Performance of hard handoff in 1xev-do rev. a systems

1x Evolution-Data Optimized Revision A (1xEV-DO Rev. A) is a cellular communications standard that introduces key enhancements to the high data rate packet switched 1xEV-DO Release 0 standard. The enhancements are driven by the increasing demand on some applications that are delay sensitive and require symmetric data rates on the uplink and the downlink. Some examples of such applications being video telephony and voice over internet protocol (VoIP). The handoff operation is critical for delay sensitive applications because the mobile station (MS) is not supposed to lose service for long periods of time. Therefore seamless server selection is used in Rev. A systems. This research analyzes the performance of this handoff technique. A theoretical approach is presented to calculate the slot error probability (SEP). The approach enables evaluating the effects of filtering, hysteresis as well as the system introduced delay to handoff execution. Unlike previous works, the model presented in this thesis considers multiple base stations (BS) and accounts for correlation of shadow fading affecting different signal powers received from different BSs. The theoretical results are then verified over ranges of parameters of practical interest using simulations, which are also used to evaluate the packet error rate (PER) and the number of handoffs per second. Results show that the SEP gives a good indication about the PER. Results also show that when considering practical handoff delays, moderately large filter constants are more efficient than smaller ones

Performance of hard handoff in 1xev-do rev. a systems

1x Evolution-Data Optimized Revision A (1xEV-DO Rev. A) is a cellular communications standard that introduces key enhancements to the high data rate packet switched 1xEV-DO Release 0 standard. The enhancements are driven by the increasing demand on some applications that are delay sensitive and require symmetric data rates on the uplink and the downlink. Some examples of such applications being video telephony and voice over internet protocol (VoIP). The handoff operation is critical for delay sensitive applications because the mobile station (MS) is not supposed to lose service for long periods of time. Therefore seamless server selection is used in Rev. A systems. This research analyzes the performance of this handoff technique. A theoretical approach is presented to calculate the slot error probability (SEP). The approach enables evaluating the effects of filtering, hysteresis as well as the system introduced delay to handoff execution. Unlike previous works, the model presented in this thesis considers multiple base stations (BS) and accounts for correlation of shadow fading affecting different signal powers received from different BSs. The theoretical results are then verified over ranges of parameters of practical interest using simulations, which are also used to evaluate the packet error rate (PER) and the number of handoffs per second. Results show that the SEP gives a good indication about the PER. Results also show that when considering practical handoff delays, moderately large filter constants are more efficient than smaller ones

Rectified Gaussian Scale Mixtures and the Sparse Non-Negative Least Squares Problem

In this paper, we develop a Bayesian evidence maximization framework to solve the sparse non-negative least squares (S-NNLS) problem. We introduce a family of probability densities referred to as the Rectified Gaussian Scale Mixture (R- GSM) to model the sparsity enforcing prior distribution for the solution. The R-GSM prior encompasses a variety of heavy-tailed densities such as the rectified Laplacian and rectified Student- t distributions with a proper choice of the mixing density. We utilize the hierarchical representation induced by the R-GSM prior and develop an evidence maximization framework based on the Expectation-Maximization (EM) algorithm. Using the EM based method, we estimate the hyper-parameters and obtain a point estimate for the solution. We refer to the proposed method as rectified sparse Bayesian learning (R-SBL). We provide four R- SBL variants that offer a range of options for computational complexity and the quality of the E-step computation. These methods include the Markov chain Monte Carlo EM, linear minimum mean-square-error estimation, approximate message passing and a diagonal approximation. Using numerical experiments, we show that the proposed R-SBL method outperforms existing S-NNLS solvers in terms of both signal and support recovery performance, and is also very robust against the structure of the design matrix.Comment: Under Review by IEEE Transactions on Signal Processin

Message Passing Algorithms and Extensions of Sparse Bayesian Learning

Sparse Signal Recovery (SSR) has an essential role in a number of modern engineering applications. This thesis focuses on Bayesian algorithms for sparse signal recovery, where it addresses some of the shortcomings associated with such algorithms. The high complexity of the sparse Bayesian learning algorithm is addressed first, where we present an algorithm that incorporates damped Gaussian generalized approximate message passing (GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning (SBL). In particular, GGAMP is used to implement the E-step in SBL in place of matrix inversion. We propose an algorithm that is much more robust to arbitrary measurement matrices than the standard damped GAMP algorithms while being much lower complexity than the standard SBL algorithm. We then extend the approach from the single measurement vector (SMV) case to the temporally correlated multiple measurement vector (MMV) case.The approach developed for the standard SBL is extended to address the sparse non-negative least squares (NNLS) problem using a rectified Gaussian scale mixture approach combined with the generalized approximate message passing algorithm. This approach enhances convergence compared to existing GAMP based sparse NNLS algorithms. Other advantages include significant reduction in derivation complexity of the algorithm, and the ability to impose different priors on the signal, simply by changing the less computationally demanding M-step. Moreover, extending the algorithm to the multiple measurement vector case is straightforward, and is achieved by a simple modification to the M-step as well.Next, we provide a new perspective of the SBL algorithm. A novel interpretation of the SBL algorithm's iterations is developed, where the iterations are divided into a minimum power distortionless receiver (MPDR) step and a denoising step. This interpretation provides significant intuitive insights into the SBL, which can potentially enable enhancing the algorithm and extending it beyond some of its current limitations. To demonstrate this potential, we propose a low complexity algorithm that can handle a wide range of sparsity promoting priors. We also show how the new perspective on SBL extends to other variants of the algorithm, such as the sequential fast-SBL algorithm and the multiple measurement vector (MMV) SBL variants. We demonstrate potential benefits of such interpretation by extending the fast-SBL to incorporate more general priors, and by developing a low complexity MMV algorithm.Finally, we address the MIMO semi-blind channel estimation problem, benefiting from the insights gained from previous results. We propose an eigenvalue decomposition based technique to significantly reduce the dimensionality of the Gaussian EM based estimation algorithm, greatly lowering the computational complexity. In addition to that, we apply the MPDR based decoupling principle to derive a tractable EM algorithm that uses the actual discrete prior of the data symbols

Sparse Bayesian Learning Using Approximate Message Passing

Abstract-We use the approximate message passing framework (AMP) [1] to address the problem of recovering a sparse vector from undersampled noisy measurements. We propose an algorithm based on Sparse Bayesian learning (SBL

Sparse Bayesian Learning Using Approximate Message Passing

Abstract-We use the approximate message passing framework (AMP) [1] to address the problem of recovering a sparse vector from undersampled noisy measurements. We propose an algorithm based on Sparse Bayesian learning (SBL