An Optimized Joint Time Synchronization and Channel Estimation Scheme for OFDM Systems

Optimized joint timing synchronization and channel estimation for communications systems with multiple transmit antennas

“…The actual threshold used in [17] is α|ĥ max |, where α is a threshold factor and |ĥ max | is the strongest channel tap gain estimate. In this paper, pre-simulations and mathematical derivations are not required to choose thresholds for fine time adjustment [9,10,12,13]. Moreover, the performance of timing synchronization is calculated based on the estimated timing offset at each receive antenna.…”

“…In addition, pre-simulations for searching a proper α are also needed. Therefore, low SNR and wide interval for fine time adjustment significantly degrade the performance in [9,10,12,13]. In Figures 5 and 6, our proposed approach has better performance than the joint scheme [17].…”

“…However, compared with the single carrier systems, OFDM systems are much more sensitive to synchronization errors [5]. Several approaches have been proposed to address this problem [6][7][8][9][10][11][12][13][14]. In addition, the performance of multiple-input signal processing depends on the amount of channel information.…”

“…Various techniques based on training sequences have been proposed to obtain accurate synchronization and channel estimation [9][10][11][12][13][14][15][16][17][18][19]. In [15], a training sequence arrangement based on modulated orthogonal sequences (MOS) for MIMO-OFDM systems was proposed to synchronize different transmit antennas.…”

“…This paper develops a joint timing synchronization and channel estimation algorithm suitable for downlink MIMO networks based on a time-domain training sequence arrangement in the short-range wireless transmission environment. Unlike the previous works [9][10][11][12][13]17,18], this paper not only introduces the use of sliding observation vector in timing synchronization phase but also applies the minimum mean squared error (MMSE) criterion to perform fine time adjustment. In this paper, we first obtain a coarse timing offset using the cross-correlation function outputs based on the proposed training sequences at each receive antenna and then apply the generalized maximum-likelihood (ML) algorithm to find the advanced timing, relative timing indices, and the corresponding CIR estimates.…”