Multiuser information processing in wireless communication

The available wireless bandwidth cannot keep pace with the amazing growth rate of wireless subscribers. Moreover, the higher data rate and superior quality of service demand of the increasingly popular multimedia services over wireless networks mandate efficient utilization of the channel. Unfortunately, the wireless channel is not very conducive towards error-free raw data transmission. Forward error correction schemes are therefore necessary for reliable information delivery. Error correcting schemes rely on the addition of redundancy to information content. Therefore it is essential to know the limits of channels in order to design the most efficient wireless systems. Traditional Shannon capacity defines the maximum rate at which data can be perfectly transferred over a channel. But none of the practical error control schemes achieve zero error rate. Moreover, most of the wireless applications can tolerate certain bit error rates. We introduce the notion of distorted channel capacity that measures the maximum rate of data transfer over a channel under a given bit error rate criteria. The performance of the various practical codes benchmarked against this measure reveals that only complex codes can perform close to the optimal bound. For example, convolutional codes of large constraint lengths perform much better than codes with smaller constraint lengths. The decoding complexity of the commonly deployed Viterbi algorithm, however, grows exponentially with the constraint length. We propose a new maximal weight basis decoding technique whose complexity is only quadratic in the constraint length and hence is better suited for real time systems deploying strong codes for error protection. We finally investigate a computationally efficient implementation of a multiuser wireless receiver. The optimal joint decoding complexity of a coded multiuser system is exponential in the number of users. Our proposed iterative joint interference cancellation and decoding technique strikes a balance between the computational complexity and the receiver performance