A novel viewpoint to the collision resolution problem is introduced in this paper for wireless slotted random access networks. This viewpoint is based on signal separation principles borrowed from signal processing problems. The received collided packets are not discarded in this approach but are exploited to extract each individual user packet information. In particular, if users collide in a given time slot, they repeat their transmission for a total of times so that copies of the collided packets are received. Then, the receiver has to resolve a source mixing problem and separate each individual user. The proposed method does not introduce throughput penalties since it requires only slots to transmit colliding packets. Performance issues that are related to the implementation of the collision detection algorithm are studied in the paper. The protocol's parameters are optimized to maximize the system throughput.Index Terms-Collision resolution, diversity, Markov chain, M/G/1 queue, multiple access, random access, signal separation.

No abstract

No abstract

The time-varying tap coefficients of frequency selective fading channels are typically modeled as random processes with low-pass power spectra. However, traditional adaptive techniques usually make no assumption on the channel's time variations and hence do not exploit this information. In this paper, Kalman filtering methods are derived to track the channel by employing a multichannel autoregressive description of the time-varying taps in a decision-feedback equalization framework. Fitting a model to the variations of the channel's taps is a challenging task because the tap coefficients are not observed directly. A novel linear method is proposed in this paper in order to estimate the model parameters from input/output data. Consistency of the proposed method is shown, and some illustrative simulations are presented.

No abstract