An adaptive Bernoulli-Gaussian model based maximum-likelihood channel equalizer for detection of binary sequences

[[abstract]]Based on a modified Bernoulli-Gaussian model, we propose an adaptive maximum-likelihood channel equalizer, which is a block signal processing algorithm, for the detection of binary sequences transmitted through an unknown slowly time-varying channel. Both computational load and storage required by the proposed adaptive channel equalizer are linearly rather than exponentially proportional to the size of signal processing block. A simulation example is provided to support that it can simultaneously track the variation of slowly time-varying channels and detect unknown binary sequences well.[[fileno]]2030157030002[[department]]電機工程學

Sparse nonlinear optimization for signal processing and communications

This dissertation proposes three classes of new sparse nonlinear optimization algorithms for network echo cancellation (NEC), 3-D synthetic aperture radar (SAR) image reconstruction, and adaptive turbo equalization in multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications, respectively. For NEC, the proposed two proportionate affine projection sign algorithms (APSAs) utilize the sparse nature of the network impulse response (NIR). Benefiting from the characteristics of l₁-norm optimization, affine projection, and proportionate matrix, the new algorithms are more robust to impulsive interferences and colored input than the conventional adaptive algorithms. For 3-D SAR image reconstruction, the proposed two compressed sensing (CS) approaches exploit the sparse nature of the SAR holographic image. Combining CS with the range migration algorithms (RMAs), these approaches can decrease the load of data acquisition while recovering satisfactory 3-D SAR image through l₁-norm optimization. For MIMO UWA communications, a robust iterative channel estimation based minimum mean-square-error (MMSE) turbo equalizer is proposed for large MIMO detection. The MIMO channel estimation is performed jointly with the MMSE equalizer and the maximum a posteriori probability (MAP) decoder. The proposed MIMO detection scheme has been tested by experimental data and proved to be robust against tough MIMO channels. --Abstract, page iv

Two dimensional signal processing for storage channels

Over the past decade, storage channels have undergone a steady increase in capacity. With the prediction of achieving 10 Tb/in2 areal density for magnetic recording channels in sight, the industry is pushing towards di erent technologies for storage channels. Heat-assisted magnetic recording, bit-patterned media, and twodimensional magnetic recording (TDMR) are cited as viable alternative technologies to meet the increasing market demand. Among these technologies, the twodimensional magnetic recording channel has the advantage of using conventional medium while relying on improvement from signal processing. Capacity approaching codes and detection methods tailored to the magnetic recording channels are the main signal processing tools used in magnetic recording. The promise is that two-dimensional signal processing will play a role in bringing about the theoretical predictions. The main challenges in TDMR media are as follows: i) the small area allocated to each bit on the media, and the sophisticated read and write processes in shingled magnetic recording devices result in signi cant amount of noise, ii) the twodimensional inter-symbol interference is intrinsic to the nature of shingled magnetic recording. Thus, a feasible two-dimensional communication system is needed to combat the errors that arise from aggressive read and write processes. In this dissertation, we present some of the work done on signal processing aspect for storage channels. We discuss i) the nano-scale model of the storage channel, ii) noise characteristics and corresponding detection strategies, iii) two-dimensional signal processing targeted at shingled magnetic recording