In this thesis, the problem of data transmission in amplify-and-forward (AF) co- operative system which implemented joint channel estimation and data detection at the destination (receiver) is considered. The nonlinear block code is designed to as- sist the above methodology. The design criterion takes into account the uncertainty of channel parameters at the receiver based on joint channel estimation and data detection algorithm and the simulations will prove that it can achieve full diversity that is offered by multiple relay and frequency-selective fading channel. Using an approximation of the union boun on the error probability as the design criterion, such that it can be simulated as a function for simulated annealing algorithm. The designed codewords are applied to the AF cooperative system. In order to assess the performance of joint estimation and detection fashion, the numerical simulations will be carried out the word error rate (WER) performances illustrate that improve- ment over differnt benchmark schemes can be obtained.

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.

Orthogonal frequency-division multiplexing (OFDM) has been adopted by many broadband wireless communication systems for the simplicity of the receiver technique to support high data rates and user mobility. However, studies also show that the advantage of OFDM over the single-carrier modulation schemes could be substantially compromised by timing or frequency estimation errors at the receiver. In this thesis we investigate the synchronization problem for practical OFDM systems using a system model generalized from the IEEE 802.11 and IEEE 802.16 standards. For preamble based synchronization schemes, which are most common in the downlink of wireless communication systems, we propose a novel timing acquisition algorithm which minimizes false alarm probability and indirectly improves correct detection probability. We then introduce a universal fractional carrier frequency offset (CFO) estimator that outperforms conventional methods at low signal to noise ratio with lower complexity. More accurate timing and frequency estimates can be obtained by our proposed frequency-domain algorithms incorporating channel knowledge. We derive four joint frequency, timing, and channel estimators with different approximations, and then propose a hybrid integer CFO estimation scheme to provide flexible performance and complexity tradeoffs. When the exact channel delay profile is unknown at the receiver, we present a successive timing estimation algorithm to solve the timing ambiguity. Both analytical and simulation results are presented to confirm the performance of the proposed methods in various realistic channel conditions. The ranging based synchronization scheme is most commonly used in the uplink of wireless communication systems. Here we propose a successive multiuser detection algorithm to mitigate multiple access interference and achieve better performance than that of conventional single-user based methods. A reduced-complexity version of the successive algorithm feasible for hardware real-time implementation is also presented in the thesis. To better understand the performance of a ranging detector from a system point of view, we develop a technique that can directly translate a detector�s missed detection probability into the maximum number of users that the method can support in one cell with a given number of ranging opportunities. The analytical results match the simulations reasonably well and show that the proposed successive algorithms allow a base station to serve more than double the number of users supported by the conventional methods. Finally, we investigate inter-carrier interference which is caused by the timevarying communication channels. We derive the bounds on the power of residual inter-carrier interference that cannot be mitigated by a frequency-domain equalizer with a given number of taps. We also propose a Turbo equalization scheme using the novel grouped Particle filter, which approaches the performance of the Maximum A Posterior algorithm with much lower complexity.

The topic of this thesis is signaling design for data transmission through wireless channels between a transmitter and a receiver that can both be equipped with one or more antennas. In particular, the focus is on channels where the propagation coefficients between each transmitter--receiver antenna pair are only partially known or completetly unknown to the receiver and unknown to the transmitter. A standard signal design approach for this scenario is based on separate training for the acquisition of channel knowledge at the receiver and subsequent error-control coding for data detection over channels that are known or at least approximately known at the receiver. If the number of parameters to estimate in the acquisition phase is high as, e.g., in a frequency-selective multiple-input multiple-output channel, the required amount of training symbols can be substantial. It is therefore of interest to study signaling schemes that minimize the overhead of training or avoid a training sequence altogether. Several approaches for the design of such schemes are considered in this thesis. Two different design methods are investigated based on a signal representation in the time domain. In the first approach, the symbol alphabet is preselected, the design problem is formulated as an integer optimization problem and solutions are found using simulated annealing. The second design method is targeted towards general complex-valued signaling and applies a constrained gradient-search algorithm. Both approaches result in signaling schemes with excellent detection performance, albeit at the cost of significant complexity requirements. A third approach is based on a signal representation in the frequency domain. A low-complexity signaling scheme performing differential space--frequency modulation and detection is described, analyzed in detail and evaluated by simulation examples. The mentioned design approaches assumed that the receiver has no knowledge about the value of the channel coefficients. However, we also investigate a scenario where the receiver has access to an estimate of the channel coefficients with known error statistics. In the case of a frequency-flat fading channel, a design criterion allowing for a smooth transition between the corresponding criteria for known and unknown channel is derived and used to design signaling schemes matched to the quality of the channel estimate. In particular, a constellation design is proposed that offers a high level of flexibility to accomodate various levels of channel knowledge at the receiver. / QC 20101014

<p>The topic of this thesis is signaling design for data transmission through wireless channels between a transmitter and a receiver that can both be equipped with one or more antennas. In particular, the focus is on channels where the propagation coefficients between each transmitter--receiver antenna pair are only partially known or completetly unknown to the receiver and unknown to the transmitter.</p><p>A standard signal design approach for this scenario is based on separate training for the acquisition of channel knowledge at the receiver and subsequent error-control coding for data detection over channels that are known or at least approximately known at the receiver. If the number of parameters to estimate in the acquisition phase is high as, e.g., in a frequency-selective multiple-input multiple-output channel, the required amount of training symbols can be substantial. It is therefore of interest to study signaling schemes that minimize the overhead of training or avoid a training sequence altogether.</p><p>Several approaches for the design of such schemes are considered in this thesis. Two different design methods are investigated based on a signal representation in the time domain. In the first approach, the symbol alphabet is preselected, the design problem is formulated as an integer optimization problem and solutions are found using simulated annealing. The second design method is targeted towards general complex-valued signaling and applies a constrained gradient-search algorithm. Both approaches result in signaling schemes with excellent detection performance, albeit at the cost of significant complexity requirements.</p><p>A third approach is based on a signal representation in the frequency domain. A low-complexity signaling scheme performing differential space--frequency modulation and detection is described, analyzed in detail and evaluated by simulation examples.</p><p>The mentioned design approaches assumed that the receiver has no knowledge about the value of the channel coefficients. However, we also investigate a scenario where the receiver has access to an estimate of the channel coefficients with known error statistics. In the case of a frequency-flat fading channel, a design criterion allowing for a smooth transition between the corresponding criteria for known and unknown channel is derived and used to design signaling schemes matched to the quality of the channel estimate. In particular, a constellation design is proposed that offers a high level of flexibility to accomodate various levels of channel knowledge at the receiver.</p>

The increasing demand for high data rate transmission over broadband radio channels has imposed significant challenges in wireless communications. Accurate channel estimation has a major impact on the whole system performance. Specifically, reliable estimate of the channel state information (CSI) is more challenging for orthogonal frequency division multiplexing (OFDM) systems in doubly selective fading channels than for the slower fading channels over which OFDM has been deployed traditionally. With the help of a basis expansion model (BEM), a novel multivariate autoregressive (AR) process is developed to model the time evolution of the fast fading channel. Relying on pilot symbol aided modulation (PSAM), a novel Kalman smoothing algorithm based on a second-order dynamic model is exploited, where the mean square error (MSE) of the channel estimator is near to that of the optimal Wiener filter. To further improve the performance of channel estimation, a novel low-complexity iterative joint channel estimation and symbol detection procedure is developed for fast fading channels with a small number of pilots and low pilot power to achieve the bit error rate (BER) performance close to when the CSI is known perfectly. The new channel estimation symbol detection technique is robust to variations of the radio channel from the design values and applicable to multiple modulation and coding types. By use of the extrinsic information transfer (EXIT) chart, we investigate the convergence behavior of the new algorithm and analyze the modulation, pilot density, and error correction code selection for good system performance for a given power level. The algorithms developed in this thesis improve the performance of the whole system requiring only low ratios of pilot to data for excellent performance in fast fading channels. / Graduate

Bayesian approaches for sparse signal recovery have enjoyed a long-standing history in signal processing and machine learning literature. Among the Bayesian techniques, the expectation maximization based Sparse Bayesian Learning(SBL) approach is an iterative procedure with global convergence guarantee to a local optimum, which uses a parameterized prior that encourages sparsity under an evidence maximization frame¬work. SBL has been successfully employed in a wide range of applications ranging from image processing to communications. In this thesis, we propose novel, efficient and low-complexity SBL-based algorithms that exploit structured sparsity in the presence of fully/partially known measurement matrices. We apply the proposed algorithms to the problem of channel estimation and data detection in Orthogonal Frequency Division Multiplexing(OFDM) systems. Further, we derive Cram´er Rao type lower Bounds(CRB) for the single and multiple measurement vector SBL problem of estimating compressible vectors and their prior distribution parameters. The main contributions of the thesis are as follows: We derive Hybrid, Bayesian and Marginalized Cram´er Rao lower bounds for the problem of estimating compressible vectors drawn from a Student-t prior distribution. We derive CRBs that encompass the deterministic or random nature of the unknown parameters of the prior distribution and the regression noise variance. We use the derived bounds to uncover the relationship between the compressibility and Mean Square Error(MSE) in the estimates. Through simulations, we demonstrate the dependence of the MSE performance of SBL based estimators on the compressibility of the vector. OFDM is a well-known multi-carrier modulation technique that provides high spectral efficiency and resilience to multi-path distortion of the wireless channel It is well-known that the impulse response of a wideband wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. In this thesis, we consider the estimation of the unknown channel coefficients and its support in SISO-OFDM systems using a SBL framework. We propose novel pilot-only and joint channel estimation and data detection algorithms in block-fading and time-varying scenarios. In the latter case, we use a first order auto-regressive model for the time-variations, and propose recursive, low-complexity Kalman filtering based algorithms for channel estimation. Monte Carlo simulations illustrate the efficacy of the proposed techniques in terms of the MSE and coded bit error rate performance. • Multiple Input Multiple Output(MIMO) combined with OFDM harnesses the inherent advantages of OFDM along with the diversity and multiplexing advantages of a MIMO system. The impulse response of wireless channels between the Nt transmit and Nr receive antennas of a MIMO-OFDM system are group approximately sparse(ga-sparse),i.e. ,the Nt Nr channels have a small number of significant paths relative to the channel delay spread, and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wire¬less channels are also group approximately-cluster sparse(ga-csparse),i.e.,every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this thesis, we cast the problem of estimating the ga-sparse and ga-csparse block-fading and time-varying channels using a multiple measurement SBL framework. We propose a bouquet of novel algorithms for MIMO-OFDM systems that generalize the algorithms proposed in the context of SISO-OFDM systems. The efficacy of the proposed techniques are demonstrated in terms of MSE and coded bit error rate performance.