946 publications from this institution
The 3rd generation cellular communications standard will be based on wideband CDMA (WCDMA). To obtain adequate performance multiuser receivers, such as LMMSE receivers, can be used. Unfortunately, previously suggested LMMSE receivers cannot be applied in WCDMA systems employing long scrambling codes. In this paper, linear receivers suitable also for downlink with long scrambling codes are studied. These receivers equalize the channel prior to the despreading, thus restoring the orthogonality of users and suppressing multiple access interference (MAI). The numerical results show that chip equalizer based receivers can offer significant performance improvement in comparison to the conventional RAKE receiver.
A deep learning (DL)-based power control algorithm that solves the max-min user fairness problem in a cell-free massive multiple-input multiple-output (MIMO) system is proposed. Max-min rate optimization problem in a cell-free massive MIMO uplink setup is formulated, where user power allocations are optimized in order to maximize the minimum user rate. Instead of modeling the problem using mathematical optimization theory, and solving it with iterative algorithms, our proposed solution approach is using DL. Specifically, we model a deep neural network (DNN) and train it in an unsupervised manner to learn the optimum user power allocations which maximize the minimum user rate. This novel unsupervised learning-based approach does not require optimal power allocations to be known during model training as in previously used supervised learning techniques, hence it has a simpler and flexible model training stage. Numerical results show that the proposed DNN achieves a performance-complexity trade-off with around 400 times faster implementation and comparable performance to the optimization-based algorithm. An online learning stage is also introduced, which results in near-optimal performance with 4-6 times faster processing.
