On channel estimation in OFDM systems

The use of multi-amplitude signaling schemes in wireless OFDM systems requires the tracking of the fading radio channel. This paper addresses channel estimation based on time-domain channel statistics. Using a general model for a slowly fading channel, we present the MMSE and LS estimators and a method for modifications compromising between complexity and performance. The symbol error rate for a 16-QAM system is presented by means of simulation results. Depending upon estimator complexity, up to 4 dB in SNR can be gained over the LS estimator. I .  Currently, orthogonal frequency-division multiplexing (OFDM) systems [l] are subject t o significant investigation. Since this technique has been adopted in the European digital audio broadcasting (DAB) system 121, OFDM signaling in fading channel environments has gained a broad interest. For instance, its applicability to digital TV broadcasting is currently being investigated . The use of differential phase-shzji! keying (DPSK) in OFDM systems avoids the tracking of a time varying channel. However, this will limit the number of bits per symbol and results in a 3 dB loss in signal-to-noise ratio (SNR) . If the receiver contains a channel estimator, multiamplitude signaling schemes can be used. In  and [6], 16-QAM modulation in an OFDM system has been investigated. A decision-directed channeltracking method, which allows the use of multi-amplitude schemes in a slow Rayleigh-fading environment is analysed in  . In the design of wireless OFDM systems, the channel is usually assumed to have a finite-length impulse response. A cyclic extension, longer than this impulse response, is put between consecutive blocks in order to avoid interblock interference and preserve orthogonality of the tones [7 ] . Generally, the OFDM system is designed so that the cyclic extension is a small percentage of the total symbol length. This paper discusses channel estimation techniques in wireless OFDM systems, that use this property of the channel impulse response.  and Cioffi  have also addressed this property. In Section 11, we describe the system model. Section I11 discusses the minimum mean-square error (MMSE) and least-squares (LS) channel estimators. The MMSE estimator has good performance but high complexity. The LS estimator has low complexity, but its performance is not as good as that of the MMSE estimator. We present modifications to both MMSE and LS estimators that use the assumption of a finite length impulse response. In Section IV we evaluate the estimators by simulating a 16-QAM signaling scheme. The performance is presented both in terms of mean-square error (MSE) and symbol error rate (SER). 11.  We will consider the system shown in Fig. 1, where zk: are the transmitted symbols, g ( t ) is the channel impulse response, E ( t ) is the white complex Gaussian channel noise and yk are the received symbols. The transmitted symbols x k are taken from a multi-amplitude signal constellation. The D/A and A/D converters contain ideal low-pass filters with bandwidth l/Ts, where T, is the sampling interval. A cyclic extension of time length TG (not shown in Fig. I. for reasons of simplicity) is used to eliminate inter-block interference and preserve the orthogonality of the tones. We treat the channel impulse response g(t) as a timelimited pulse train of the form g( t ) = ams(t TmTs), (1) m where the amplitudes a, are complex valued and 0 5 T,T, 5 TG, i.e., the entire impulse response lies inside Fig. 1: Base-band OFDM system 0-7803-2742-XI95 $4.00