using polarisation diversity as proposed for future mobile handsets, is presented. The channel capacity is investigated and discussed, as applying to Rayleigh fading channels with different power spectrum distributions with respect to azimuth and zenith angles. The channel capacity of 2 × 2 and 3 × 3 MIMO systems using spatial polarisation diversity is presented for different antenna designs. The presented results show that the maximum channel capacity for an antenna contained within a small volume can be reached with careful selection of the orthogonal spatial fields. The results are also compared against planar array MIMO antenna systems, in which the antenna size considered was much larger. A 50% antenna size reduction method is explored by applying magnetic wall concept on the symmetry reference of the antenna structure. Using this method, a triple dual-band inverted-F antenna system is presented and considered for MIMO application. Means of achieving minimum coupling between the three antennas are investigated over the 2.45 GHz and 5.2 GHz bands. A new 2 2 MIMO dual-band balanced antenna handset, intended to minimise the coupling with the handset and human body was proposed, developed and tested. The antenna coupling with the handset and human hand is reported in terms the radiation performance and the available channel capacity. In addition, a dual-polarisation dipole antenna is proposed, intended for use as one of three collocated orthogonal antennas in a polarisation-diversity MIMO communication system. The antenna actually consists of two overlaid electric and magnetic dipoles, such that their radiation patterns are nominally identical but they are cross-polarised and hence only interact minimally.

Multi-User Multi-Input Multi-Output (MU-MIMO) systems are considered to be the sustainable technologies of the current and future of the upcoming wireless and mobile networks generations. The perspectives of these technologies under several scenarios is the focus of the present thesis. The initial system model covers the MU-MIMO, especially in the massive form that is considered to be the promising ideas and pillars of the 5G network. It is observed that the optimal number of users should be served in the time-frequency resource even though the maximum limitation of the MU-MIMO is governed by the total receiving antennas (K) is less than or equal to the base station antennas (M). The system capacity of the massive MIMO (mMIMO) under perfect channel state information (CSI) of uncorrelated channel is investigated and studied. Two types of precoders were applied, one is directly based on channel inversion, and the other uses the Eigen decomposition that is derived subject to the signal to a leakage maximization problem. The two precoders show a degree of equivalency under certain assumptions for the number of antennas at the user end. The convex optimization of multi-antenna networks to achieve the design model of optimum beamformer (BF) based on the uniform linear array (ULA) is studied. The ULA is selected for its simplicity to analyse many scenarios and its importance to match the future network applied millimetre wave (mmWave) spectrum. The maximum beams generated by the ULA are explored in terms of several physical system parameters. The duality between the MU-MIMO and ULA and how they are related based on beamformer operation are detailed and discussed. Finally, two approaches for overloaded systems are presented when the availability of massive array that is not guaranteed due to physical restrictions since the existence of a large number of devices will result in breaking the dimension rule (i.e., K ≤ M). As a solution, a low complexity users selection algorithm is proposed. The channel considered is uncorrelated with full and perfect knowledge at the BS. In particular, these two channel conditions may not be available in all scenarios. The CSI may be imperfect, and even the instantaneous form does not exist. A hybrid precoder between the mixed CSI (includes imperfect and statistical) and rate splitting approach is proposed to deal with an overloaded system under a low number of BS antennas. / Ministry of Higher Education and Scientific Research of Iraq

<p>The Multiple-input and Multiple-output (MIMO) channel model is very useful for the presentation of a wide range of wireless communication systems. This thesis addresses the joint design of a precoder and a receiver for a MIMO channel model, in a scenario in which perfect channel state information (CSI) is available at both ends. We develop a novel framework for the transmitting-receiving procedure. Under the proposed framework, the receiver decomposes the channel matrix by using a block QR decomposition, where Q is a unitary matrix and R is a block upper triangular matrix. The optimal maximum likelihood (ML) detection process is employed within each diagonal block of R. Then, the detected block of symbols is substituted and subtracted sequentially according to the block QR decomposition based successive cancellation. On the transmitting end, the expression of probability of error based on ML detection is chosen as the design criterion to formulate the precoder design problem. This thesis presents a design of MIMO transceivers in the particular case of having 4 transmitting and 4 receiving antennas with full CSI knowledge on both sides. In addition, a closed-form expression for the optimal precoder matrix is obtained for channels satisfying certain conditions. For other channels not satisfying the specific condition, a numerical method is applied to obtain the optimal precoder matrix.</p> / Master of Applied Science (MASc)