—In cognitive radio network the primary channel information is beneficial. But it can not be obtained by direct channel estimation in cognitive system as pervious methods. And only one possible way is the primary receiver broadcasts the primary channel information to the cognitive users, but it would require the modification of the primary receiver and additional precious spectrum resource. Coo...

In orthogonal frequency division modulation (OFDM) communication systems, channel state information (CSI) is required at receiver due to the fact that frequency-selective fading channel leads to disgusting intersymbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by compressive sensing based sparse ...

It is widely accepted that single carrier frequency division multiple access (SC-FDMA) is an excellent candidate for broadband wireless systems. Channel estimation is one of the key challenges in SC-FDMA, since accurate channel estimation can significantly improve equalization at the receiver and, consequently, enhance the communication performances. In this paper, we study the application of c...

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 no...

To improve accuracy of the channel, this paper proposes a parametric sparse multiple input multiple output (MIMO)-OFDM channel estimation scheme based on the finite rate of innovation (FRI) theory, whereby super-resolution estimates of path delays with arbitrary values can be achieved. For outdoor communication scenarios, where wireless channels are sparse in nature, path delays of different tr...

A joint-optimization method is proposed for enhancing the behavior of the l1-normand sum-log norm-penalized NLMS algorithms to meet the requirements of sparse adaptive channel estimations. The improved channel estimation algorithms are realized by using a state stable model to implement a joint-optimization problem to give a proper trade-off between the convergence and the channel estimation be...

Due to the sparse distribution of reflectors in space, wireless channels are commonly sparse. Thus, utilizing the sparsity of channels in the delay-Doppler domain, a channel estimation method based on compressed sensing (CS) theory can reduce the number of pilots. However, because of discrete truncation in the time domain and limited bandwidth, the time delay and frequency shift of non-integer ...

Natural-gradient (NG) adaptive algorithms are known to be superior to stochasticgradient (SG) algorithms when the channel to be identified exhibits a known Riemannian structure. In sparse channel identification, for example, the improved-proportionate normalized least-mean-square (IPNLMS) is a well known NG algorithm which outperforms the classical SG normalized least-mean square (NLMS). Apart ...

ELECT Pilot power allocation is investigated under the framework of physical layer secure communications in time-division duplex systems, where the secret keys are generated from the estimates of sparse wireless channels. The joint optimisation of secret key capacity and sparse channel estimation performance based on pilot power allocation is formulated as a convex optimisation problem. Conside...