Ultra wide band (UWB) is an extremely promising wireless technology for researchers and industrials. One of the most interesting is its high data rate and fading robustness due to selective frequency fading. However, beside such advantages, UWB system performance is highly affected by existing narrowband interference (NBI), undesired UWB signals and tone/multi-tone noises. For this reason, research about NBI cancellation is still a challenge to improve the system performance vs. receiver complexity, power consumption, linearity, etc. In this work, the two major receiver sections, i.e., analog (radiofrequency or RF) and digital (digital signal processing or DSP), were considered and new techniques proposed to reduce circuit complexity and power consumption, while improving signal parameters. In the RF section, different multiband UWB low-noise amplifier key design parameters were investigated like circuit configuration, input matching and desired/undesired frequency band filtering, highlighting the most suitable filtering package for efficient UWB NBI cancellation. In the DSP section, due to pulse transmitter signals, different issues like modulation type and level, pulse variety, shape and color noise/tone noise assumptions, were addressed for efficient NBI cancelation. A comparison was performed in terms of bit-error rate, signal-to-interference ratio, signal-to-noise ratio, and channel capacity to highlight the most suitable parameters for efficient DSP design. The optimum number of filters that allows the filter bandwidth to be reduced by following the required low sampling rate and thus improving the system bit error rate was also investigated.

In the development of digital communication and information theory, every channel decoding rule has resulted in a revolution at the time when it was invented. In the area of information theory, early channel coding theorems were established mainly by maximum likelihood decoding, while the arrival of typical sequence decoding signaled the era of multi-user information theory, in which achievability proof became simple and intuitive. Practical channel code design, on the other hand, was based on minimum distance decoding at the early stage. The invention of belief propagation decoding with soft input and soft output, leading to the birth of turbo codes and low-density-parity check (LDPC) codes which are indispensable coding techniques in current communication systems, changed the whole research area so dramatically that people started to use the term "modern coding theory'' to refer to the research based on this decoding rule. In this thesis, we propose a new decoding rule, dubbed jar decoding, which would be expected to bring some new thoughts to both the code performance analysis and the code design. Given any channel with input alphabet X and output alphabet Y, jar decoding rule can be simply expressed as follows: upon receiving the channel output y^n ∈ Y^n, the decoder first forms a set (called a jar) of sequences x^n ∈ X^n considered to be close to y^n and pick any codeword (if any) inside this jar as the decoding output. The way how the decoder forms the jar is defined independently with the actual channel code and even the channel statistics in certain cases. Under this jar decoding, various coding theorems are proved in this thesis. First of all, focusing on the word error probability, jar decoding is shown to be near optimal by the achievabilities proved via jar decoding and the converses proved via a proof technique, dubbed the outer mirror image of jar, which is also quite related to jar decoding. Then a Taylor-type expansion of optimal channel coding rate with finite block length is discovered by combining those achievability and converse theorems, and it is demonstrated that jar decoding is optimal up to the second order in this Taylor-type expansion. Flexibility of jar decoding is then illustrated by proving LDPC coding theorems via jar decoding, where the bit error probability is concerned. And finally, we consider a coding scenario, called interactive encoding and decoding, and show that jar decoding can be also used to prove coding theorems and guide the code design in the scenario of two-way communication.

The interest in wireless communications among consumers has exploded since the introduction of the "3G" cell phone standards. One reason for their success is the increasingly higher data rates achievable through the networks. A further increase in data rates is possible through the use of multiple antennas at either or both sides of the wireless links. Precoding and receive filtering using matrices obtained from a singular value decomposition (SVD) of the channel matrix is a transmission strategy for achieving the channel capacity of a deterministic narrowband multiple-input multiple-output (MIMO) communications channel. When signalling over wideband channels using orthogonal frequency-division multiplexing (OFDM), an SVD must be performed for every sub-carrier. As the number of sub-carriers of this traditional approach grow large, so does the computational load. It is therefore interesting to study alternate means for obtaining the decomposition. A wideband MIMO channel can be modeled as a matrix filter with a finite impulse response, represented by a polynomial matrix. This thesis is concerned with investigating algorithms which decompose the polynomial channel matrix directly. The resulting decomposition factors can then be used to obtain the sub-carrier based precoding and receive filtering matrices. Existing approximative polynomial matrix QR and singular value decomposition algorithms were modified, and studied in terms of decomposition quality and computational complexity. The decomposition algorithms were shown to give decompositions of good quality, but if the goal is to obtain precoding and receive filtering matrices, the computational load is prohibitive for channels with long impulse responses. Two algorithms for performing exact rational decompositions (QRD/SVD) of polynomial matrices were proposed and analyzed. Although they for simple cases resulted in excellent decompositions, issues with numerical stability of a spectral factorization step renders the algorithms in their current form purposeless. For a MIMO channel with exponentially decaying power-delay profile, the sum rates achieved by employing the filters given from the approximative polynomial SVD algorithm were compared to the channel capacity. It was shown that if the symbol streams were decoded independently, as done in the traditional approach, the sum rates were sensitive to errors in the decomposition. A receiver with a spatially joint detector achieved sum rates close to the channel capacity, but with such a receiver the low complexity detector set-up of the traditional approach is lost. Summarizing, this thesis has shown that a wideband MIMO channel can be diagonalized in space and frequency using OFDM in conjunction with an approximative polynomial SVD algorithm. In order to reach sum rates close to the capacity of a simple channel, the computational load becomes restraining compared to the traditional approach, for channels with long impulse responses.

In the first part, the topic of average channel capacity for Orthogonal Frequency Division Multiplexing (OFDM) under Rayleigh, Rician, Nakagami-m, Hoyt, Weibull and Lognormal fading is addressed. With the assumption that channel state information is known, we deal with a lower bound for the capacity and find closed computable forms for Rician fading without diversity and with Maximum Ratio Combining diversity at the receiver. Approximate expressions are also provided for the capacity lower bound in the case of high Signal to Noise Ratio. This thesis presents two simplified Maximum A Posteriori (MAP) channel estimators to be used in OFDM systems under frequency selective slowly varying Rayleigh fading. Both estimators use parametric models, where the first model assumes exponential frequency domain correlation while the second model is based on the assumption of exponential power delay profile. Expressions for the mean square error of estimations are derived and the relation between the correlation of subchannel taps and error variance is investigated. Dependencies of the proposed estimators&rsquo / performances on the model parameter and noise variance estimation errors are analyzed. We also provide approximations on the estimators&rsquo / algorithms in order to make the estimators practical. Finally, we investigate SER performance of the simplified MAP estimator based on exponential power delay profile assumption used for OFDM systems with QPSK modulation. The results indicate that the proposed estimator performance is always better than that of the ML estimator, and as the subchannel correlation increases the performance comes closer to that of perfectly estimated channel case.

The continuous advancement in wireless communications technology demands new approaches to improving the capacity of existing radio links. The high data throughput required can be achieved by the complete utilization of space, time and polarization diversities inherent in any propagation environment. Among the different propagation scenarios, the indoor channels represent a particularly challenging problem given the number and complexity of interactions between the transmitted signal and the environment. This dissertation explores the interrelation between propagation physics and space-time-polarization diversity based on a novel high resolution channel sounding and reconstruction technique. First, a method to reconstruct the indoor complex channel response based on a limited set of samples and the elimination of the interference using deconvolution techniques is presented. Then, the results for the joint angle-of-arrival, delay characterization and depolarization of electromagnetic waves are presented. Finally, a novel approach to using depolarized multipath signals to boost the receiver signal-to-noise performance is presented. The current study shows that full utilization of the diversities of channel novel wireless systems can be proposed with significant improvement in capacity.

The continuous advancement in wireless communications technology demands new approaches to improving the capacity of existing radio links. The high data throughput required can be achieved by the complete utilization of space, time and polarization diversities inherent in any propagation environment. Among the different propagation scenarios, the indoor channels represent a particularly challenging problem given the number and complexity of interactions between the transmitted signal and the environment. This dissertation explores the interrelation between propagation physics and space-time-polarization diversity based on a novel high resolution channel sounding and reconstruction technique. First, a method to reconstruct the indoor complex channel response based on a limited set of samples and the elimination of the interference using deconvolution techniques is presented. Then, the results for the joint angle-of-arrival, delay characterization and depolarization of electromagnetic waves are presented. Finally, a novel approach to using depolarized multipath signals to boost the receiver signal-to-noise performance is presented. The current study shows that full utilization of the diversities of channel novel wireless systems can be proposed with significant improvement in capacity.

This research investigates techniques for iterative channel estimation to maximize channel capacity and communication security. The contributions of this dissertation are as follows: i) An accurate, low-complexity approach to pilot-assisted fast-fading channel estimation for single-carrier modulation with a turbo equalizer and a decoder is proposed. The channel is estimated using a Kalman filter (KF) followed by a zero-phase filter (ZPF) as a smoother. The combination of the ZPF with the KF of the channel estimator makes it possible to reduce the estimation error to near the Wiener bound. ii) A new semi-blind channel estimation technique is introduced for multiple-input-multiple-output channels. Once the channel is estimated using a few pilots, a low-order KF is employed to progressively predict the channel gains for the upcoming blocks. iii) The capacity of radio channels is investigated when iterative channel estimation, data detection, and decoding are employed. By taking the uncertainty in decoded data bits into account, the channel Linear Minimum Mean Square Error (LMMSE) estimator of an iterative receiver with a given pilot ratio is obtained. The derived error value is then used to derive a bound on capacity. It is shown that in slow fading channels, iterative processing provides only a marginal advantage over non-iterative approach to channel estimation. Knowing the capacity gain from iterative processing versus purely pilot-based channel estimation helps a designer to compare the performance of an iterative receiver against a non-iterative one and select the best balance between performance and cost. iv) A Radio channel is characterized by random parameters which can be used to generate shared secret keys by the communicating parties when the channel is estimated. This research studies upper bounds on the rate of the secret keys extractable from iteratively estimated channels. Various realistic scenarios are considered where the transmission is half-duplex and/or the channel is sampled under the Nyquist rate. The effect of channel sampling interval, fading rate and noise on the key rate is demonstrated. The results of this research can be beneficial for the design and analysis of reliable and secure mobile wireless systems. / Graduate / 0544

In the development of digital communication and information theory, every channel decoding rule has resulted in a revolution at the time when it was invented. In the area of information theory, early channel coding theorems were established mainly by maximum likelihood decoding, while the arrival of typical sequence decoding signaled the era of multi-user information theory, in which achievability proof became simple and intuitive. Practical channel code design, on the other hand, was based on minimum distance decoding at the early stage. The invention of belief propagation decoding with soft input and soft output, leading to the birth of turbo codes and low-density-parity check (LDPC) codes which are indispensable coding techniques in current communication systems, changed the whole research area so dramatically that people started to use the term "modern coding theory'' to refer to the research based on this decoding rule. In this thesis, we propose a new decoding rule, dubbed jar decoding, which would be expected to bring some new thoughts to both the code performance analysis and the code design. Given any channel with input alphabet X and output alphabet Y, jar decoding rule can be simply expressed as follows: upon receiving the channel output y^n ∈ Y^n, the decoder first forms a set (called a jar) of sequences x^n ∈ X^n considered to be close to y^n and pick any codeword (if any) inside this jar as the decoding output. The way how the decoder forms the jar is defined independently with the actual channel code and even the channel statistics in certain cases. Under this jar decoding, various coding theorems are proved in this thesis. First of all, focusing on the word error probability, jar decoding is shown to be near optimal by the achievabilities proved via jar decoding and the converses proved via a proof technique, dubbed the outer mirror image of jar, which is also quite related to jar decoding. Then a Taylor-type expansion of optimal channel coding rate with finite block length is discovered by combining those achievability and converse theorems, and it is demonstrated that jar decoding is optimal up to the second order in this Taylor-type expansion. Flexibility of jar decoding is then illustrated by proving LDPC coding theorems via jar decoding, where the bit error probability is concerned. And finally, we consider a coding scenario, called interactive encoding and decoding, and show that jar decoding can be also used to prove coding theorems and guide the code design in the scenario of two-way communication.

In this thesis we study the information transmission through Gaussian quantum channels. Gaussian quantum channels model physical communication links, such as free space communication or optical fibers and therefore, may be considered as the most relevant quantum channels. One of the central characteristics of any communication channel is its capacity. In this work we are interested in the classical capacity, which is the maximal number of bits that can be reliably transmitted per channel use. An important lower bound on the classical capacity is given by the Gaussian capacity, which is the maximal transmission rate with the restriction that only Gaussian encodings are allowed: input messages are encoded in so-called Gaussian states for which the mean field amplitudes are Gaussian distributed.<p><p>We focus in this work mainly on the Gaussian capacity for the following reasons. First, Gaussian encodings are easily accessible experimentally. Second, the difficulty of studying the classical capacity, which arises due to an optimization problem in an infinite dimensional Hilbert space, is greatly reduced when considering only Gaussian input encodings. Third, the Gaussian capacity is conjectured to coincide with the classical capacity, even though this longstanding conjecture is unsolved until today.<p><p>We start with the investigation of the capacities of the single-mode Gaussian channel. We show that the most general case can be reduced to a simple, fiducial Gaussian channel which depends only on three parameters: its transmissivity (or gain), the added noise variance and the squeezing of the noise. Above a certain input energy threshold, the optimal input variances are given by a quantum water-filling solution, which implies that the optimal modulated output state is a thermal state. This is a quantum extension (or generalization) of the well-known classical water-filling solution for parallel Gaussian channels. Below the energy threshold the solution is given by a transcendental equation and only the less noisy quadrature is modulated. We characterize in detail the dependence of the Gaussian capacity on its channel parameters. In particular, we show that the Gaussian capacity is a non-monotonous function of the noise squeezing and analytically specify the regions where it exhibits one maximum, a maximum and a minimum, a saddle point or no extrema. <p><p>Then, we investigate the case of n-mode channels with noise correlations (i.e. memory), where we focus in particular on the classical additive noise channel. We consider memory models for which the noise correlations can be unraveled by a passive symplectic transformation. Therefore, we can simplify the problem to the study of the Gaussian capacity in an uncorrelated basis, which corresponds to the Gaussian capacity of n single-mode channels with a common input energy constraint. Above an input energy threshold the solutions is given by a global quantum water-filling solution, which implies that all modulated single-mode output states are thermal states with the same temperature. Below the threshold the channels are divided into three sets: i) those that are excluded from information transmission, ii) those for which only the less noisy quadrature is modulated, and iii) those for which the quantum water-filling solution is satisfied. As an example we consider a Gauss-Markov correlated noise, which in the uncorrelated basis corresponds to a collection of single-mode classical additive noise channels. When rotating the collection of optimal single-mode input states back to the original, correlated basis the optimal multi-mode input state becomes a highly entangled state. We then compare the performance of the optimal input state with a simple coherent state encoding and conclude that one gains up to 10% by using the optimal encoding.<p><p>Since the preparation of the optimal input state may be very challenging we consider sub-optimal Gaussian-matrix product states (GMPS) as input states as well. GMPS have a known experimental setup and, though being heavily entangled, can be generated sequentially. We demonstrate that for the Markovian correlated noise as well as for a non-Markovian noise model in a wide range of channel parameters, a nearest-neighbor correlated GMPS achieves more than 99.9% of the Gaussian capacity. At last, we introduce a new noise model for which the GMPS is the exact optimal input state. Since GMPS are known to be ground states of quadratic Hamiltonians this suggests a starting point to develop links between optimization problems of quantum communication and many body physics. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Ultra wide band (UWB) is an extremely promising wireless technology for researchers and industrials. One of the most interesting is its high data rate and fading robustness due to selective frequency fading. However, beside such advantages, UWB system performance is highly affected by existing narrowband interference (NBI), undesired UWB signals and tone/multi-tone noises. For this reason, research about NBI cancellation is still a challenge to improve the system performance vs. receiver complexity, power consumption, linearity, etc. In this work, the two major receiver sections, i.e., analog (radiofrequency or RF) and digital (digital signal processing or DSP), were considered and new techniques proposed to reduce circuit complexity and power consumption, while improving signal parameters. In the RF section, different multiband UWB low-noise amplifier key design parameters were investigated like circuit configuration, input matching and desired/undesired frequency band filtering, highlighting the most suitable filtering package for efficient UWB NBI cancellation. In the DSP section, due to pulse transmitter signals, different issues like modulation type and level, pulse variety, shape and color noise/tone noise assumptions, were addressed for efficient NBI cancelation. A comparison was performed in terms of bit-error rate, signal-to-interference ratio, signal-to-noise ratio, and channel capacity to highlight the most suitable parameters for efficient DSP design. The optimum number of filters that allows the filter bandwidth to be reduced by following the required low sampling rate and thus improving the system bit error rate was also investigated.