Enhanced Channel Estimation Based On Basis Expansion Using Slepian Sequences for Time Varying OFDM Systems

The Channel estimation in OFDM has become very important to recover the accurate information from the received data as the next generation of wireless technology has very high data rate along with the very high speed mobile terminals as users. In addition the fast fading channels, ICI, multipath fading channels may completely destroy the data. Also it is required to use less complex method for estimation. We are proposing the method which compares the number of techniques and gives the results in BER Vs SNR graphs. The LS estimation technique is less complex as compared to MMSE estimation but gives fails in accuracy. Using Prolate function we can reduce the complexity in calculation of parameters. If compared with state of art approach where the complexity is O(N)3, the complexity using Prolate function is O(N)2.The function depends upon maximum delay and maximum Doppler frequency spread thus parameter calculation is reduced. The technique dose not calculate particular channel characteristics. Slepian sequences utilizes the bandwidth as the sharp pulses replace the regular rectangular pulses which causes spectral leakage and thus ICI. The simulation of BER Vs SNR using CP and UW with and without Prolate is proposed that increases spectral efficiency with reduced calculations replacing rectangular pulses by Slepian pulses which increase energy concentration by Sharpe pulses thus reduction in inter carrier interference caused by multipath fading. DOI: 10.17762/ijritcc2321-8169.150513

Fractional fourier based sparse channel estimation for multicarrier underwater acoustic communication system

This paper presents a hybrid sparse channel estimation based on Fractional Fourier Transform (FrFT) for orthogonal frequency division multiplex (OFDM) scenario to exploit channel sparsity of underwater acoustic (UWA) channel. A novel channel dictionary matrix based on chirp signals is constructed and mutual coherence is adopted to evaluate its preservation of sparse information. In addition, Compressive Sampling Matching Pursuit (CoSaMP) is implemented to estimate the sparse channel coefficients. Simulation results demonstrate a significant Normalized Mean Square Error (NMSE) improvement of 10dB over Basis Expansion Model (BEM) with less complexity

BER PERFORMANCE ANALYSIS FOR MIMO-OFDM IN TIME-VARYING CHANNELS

In orthogonal frequency division multiplexing (OFDM) systems, time varying channels leads to destroy the orthogonality among subcarriers and yielding inter carrier interference (ICI) in OFDM Systems. A time domain approach is used to reduce time variations in ICI-mitigating block. A time domain equalizer (TEQ) is often used at the receiver to mitigate the total response transmission time but the design of TEQ is a difficult task. In this paper, a linear time varying channel is considered to suppress inter carrier interference and to lower computational complexity. The receiver structure of Time domain Synchronous OFDM is able to estimate the linear time varying channels easily, so TDS-OFDM is suitable for proposed ICI mitigation algorithm. Multi input multi output OFDM system (MIMO-OFDM) needed channel estimation based on Linear Time Varying channel model. Two modulations schemes, QPSK and 16 QAM are used in proposed work to improve performance of two parameters bit error rate (BER) and minimum mean square error (MMSE). Simulation results show that the proposed work can sufficiently suppress the ICI in time varying channels by comparing linear time varying (LTV) channel and linear time invariant (LTI) channel in MIMO-OFDM

A new subspace method for blind estimation of selective MIMO-STBC channels

In this paper, a new technique for the blind estimation of frequency and/or time-selective multiple-input multiple-output (MIMO) channels under space-time block coding (STBC) transmissions is presented. The proposed method relies on a basis expansion model (BEM) of the MIMO channel, which reduces the number of parameters to be estimated, and includes many practical STBC-based transmission scenarios, such as STBC-orthogonal frequency division multiplexing (OFDM), space-frequency block coding (SFBC), time-reversal STBC, and time-varying STBC encoded systems. Inspired by the unconstrained blind maximum likelihood (UML) decoder, the proposed criterion is a subspace method that efficiently exploits all the information provided by the STBC structure, as well as by the reduced-rank representation of the MIMO channel. The method, which is independent of the specific signal constellation, is able to blindly recover the MIMO channel within a small number of available blocks at the receiver side. In fact, for some particular cases of interest such as orthogonal STBC-OFDM schemes, the proposed technique blindly identifies the channel using just one data block. The complexity of the proposed approach reduces to the solution of a generalized eigenvalue (GEV) problem and its computational cost is linear in the number of sub-channels. An identifiability analysis and some numerical examples illustrating the performance of the proposed algorithm are also providedThis work was supported by the Spanish Government under projects TEC2007-68020-C04-02/TCM (MultiMIMO) and CONSOLIDER-INGENIO 2010 CSD2008-00010 (COMONSENS)

Pilot Design for Enhanced Channel Estimation in Doubly Selective Channels

This paper investigates pilot design for enhanced channel estimation in single carrier communication systems over doubly-selective channels (DSC). Our contribution is twofold: first, we propose to use Huffman sequences as pilot clusters with low peak-to-average power ratio (PAPR), yet with good channel estimation performance when periodic pilot placement is adopted; second, we propose a low-complexity pilot placement strategy based on the analysis of the complex-exponential basis expansion model (CE-BEM) of the DSC. The latter leads to improved channel estimation performance with useful insights for pilot placement