ISIT : first set of talks

Outer Bounds for User Cooperation
Ravi Tandon, Sennur Ulukus

This paper looked converse bounds for the MAC with generalized feedback. By applying the dependence balance bound, one can get an outer bound for the capacity region in terms of auxiliary random variables whose cardinality is difficult to bound. For a specific form of the channel, the “user cooperation channel.” where the feedback signals to the users are made from noisy versions of the other users’s signal, they find a way to evaluate the bound by looking at all input densities satisfying the constraints and then arguing that it is sufficient to consider Gaussian densities. The resulting bound matches the full-cooperation and no-cooperation bound when taking the respective limits in the feedback noise, and is tighter than the cutset bound.

Optimal Quantization of Random Measurements in Compressed Sensing
John Sun, Vivek Goyal

The goal here was to design a quantizer for noisy measurements in compressed sensing, while keeping the decoder/reconstructor fixed. This was done in the same high rate scalar quantization setting that I’d seen in other talks, with point-density functions to represent the reconstruction points. They draw a connection to the earlier work on functional scalar quantization and find optimal quantizers for the Lasso. They used an recent called “homotopy continuation,” which looks at the solution produced by the Lasso as a function of the regularization parameter, which I should probably read up on a bit more…

A Sparsity Detection Framework for On-Off Random Access Channels
Sundeep Rangan, Alyson Fletcher, Vivek Goyal

This looked at a MAC with n users and the decoder wants to detect which users transmitted. The users are each “on” with some fixed probability, and they show this can be put into a sparsity detection framework. There are two problems to overcome — the near-far effect of large dynamic range of the received signals, and a “multiaccess interference” (MAI) phenomenon that plagues most detection algorithms, including the Lasso and orthogonal matching pursuit (OMP). The question is whether a practical algorithm can overcome these effects — they show that a modification of OMP can do so as long as the decoder knows some additional information about the received signals, namely the power profile which is the order of the users’ power, conditional that they are active.

Eigen-Beamforming with Delayed Feedback and Channel Prediction
Tr Ramya, Srikrishna Bhashyam

This paper looked at the effect of delay in the feedback link in a system that is trying to do beamforming. The main effect of delay is that there is a mismatch between CSIT and CSIR. There is also the effect of channel estimation error. One solution is to do some channel prediction at the encoder, and they show that delayed feedback can affect the diversity order, while prediction can help improve the performance. Unfortunately, at high Doppler shift and and high SNR, the prediction filter becomes too complex. I hadn’t really seen much work on the effect of longer delay in feedback, so this was an interesting set of ideas for me.


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