51 research outputs found
A Novel Data-Aided Channel Estimation with Reduced Complexity for TDS-OFDM Systems
In contrast to the classical cyclic prefix (CP)-OFDM, the time domain
synchronous (TDS)-OFDM employs a known pseudo noise (PN) sequence as guard
interval (GI). Conventional channel estimation methods for TDS-OFDM are based
on the exploitation of the PN sequence and consequently suffer from intersymbol
interference (ISI). This paper proposes a novel dataaided channel estimation
method which combines the channel estimates obtained from the PN sequence and,
most importantly, additional channel estimates extracted from OFDM data
symbols. Data-aided channel estimation is carried out using the rebuilt OFDM
data symbols as virtual training sequences. In contrast to the classical turbo
channel estimation, interleaving and decoding functions are not included in the
feedback loop when rebuilding OFDM data symbols thereby reducing the
complexity. Several improved techniques are proposed to refine the data-aided
channel estimates, namely one-dimensional (1-D)/two-dimensional (2-D) moving
average and Wiener filtering. Finally, the MMSE criteria is used to obtain the
best combination results and an iterative process is proposed to progressively
refine the estimation. Both MSE and BER simulations using specifications of the
DTMB system are carried out to prove the effectiveness of the proposed
algorithm even in very harsh channel conditions such as in the single frequency
network (SFN) case
Enhanced Two-Dimensional Data-aided Channel Estimation for TDS-OFDM
International audienceIn time domain synchronous (TDS)-OFDM, the channel estimation is conventionally carried out based on the pseudo noise (PN) sequence. The PN sequence based channel estimation however suffers interference from adjacent OFDM data symbols. This paper proposes a new low-complexity dataaided channel estimation method with two-dimensional (2-D) estimate refinement and interpolation. Data-aided channel estimation is carried out using the rebuilt OFDM data symbols as virtual training symbols. In contrast to the classical turbo channel estimation, interleaving and decoding functions are not used when rebuilding OFDM data symbols thereby reducing the complexity. 2-D estimate refinement and interpolation are proposed to improve the data-aided channel estimation. Simulation results show that the performance of TDS-OFDM based DTMB system using the proposed method is very close to that with perfect channel estimation in terms of bit error rate (BER)
Timing and Carrier Synchronization in Wireless Communication Systems: A Survey and Classification of Research in the Last 5 Years
Timing and carrier synchronization is a fundamental requirement for any wireless communication system to work properly. Timing synchronization is the process by which a receiver node determines the correct instants of time at which to sample the incoming signal. Carrier synchronization is the process by which a receiver adapts the frequency and phase of its local carrier oscillator with those of the received signal. In this paper, we survey the literature over the last 5 years (2010–2014) and present a comprehensive literature review and classification of the recent research progress in achieving timing and carrier synchronization in single-input single-output (SISO), multiple-input multiple-output (MIMO), cooperative relaying, and multiuser/multicell interference networks. Considering both single-carrier and multi-carrier communication systems, we survey and categorize the timing and carrier synchronization techniques proposed for the different communication systems focusing on the system model assumptions for synchronization, the synchronization challenges, and the state-of-the-art synchronization solutions and their limitations. Finally, we envision some future research directions
Semi-Data-Aided Channel Estimation for MIMO Systems via Reinforcement Learning
Data-aided channel estimation is a promising solution to improve channel
estimation accuracy by exploiting data symbols as pilot signals for updating an
initial channel estimate. In this paper, we propose a semi-data-aided channel
estimator for multiple-input multiple-output communication systems. Our
strategy is to leverage reinforcement learning (RL) for selecting reliable
detected symbols among the symbols in the first part of transmitted data block.
This strategy facilitates an update of the channel estimate before the end of
data block transmission and therefore achieves a significant reduction in
communication latency compared to conventional data-aided channel estimation
approaches. Towards this end, we first define a Markov decision process (MDP)
which sequentially decides whether to use each detected symbol as an additional
pilot signal. We then develop an RL algorithm to efficiently find the best
policy of the MDP based on a Monte Carlo tree search approach. In this
algorithm, we exploit the a-posteriori probability for approximating both the
optimal future actions and the corresponding state transitions of the MDP and
derive a closed-form expression for the best policy. Simulation results
demonstrate that the proposed channel estimator effectively mitigates both
channel estimation error and detection performance loss caused by insufficient
pilot signals
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