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This paper proposes to encode pilot bits together with information bits by using a systematic encoder and applying them to an iterative receiver, where channel estimation and decoding are iteratively performed. This paper then shows why and how the encoded pilot bits can give a positive gain over a conventional uncoded pilot system. Specifically, this paper considers an orthogonal frequency-division multiplexing system that is encoded by a low-density parity-check code under a frequency-selective fading channel and shows theoretical analysis as well as simulation results. Index Terms--Channel estimation, encoded pilot (EP), iterative receiver, low-density parity-check (LDPC), orthogonal frequency-division multiplexing (OFDM).

Recently, the smart antenna (a blind adaptive antenna array) has brought much attention to its ability to improve the future code-division multiple-access (CDMA) wireless communications systems. However, Adachi et al. [1], [2] employed only one out of M elements that are in a smart antenna array during the pseudonoise (PN) code acquisition process due to the difficulty in obtaining the weight coefficients of the smart antenna and PN code acquisition jointly and adaptively. Yet, as the PN code is acquired, all M elements are used in [1] and [2] to improve the signal-to-noise ratio (SNR) at a reverse link. In this paper, we propose an efficient PN code acquisition scheme where all elements are employed during PN code acquisition. Also, we show that by using all elements, the PN code acquisition time can be shortened by at least 40% for a given signal-to-noise ratio and M = 5, compared to the single element case. Furthermore, the power of a random access user signal in the reverse link can be lowered at least 3 dB. The additional computational load and complexity to use the proposed PN code acquisition are insignificant. Index Terms--Code.division multiple access (CDMA), pseudonoise (PN) code acquisition, smart antennas.

The analysis in this paper concerns the performance of smart antenna algorithms when used in code-division multiple access (CDMA) wireless communication systems. Complex pseudonoise (PN) spreading, despreading, and pilot-aided channel estimates in the cdma2000 reverse link are some of major characteristics that are different from those in the IS-95 CDMA systems. These different features are included in our analysis. Four computationally efficient smart antenna algorithms are introduced: 1) smart antenna based on maximum output power criteria without lagrange multiplier; 2) smart antenna based on maximum signal-to-interference-plus-noise output power ratio (SINR) criteria with eigenvector solution; 3) smart antenna based on maximum SINR output criteria without eigenvector solution; 4) more simplified smart antenna based on maximum SINR output criteria without eigenvector solution. Algorithms (1) and (4) require only 4M computational instruction cycles per snapshot where M is the number of antenna array elements. Algorithms (2) and (3) require [M.sup.2] and (4M + 2[M.sup.2]) operations per snapshot, respectively. These computational loads are significantly smaller than those of typical eigenvalue decomposition blind detection approaches. Bit error rates (BERs) resulting from these algorithms are evaluated through simulation. Double spike power delay profile with equal or unequal power is used. Also, a cluster of interfering users and scattered interference users are considered. For BER comparisons, antenna diversity using equal gain combining is also analyzed. The four smart antenna algorithms show significant capacity improvement compared to the antenna array diversity using equal gain combining under the double spike power delay profile with equal power and scattered interference environments. Index Terms--Adaptive array antenna, code division multiple access (CDMA), mobile fading environment, multiple access interference, smart antenna, wireless communications.

Recently, a digital communication receiver, called a third-generation receiver, has been developed. This receiver takes samples of the direct-sequence spread signal at a nonzero intermediate frequency (IF) instead of the zero IF (baseband), and quantizes the samples by employing a 1-b analog-to-digital (A/D) converter at the receiver front end. These 1-b samples are digitally processed for pseudonoise (PN) code, carrier, bit synchronization, and bit decision with the use of an application-specific integrated circuit. In this paper, the effects of the IF sampling and 1-b A/D conversion on PN code synchronization are analyzed for a PN spread-spectrum communication system with oversampling rate, e.g., 12 samples per chip. In addition, bit-error rate (BER) degradation due to the 1-b A/D conversion is studied by assuming perfect PN code, carrier, and bit synchronization. It is observed that BER degradation due to the 1-b A/D is significant, e.g., 2.4 dB, when decimation is made after IF sampling such that only one sample per chip is used for bit decision. These analyzed BER results agree well with the simulated results. However, if no decimation is made and oversampling is used for bit decision, BER degradation due to 1-b A/D conversion is insignificant, e.g., 0.6 dB. Index Terms - Digital receiver, 1-b A/D signal processing, PN code acquisition, PN spread spectrum, satellite communications.

A baseband digital narrow-band FM receiver, called zero-intermediate frequency zero-crossing demodulator (ZIFZCD), has been recently developed. This demodulator may offer low complexity and simple implementation. However, the bit error rate (BER) performance of the ZIFZCD is inferior to that of the limiter-discriminator-integrate-and-dump (LDI) demodulator. In this paper, a simple decision feedback postprocessor (DFP) is proposed to improve the performance of the ZIFZCD. Analysis and simulation BER results of the ZIFZCD with the DFP are presented for minimum-shift keying (MSK) and Gaussian MSK (GMSK) signals under additive white Gaussian noise (AWGN) and mobile fading environments. Index Terms--GMSK, narrow-band digital FM, wireless communications, zero-crossing demodulation, zero-IF.