Improving the Spectral Efficiency of Nonlinear Satellite Systems through Time-Frequency Packing and Advanced Processing

We consider realistic satellite communications systems for broadband and broadcasting applications, based on frequency-division-multiplexed linear modulations, where spectral efficiency is one of the main figures of merit. For these systems, we investigate their ultimate performance limits by using a framework to compute the spectral efficiency when suboptimal receivers are adopted and evaluating the performance improvements that can be obtained through the adoption of the time-frequency packing technique. Our analysis reveals that introducing controlled interference can significantly increase the efficiency of these systems. Moreover, if a receiver which is able to account for the interference and the nonlinear impairments is adopted, rather than a classical predistorter at the transmitter coupled with a simpler receiver, the benefits in terms of spectral efficiency can be even larger. Finally, we consider practical coded schemes and show the potential advantages of the optimized signaling formats when combined with iterative detection/decoding.Comment: 8 pages, 8 figure

Two-Stage LASSO ADMM Signal Detection Algorithm For Large Scale MIMO

This paper explores the benefit of using some of the machine learning techniques and Big data optimization tools in approximating maximum likelihood (ML) detection of Large Scale MIMO systems. First, large scale MIMO detection problem is formulated as a LASSO (Least Absolute Shrinkage and Selection Operator) optimization problem. Then, Alternating Direction Method of Multipliers (ADMM) is considered in solving this problem. The choice of ADMM is motivated by its ability of solving convex optimization problems by breaking them into smaller sub-problems, each of which are then easier to handle. Further improvement is obtained using two stages of LASSO with interference cancellation from the first stage. The proposed algorithm is investigated at various modulation techniques with different number of antennas. It is also compared with widely used algorithms in this field. Simulation results demonstrate the efficacy of the proposed algorithm for both uncoded and coded cases.Comment: 5 pages, 4 figure

Channel, Phase Noise, and Frequency Offset in OFDM Systems: Joint Estimation, Data Detection, and Hybrid Cramer-Rao Lower Bound

Oscillator phase noise (PHN) and carrier frequency offset (CFO) can adversely impact the performance of orthogonal frequency division multiplexing (OFDM) systems, since they can result in inter carrier interference and rotation of the signal constellation. In this paper, we propose an expectation conditional maximization (ECM) based algorithm for joint estimation of channel, PHN, and CFO in OFDM systems. We present the signal model for the estimation problem and derive the hybrid Cramer-Rao lower bound (HCRB) for the joint estimation problem. Next, we propose an iterative receiver based on an extended Kalman filter for joint data detection and PHN tracking. Numerical results show that, compared to existing algorithms, the performance of the proposed ECM-based estimator is closer to the derived HCRB and outperforms the existing estimation algorithms at moderate-to-high signal-to-noise ratio (SNR). In addition, the combined estimation algorithm and iterative receiver are more computationally efficient than existing algorithms and result in improved average uncoded and coded bit error rate (BER) performance

Throughput-based Design for Polar Coded-Modulation

Typically, forward error correction (FEC) codes are designed based on the minimization of the error rate for a given code rate. However, for applications that incorporate hybrid automatic repeat request (HARQ) protocol and adaptive modulation and coding, the throughput is a more important performance metric than the error rate. Polar codes, a new class of FEC codes with simple rate matching, can be optimized efficiently for maximization of the throughput. In this paper, we aim to design HARQ schemes using multilevel polar coded-modulation (MLPCM). Thus, we first develop a method to determine a set-partitioning based bit-to-symbol mapping for high order QAM constellations. We simplify the LLR estimation of set-partitioned QAM constellations for a multistage decoder, and we introduce a set of algorithms to design throughput-maximizing MLPCM for the successive cancellation decoding (SCD). These codes are specifically useful for non-combining (NC) and Chase-combining (CC) HARQ protocols. Furthermore, since optimized codes for SCD are not optimal for SC list decoders (SCLD), we propose a rate matching algorithm to find the best rate for SCLD while using the polar codes optimized for SCD. The resulting codes provide throughput close to the capacity with low decoding complexity when used with NC or CC HARQ

Automatic classification of digital communication signal modulations

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityAutomatic modulation classification detects the modulation type of received communication signals. It has important applications in military scenarios to facilitate jamming, intelligence, surveillance, and threat analysis. The renewed interest from civilian scenes has been fuelled by the development of intelligent communications systems such as cognitive radio and software defined radio. More specifically, it is complementary to adaptive modulation and coding where a modulation can be deployed from a set of candidates according to the channel condition and system specification for improved spectrum efficiency and link reliability. In this research, we started by improving some existing methods for higher classification accuracy but lower complexity. Machine learning techniques such as k-nearest neighbour and support vector machine have been adopted for simplified decision making using known features. Logistic regression, genetic algorithm and genetic programming have been incorporated for improved classification performance through feature selection and combination. We have also developed a new distribution test based classifier which is tailored for modulation classification with the inspiration from Kolmogorov-Smirnov test. The proposed classifier is shown to have improved accuracy and robustness over the standard distribution test. For blind classification in imperfect channels, we developed the combination of minimum distance centroid estimator and non-parametric likelihood function for blind modulation classification without the prior knowledge on channel noise. The centroid estimator provides joint estimation of channel gain and carrier phase o set where both can be compensated in the following nonparametric likelihood function. The non-parametric likelihood function, in the meantime, provide likelihood evaluation without a specifically assumed noise model. The combination has shown to have higher robustness when different noise types are considered. To push modulation classification techniques into a more timely setting, we also developed the principle for blind classification in MIMO systems. The classification is achieved through expectation maximization channel estimation and likelihood based classification. Early results have shown bright prospect for the method while more work is needed to further optimize the method and to provide a more thorough validation.School of Engineering and Design Brunel University London, the Faculty of Engineering University of Liverpool, and the University of Liverpool Graduate Association (Hong Kong)