This paper investigates an adaptive M-QAM modulation scheme with constant peak power for which the received signal is divided by the estimated channel gain. Generally, for adaptive modulation schemes, the transmitter adjusts its modulation parameters based on the received power. The receiver estimates channel conditions and sends feedback information via a return channel to the transmitter. In this paper, we consider Rayleigh flat fading channels. At the receiver the fading is estimated using pilot symbols. To cancel the channel impact, we correct the received signal by dividing it by the estimated value of the fading. So, we propose to adjust the modulation level by examining the statistics of the corrected signal. The modulation switching protocol take into account the channel estimation error variance. Since linear region of practical amplifiers is limited, when switching from one modulation scheme to an other, the peak power is kept constant.

The performance of suboptimal convolutional decoding over fading channels is explored. The suboptimal decoding algorithm used is the bidirectional algorithm. By estimating a "decoder weight spectrum" for the decoder, an "equivalent free distance" may be observed. Furthermore, by using this "decoder weight spectrum", useful estimations of the error probabilities are obtained and compared to computer-simulation results in the case of very slow and very fast fading. The resultant curves are shown to be very tightly related. Computer-simulation results are also shown for various signal-to-noise ratios, normalized Doppler spreads, and frame length on three typical fading channels: the Rayleigh fading channel with exponential and Bessel autocorrelation functions and the Rician fading channel with exponential autocorrelation function. We show that considerable gains (up to 4 dB) can be obtained with respect to a similar-complexity Viterbi decoder at a frame error probability P/sub e/=10/sup -3/ and a slightly smaller gain (up to 1.8 dB) at a bit error probability P/sub b/=10/sup -5/.

This paper describes a complete digital radio system model which takes into account the effects of most degradations due to channel conditions and equipment imperfections. System parameters which are taken into consideration include, but are not limited to, the following: modulator imbalance, filter frequency responses, power amplifier nonlinearities, carrier and symbol timing recovery loops, and synthesizer phase noise. The parameters for each module in the radio can be varied, and the end-to-end performance computed. Furthermore, a novel semianalytic method is developed for the purpose of speeding up the simulation leading to the calculation of the bit error rate (BER) versus E/sub b//N/sub o/ for the radio model. This novel technique, when compared with classical semianalytic methods, provides 1 dB improvement in the accuracy of the simulation results. The model accurately predicts the radio performance as measured by BER versus E/sub b//N/sub o/, dispersive fade margin, transmitted frequency spectrum, and transient acquisition responses. Simulation results for 16 QAM and OQPSK systems were compared to measurements on two physical radios. The accuracy of the simulation results was found to be within 0.1 dB in E/sub b//N/sub o/ at a BER of 10/sup -6/ without the RF portions and between 0.2 and 0.45 dB for a complete radio system.

The evaluation of the performance of partial selection networks which select a set of M elements from a set of N inputs is addressed. The partial selection problem occurs when dealing with non-exhaustive multi-path breadth-first searches, like in the M algorithm or the bidirectional algorithm. These algorithms are used in the decoding of convolutional codes. The paper presents a set of bounds to evaluate the quality of regular, Delta class, networks of depth 1gN and width N/2, with respect to their selection capabilities. The results from the bounds are compared to Monte Carlo simulations of the selection capabilities of the Banyan and Alekseyev networks. Finally, the performance degradation associated with the use of these networks on the performance of a bidirectional decoder is presented. In particular, the authors show that even with imperfect selection, the bidirectional decoder can outperform a Viterbi decoder of comparable complexity.>

Bidirectional multiple-path tree searching algorithms for the decoding of convolutional codes are presented. These suboptimal coding algorithms use a multiple-path breadth-first bidirectional tree exploration procedure and long-memory convolution codes. It is shown that, compared to the usual M-algorithm, the bidirectional exploration considerably reduces the bit error propagation due to correct path loss. Computer simulations using rate-1/2 codes over binary symmetric channels are used to analyze the effect of the number of path extensions, code memory, and frame length on the bit error probability. The results show that with a bit error probability of 10/sup -5/, coding gains on the order of 2 dB over the M-algorithm and 1 dB over a Viterbi decoder of equivalent complexity can be achieved.>

Bidirectional suboptimal breadth-first decoding of convolutional codes is an attractive technique for slowly-varying and quasistatic fading channels as it restricts the extent of decoding errors due to correct path loss to very heavy noise or interference regions. The paper compares the performance of such a decoding scheme to the Viterbi algorithm over wideband TDMA indoor radio links where equalization and space diversity are also used to combat dispersive fading and cochannel interference. It is shown that, with dual space diversity, Viterbi decoding and bidirectional decoding of convolutional codes are both attractive alternatives, in terms of outage rate, to increasing the space diversity order from two to three. On the basis of equal computational complexity, bidirectional decoding is also shown to be superior to Viterbi decoding. Furthermore, this advantage increases as the bit error rate performance criterion becomes more stringent which makes bidirectional decoding particularly attractive for data applications.>