Joint Estimation and Decoding of Space-Time Trellis Codes

Abstract

We explore the possibility of using an emerging tool in statistical signal processing, sequential importance sampling (SIS), for joint estimation and decoding of space-time trellis codes (STTC). First, we provide background on SIS, and then we discuss its application to space-time trellis code (STTC) systems. It is shown through simulations that SIS is suitable for joint estimation and decoding of STTC with time-varying flat-fading channels when phase ambiguity is avoided. We used a design criterion for STTCs and temporally correlated channels that combats phase ambiguity without pilot signaling. We have shown by simulations that the design is valid.

ARMA Synthesis of Fading Channels

“…Table II compares the performance of the ARMA, AR, IDFT, and SOS methods under 28,24,22,20, and 16 bits of quantization to represent varying degrees of precision. Equations (15) and (16) were used again to determine the quality of the generated variates. The quantization was applied to the inputs, outputs, filter coefficients, and the output of the IFFT operation.…”

“…The resulting ARMA system could then be applied to systems design and performance assessment in areas such as power control for broadband and CDMA systems [12]- [17], channel estimation using Kalman filters [16]- [19], and blind detection and decoding [13]- [18]. In addition to computational considerations, this correspondence compares the finite-precision performances of the above fading channel simulation techniques, filling a gap in the previous literature.…”

Data detection in relay-based communication systems using Bayesian methods

“…Particularly, Markov Chain Monte Carlo (MCMC) methods [1,2] and particle filtering techniques have been extensively and successfully used in solving various communication problems [3]. Some of the problems that have been addressed by the Bayesian methods include channel estimation [4,5], equalization [6,7], synchronization [8], and data detection for single-antenna and for multiple-antenna systems [9] in both single-user and multi-user environments [10]. In this paper, we extend the application of these methods to data detection and channel estimation for a relay-based communication system (RCS).…”

Sparse Bayesian learning-based data-aided channel estimation in STTC MIMO systems

“…While the work in [4] employs an expectation maximization (EM) framework, the authors in [5] and [6] make use of the sequence importance sampling (SIS) and the deterministic particle filtering paradigms respectively for joint estimation. However, to the best of our knowledge, none of the existing works exploit the inherent spatial sparsity of the MIMO wireless channel.…”