Sharif Digital Repository

In this paper, which is an extended version of our work at LVA/ICA 2010 [1], the problem of Inter Symbol Interface (ISI) Sparse channel estimation and equalization will be investigated. We firstly propose an adaptive method based on the idea of Least Mean Square (LMS) algorithm and the concept of smoothed l0 (SL0) norm presented in [2] for estimation of sparse ISI channels. Afterwards, a new non-adaptive fast channel estimation method based on SL0 sparse signal representation is proposed. ISI channel estimation will have a direct effect on the performance of the ISI equalizer at the receiver. So, in this paper we investigate this effect in the case of optimal Maximum Likelihood... 

A novel architecture for QR decomposition-based recursive least squares is presented. It offers low latency for applications where the channel equalization and adaptive filtering are mandatory. This approach reduces the computations by rewriting the equations in a manner that lets intense hardware resource sharing by reusing similar values in different computations. Moreover, precision range conversion allows for combining complex operations such as root square and division with minimum effect on the overall quantization error. Hence, an efficient lookup table-based solution has highly enhanced the performance of the design by 2.7 times with respect to the previous works  

Reservoir computing is a machine learning approach that enables us to use recurrent neural networks without involving the complexity of training algorithms and make hardware implementation possible. We present a novel photonic architecture of a reservoir computer that employs a nonlinear node and a resonator to implement a virtual recurrent neural network. This resonator behaves as an echo generator component that substitutes the delay line in delaybased reservoir computers available in the literature. The virtual neural network formed in our implementation is fundamentally different from the delay-based reservoir computers. Different virtual architectures based on the FSR and the Finesse of... 

Channel equalization is a crucial part of the OFDM communications protocols, which in turn requires channel estimation. In this paper, we consider the problem of orthogonal pilot design in MIMO-OFDM systems for sparse channel estimation. The pilot design in MIMO scenarios compared to the conventional SISO case has the additional constraint that the capability of recovery should be uniformly provided for all single channels. For instance, perfect estimation of a channel at the cost of another one is not permitted. This requirement is even more significant in the emerging Massive-MIMO systems. Our pilot design is based on the compressed sensing technique of minimizing the coherence measure of... 

While ISI is a common issue in classical communications, it is more challenging and prominent in the context of molecular communication, because one cannot readily combat ISI with classical channel equalization techniques. This is due to the fact that transmitter can only release a positive amount of concentration of a specific molecule into the medium. Previous works have proposed use of chemical reactions to remove molecules from the environment, and to effectively simulate negative signals. However, the differential equation describing a diffusion-reaction process is non-linear. This precludes the possibility of using Fourier transform tools. In this paper, a solution for simulating...