Over the past few years there has been an extensive growth in data traffic consumption devices. Billions of mobile data devices are connected to the global wireless network. Customers demand revived services and up-to-date developed applications, like real-time video and games. These applications require reliable and high data rate wireless communication with high throughput network. One way to meet these requirements is by increasing the number of transmit and/or receive antennas of the wireless communication systems. Massive multiple-input multiple-output (MIMO) has emerged as a promising candidate technology for the next generation (5G) wireless communication. Massive MIMO increases the spatial multiplexing gain and the data rate by adding an excessive number of antennas to the base station (BS) terminals of wireless communication systems. However, building efficient algorithms able to decode a coherently or non-coherently large flow of transmitted signal with low complexity is a big challenge in massive MIMO. In this dissertation, we propose novel approaches to achieve optimal performance for joint channel estimation and signal detection for massive MIMO systems. The dissertation consists of three parts depending on the number of users at the receiver side. In the first part, we introduce a probabilistic approach to solve the problem of coherent signal detection using the optimized Markov Chain Monte Carlo (MCMC) technique. Two factors contribute to the speed of finding the optimal solution by the MCMC detector: The probability of encountering the optimal solution when the Markov chain converges to the stationary distribution, and the mixing time of the MCMC detector. First, we compute the optimal value of the “temperature'' parameter such that the MC encounters the optimal solution in a polynomially small probability. Second, we study the mixing time of the underlying Markov chain of the proposed MCMC detector. We assume the channel state information is known in the first part of the dissertation; in the second part we consider non-coherent signal detection. We develop and design an optimal joint channel estimation and signal detection algorithms for massive (single-input multiple-output) SIMO wireless systems. We propose exact non-coherent data detection algorithms in the sense of generalized likelihood ratio test (GLRT). In addition to their optimality, these proposed tree based algorithms perform low expected complexity and for general modulus constellations. More specifically, despite the large number of the unknown channel coefficients for massive SIMO systems, we show that the expected computational complexity of these algorithms is linear in the number of receive antennas (N) and polynomial in channel coherence time (T). We prove that as $N \rightarrow \infty$, the number of tested hypotheses for each coherent block equals $T$ times the cardinality of the modulus constellation. Simulation results show that the optimal non-coherent data detection algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity. In the part three, we consider massive MIMO uplink wireless systems with time-division duplex (TDD) operation. We propose an optimal algorithm in terms of GLRT to solve the problem of joint channel estimation and data detection for massive MIMO systems. We show that the expected complexity of our algorithm grows polynomially in the channel coherence time (T). The proposed algorithm is novel in two terms: First, the transmitted signal can be chosen from any modulus constellation, constant and non-constant. Second, the algorithm decodes the received noisy signal, which is transmitted a from multiple-antenna array, offering exact solution with polynomial complexity in the coherent block interval. Simulation results demonstrate significant performance gains of our approach compared with suboptimal non-coherent detection schemes. To the best of our knowledge, this is the first algorithm which efficiently achieves GLRT-optimal non-coherent detections for massive MIMO systems with general constellations.

<p>Prediction of the rapidly fading envelope of a mobile radio channel enables a number of capacity improving techniques like fast resource allocation and fast link adaptation. This thesis deals with linear prediction of the complex impulse response of a channel and unbiased quadratic prediction of the power. The design and performance of these predictors depend heavily on the correlation properties of the channel. Models for a channelwhere the multipath is caused by clusters of scatterers are studied. The correlation for the contribution from a cluster can be approximated as a damped complex sinusoid. A suitable model for the dynamics of the channel is an ARMA-process. This motivates the use of linear predictors.</p><p>A limiting factor in the prediction are the estimation errors on the observed channels. This estimation error, caused by measurement noise and time variation, is analyzed for a block based least squares algorithm which operates on a Jakes channel model. Efficient noise reduction on the estimated channel impulse responses can be obtained with Wienersmoothers that are based on simple models for the dynamics of the channel combined with estimates of the variance of the estimation error.</p><p>Power prediction that is based on the squared magnitude of linear prediction of the taps will be biased. Hence, a bias compensated power predictor is proposed and the optimal prediction coefficients are derived for the Rayleigh fading channel. The corresponding probability density functions for the predicted power are also derived. A performance evaluation of the prediction algorithm is carried out on measured broadband mobile radio channels. The performance is highly dependent on the variance of the estimation error and the dynamics of the individual taps.</p>

This thesis examines various signal processing techniques that are required for establishing efficient (near optimal) communications in multiuser multiple-input multiple-output (MIMO) environments. The central part of this thesis is dedicated to acquisition of information about the MIMO channel state - at both the receiver and the transmitter. This information is required to organize a communication set up which utilizes all the available channel resources. Realistic channel model, i.e., the spatial channel model (SCM), has been used in this study, together with modern long-term evolution (LTE) standard. The work consists of three major themes: (a) estimation of the channel at the receiver, also known as tracking; (b) quantization of the channel information and its feedback from receiver to the transmitter (feedback quantization); and (c) reconstruction of the channel knowledge at the transmitter, and its use for data precoding during communication transmission. / Communications

Prediction of the rapidly fading envelope of a mobile radio channel enables a number of capacity improving techniques like fast resource allocation and fast link adaptation. This thesis deals with linear prediction of the complex impulse response of a channel and unbiased quadratic prediction of the power. The design and performance of these predictors depend heavily on the correlation properties of the channel. Models for a channelwhere the multipath is caused by clusters of scatterers are studied. The correlation for the contribution from a cluster can be approximated as a damped complex sinusoid. A suitable model for the dynamics of the channel is an ARMA-process. This motivates the use of linear predictors. A limiting factor in the prediction are the estimation errors on the observed channels. This estimation error, caused by measurement noise and time variation, is analyzed for a block based least squares algorithm which operates on a Jakes channel model. Efficient noise reduction on the estimated channel impulse responses can be obtained with Wienersmoothers that are based on simple models for the dynamics of the channel combined with estimates of the variance of the estimation error. Power prediction that is based on the squared magnitude of linear prediction of the taps will be biased. Hence, a bias compensated power predictor is proposed and the optimal prediction coefficients are derived for the Rayleigh fading channel. The corresponding probability density functions for the predicted power are also derived. A performance evaluation of the prediction algorithm is carried out on measured broadband mobile radio channels. The performance is highly dependent on the variance of the estimation error and the dynamics of the individual taps.

In this thesis, we proposed a precoding scheme for semi-blind channel estimation in amplify-and-forward (AF) multipath two-way relay networks (TWRN), where two terminals exchange their information through multi-relays. The precoding scheme, which diminishes computational complexity of semi-blind channel estimator, is used to distinguish received signal at both terminals from multi-relays. By applying a non-redundant linear precoding scheme at multi-relays, we proposed a semi-blind channel estimation to estimate the channel impulse response (CIR) of direct link and the cascaded source-relay-terminal links. Firstly, semi-blind channel estimation adopts least-square (LS) estimation to find the CIR of direct link between both terminals using a smaller number of training symbols. Secondly, the CIR of the cascaded source-relay-terminal links are obtained through second-order statistics (SOS) of received signals at both terminals. Consequently, the proposed scheme can effectively reduce the computational complexity and enhance the spectral efficiency in overall system. Simulation results corroborate the effectiveness of the proposed scheme.

In this thesis, implementation details of a joint channel estimator and parallelized decoder structure on an FPGA-based platform is considered. Turbo decoders are used for the decoding process in this structure. However, turbo decoders introduce large decoding latencies since they operate in an iterative manner. To overcome that problem, parallelization is applied to the turbo codes and the resulting parallel decodable turbo code (PDTC) structure is employed for coding. The performance of a PDTC decoder and parameters affecting its performance is given on an additive white Gaussian noise (AWGN) channel. These results are compared with the results of a parallel study which employs a different architecture in implementing the PDTC decoder. In the fading channel case, a pilot symbol assisted estimation method is employed for the channel estimation process. In this method, the channel coefficients are estimated by a 2-way LMS (least mean-squares) algorithm. The difficulties in the implementation of this joint structure in a fixed-point arithmetic and the solutions to overcome these difficulties are described in details. The proposed joint structure is tested with varying design parameters over a Rayleigh fading channel. The overall decoding latencies and allowed data rates are calculated after obtaining a reasonable performance from the design.

This thesis investigates the semi-blind channel estimation in uplink (UL) of Orthogonal Frequency Division Multiple Access (OFDMA) systems based on subspace decomposition. We exploit the orthogonality between signal subspace and noise subspace induced by virtual carriers (VCs) and cyclic prefix (CP) and the property of that the exclusive sub-carriers set is assigned to each user to estimate and identify the channels for each user individually. In OFDMA systems, when some users don¡¦t communicate with base station, the sub-carriers of non-active user provide extra redundancy for channel estimate to enhance the accuracy of channel estimation. Furthermore, the sufficient channel identifiability condition is developed. Furthermore, a novel scheme, called as virtual carriers recovery (VCR) scheme, is proposed to improve the performance of the subspace-based channel estimation method. It suppresses the noise interference by recovering the VCs to zeros at receiver. The simulation results illustrate that the enhancement of VCR scheme is particularly apparent for the partially loaded OFDMA system at low signal to noise ratio (SNR). In addition, the VCR scheme increases the convergence rate of the subspace-base semi-blind channel estimation.

Orthogonal Frequency Division Multiplexing (OFDM) techniques have been used in many wireless communication systems to improve the system capacity and achieve high data-rate. It possesses good spectral efficiency and robustness against interferences. The OFDM system has been adopted in many communication standards, such as the 802.11a/g standards for the high-speed WLAN, HIPERLAN2, and IEEE 802.16 standard, and meanwhile, it is also employed in the European DAB and DVB systems. To avoid the inter-block interference (IBI), usually, in the transmitter of OFDM systems the redundancy with sufficient length is introduced, it allows us to overcome the IBI problem, due to highly dispersive channel. Many redundancy insertion methods have been proposed in the literatures, there are cyclic prefix (CP), zero padding (ZP) and the pseudorandom postfix (PRP). Under such system we have still to know the correct channel state information for equalizing the noisy block signal. Especially, in time-varying channel, the incorrect channel state information may introduce serious inter-symbol interference (ISI), if the channel estimation could not perform correctly. In this thesis, the PRP-OFDM system is considered. According to the PRP-OFDM scheme, the redundancy with pseudorandom postfix (PRP) approach is employed to make semi-blind channel estimation with order-one statistics of the received signal. But these statistic characteristics may not be available under time-varying channel. Hence, in this thesis, we propose a modified PRP-OFDM system with assistant training sequences, which is equipped with minimum mean-square-error equalizer and utilize Kalman filter algorithm to implement time-varying channel estimation. To do so, we first model time-varying channel estimation problem with a dynamic system, and adopt the Kalman filter algorithm to estimate the true channel coefficients. Unfortunately, since most parameters in dynamic system are random and could not to be known in advance. We need to apply effective estimation schemes to estimate the statistics of true parameters for implementing the Kalman filter algorithm. When the channel state information is known, MMSE equalizer follows to suppress the inter-symbol interference (ISI). Moreover, after making decision the binary data can be used to re-modulate PRP-OFDM symbol and to be re-used in Kalman filter to obtain more accurate CSI to improve the effectiveness of the equalizer. Via computer simulations, we verify that desired performance in terms of bit error rate (BER), can be achieved compared with the CP-OFDM systems.

With the increasing advancements in the digital technology, future wireless systems are promising to support higher data rates, higher mobile speeds, and wider coverage areas, among other features. While further technological developments allow systems to support higher computational complexity, lower power consumption, and employ larger memory units, other resources remain limited. One such resource, which is of great importance to wireless systems, is the available spectrum for radio communications. To be able to support high data rate wireless applications, there is a need for larger bandwidths in the spectrum. Since the spectrum cannot be expanded, studies have been concerned with fully utilizing the available spectrum. One approach to achieve this goal is to reuse the available spectrum through space, time, frequency, and code multiplexing techniques. Another approach is to optimize the transceiver design as to achieve the highest throughput over the used spectrum. From the physical layer perspective, there is a need for a highly flexible and efficient modulation technique to carry the communication signal. A multicarrier modulation technique known as orthogonal frequency division multiplexing (OFDM) is one example of such a technique. OFDM has been used in a number of current wireless standards such as wireless fidelity (WiFi) and worldwide interoperability for microwave access (WiMAX) standards by the Institute of Electrical and Electronics Engineers (IEEE), and has been proposed for future 4G technologies such as the long term evolution (LTE) and LTE-advanced standards by the 3rd Generation Partnership Project (3GPP), and the wireless world initiative new radio (WINNER) standard by the Information society technologies (IST). This is due to OFDM’s high spectral efficiency, resistance to narrow band interference, support for high data rates, adaptivity, and scalability. In this dissertation, OFDM and multiuser OFDM , also known as orthogonal frequency division multiple access (OFDMA), techniques are investigated as a candidate for advanced wireless systems. Features and requirements of future applications are discussed in detail, and OFDM’s ability to satisfy these requirements is investigated. We identify a number of challenges that when addressed can improve the performance and throughput of OFDM-based systems. The challenges are investigated over three stages. In the first stage, minimizing, or avoiding, the interference between multiple OFDMA users as well as adjacent systems is addressed. An efficient algorithm for OFDMA uplink synchronization that maintains the orthogonality between multiple users is proposed. For adjacent channel interference, a new spectrum shaping method is proposed that can reduce the out-of-band radiation of OFDM signals. Both methods increase the utilization of available spectrum and reduce interference between different users. In the second stage, the goal is to maximize the system throughput for a given available bandwidth. The OFDM system performance is considered under practical channel conditions, and the corresponding bit error rate (BER) expressions are derived. Based on these results, the optimum pilot insertion rate is investigated. In addition, a new pilot pattern that improves the system ability to estimate and equalize various radio frequency (RF) impairments is proposed. In the last stage, acquiring reliable measurements regarding the received signal is addressed. Error vector magnitude (EVM) is a common performance metric that is being used in many of today’s standards and measurement devices. Inferring the signal-to-noise ratio (SNR) from EVM measurements has been investigated for either high SNR values or data-aided systems. We show that using current methods does not yield reliable estimates of the SNR under other conditions. Thus, we consider the relation between EVM and SNR for nondata-aided systems. We provide expressions that allow for accurate SNR estimation under various practical channel conditions.

In many modern wireless and wireline communication networks, the interference power from other communication and non-communication devices is increasingly dominating the background noise power, leading to interference limited communication systems. Conventional communication systems have been designed under the assumption that noise in the system can be modeled as additive white Gaussian noise (AWGN). While appropriate for thermal noise, the AWGN model does not always capture the interference statistics in modern communication systems. Interference from uncoordinated users and sources is particularly harmful to communication performance because it cannot be mitigated by current interference management techniques. Based on previous statistical-physical models for uncoordinated wireless interference, this dissertation derives similar models for uncoordinated interference in PLC networks. The dissertation then extends these models for wireless and powerline interference to include temporal dependence among amplitude samples. The extensions are validated with measured data. The rest of this dissertation utilizes the proposed models to design receivers in interference limited environments. Prior designs generally adopt suboptimal approaches and often ignore the problem of channel estimation which limits their applicability in practical systems. This dissertation uses the graphical model representation of the OFDM system to propose low-complexity message passing OFDM receivers that leverage recent results in soft-input soft-output decoding, approximate message passing, and sparse signal recovery for joint channel/interference estimation and data decoding. The resulting receivers provide huge improvements in communication performance (more than 10dB) over the conventional receivers at a comparable computational complexity. Finally, this dissertation addresses the design of robust receivers that can be deployed in rapidly varying environments where the interference statistics are constantly changing. / text