A low-complexity Kalman-filter-based channel estimation method for orthogonal frequency-division multiplexing systems is proposed. This method belongs to the pilot-symbol-aided parametric channel estimation method in which the channel responses are characterised as a collection of sparse propagation paths. Because of the slow variation of the signal subspace in the channel samples’ correlation ...

ELECT The joint sparsity of uplink channels in massive multi-input– multi-output (MIMO) systems is explored and a block sparse model is proposed for joint channel estimation. The block coherence of this model is analysed. It is indicated that as the number of antennas at the base station grows to be infinity, the block coherence will be zero. Then a block optimised orthogonal matching pursuit (...

In this paper, we consider a dual channel signal model with common frequency that is appropriate in a plurality of measurement setups, including impedance measurements and imbalance measurement in direct conversion RF receivers. We investigate on a new approach and present analysis of parameter estimation, specifically the seven parameter dual channel sine fit in which the signals from both cha...

In this thesis a new type of representation for medium level vision operations is explored. We focus on representations that are sparse and monopolar. The word sparse signifies that information in the feature sets used is not necessarily present at all points. On the contrary, most features will be inactive. The word monopolar signifies that all features have the same sign, e.g. are either posi...

Downlink channel estimation with low pilot overhead is an important and challenging problem in large-scale MIMO systems due to the substantially increased MIMO channel dimension. In this letter, we propose a block iterative support detection (block-ISD) based algorithm for downlink channel estimation to reduce the pilot overhead, which is achieved by fully exploiting the block sparsity inherent...

In this paper, we firstly propose an adaptive method based on the idea of Least Mean Square (LMS) algorithm and the concept of smoothed l0 (SL0) normpresented in [1] for estimation of sparse Inter Symbol Interface (ISI) channels which will appear in wireless and acoustic underwater transmissions. Afterwards, a new non-adaptive fast channel estimation method based on SL0 sparse signal representa...

Massive Multiple Input Output (MIMO) is a promising technique for communications due to the high data transmission rate. To harvest benefit from massive MIMO, it necessary have accurate channel estimates. Such channels often exhibit sparsity in virtual angular domain. This paper proposes dichotomous coordinate descent (DCD) based algorithm joint sparse estimation domain orthogonal-frequency-div...