Cognitive Wyner Networks with Clustered Decoding

We study an interference network where equally-numbered transmitters and receivers lie on two parallel lines, each transmitter opposite its intended receiver. We consider two short-range interference models: the "asymmetric network," where the signal sent by each transmitter is interfered only by the signal sent by its left neighbor (if present), and a "symmetric network," where it is interfered by both its left and its right neighbors. Each transmitter is cognizant of its own message, the messages of the $t_\ell$ transmitters to its left, and the messages of the $t_r$ transmitters to its right. Each receiver decodes its message based on the signals received at its own antenna, at the $r_\ell$ receive antennas to its left, and the $r_r$ receive antennas to its right. For such networks we provide upper and lower bounds on the multiplexing gain, i.e., on the high-SNR asymptotic logarithmic growth of the sum-rate capacity. In some cases our bounds meet, e.g., for the asymmetric network. Our results exhibit an equivalence between the transmitter side-information parameters $t_\ell, t_r$ and the receiver side-information parameters $r_\ell, r_r$ in the sense that increasing/decreasing $t_\ell$ or $t_r$ by a positive integer $\delta$ has the same effect on the multiplexing gain as increasing/decreasing $r_\ell$ or $r_r$ by $\delta$. Moreover---even in asymmetric networks---there is an equivalence between the left side-information parameters $t_\ell, r_\ell$ and the right side-information parameters $t_r, r_r$.Comment: Second revision submitted to IEEE Transactions on Information Theor

Interference, Cooperation and Connectivity - A Degrees of Freedom Perspective

We explore the interplay between interference, cooperation and connectivity in heterogeneous wireless interference networks. Specifically, we consider a 4-user locally-connected interference network with pairwise clustered decoding and show that its degrees of freedom (DoF) are bounded above by 12/5. Interestingly, when compared to the corresponding fully connected setting which is known to have 8/3 DoF, the locally connected network is only missing interference-carrying links, but still has lower DoF, i.e., eliminating these interference-carrying links reduces the DoF. The 12/5 DoF outer bound is obtained through a novel approach that translates insights from interference alignment over linear vector spaces into corresponding sub-modularity relationships between entropy functions.Comment: Submitted to 2011 IEEE International Symposium on Information Theory (ISIT

Sparse neural networks with large learning diversity

Coded recurrent neural networks with three levels of sparsity are introduced. The first level is related to the size of messages, much smaller than the number of available neurons. The second one is provided by a particular coding rule, acting as a local constraint in the neural activity. The third one is a characteristic of the low final connection density of the network after the learning phase. Though the proposed network is very simple since it is based on binary neurons and binary connections, it is able to learn a large number of messages and recall them, even in presence of strong erasures. The performance of the network is assessed as a classifier and as an associative memory

Complete Interference Mitigation Through Receiver-Caching in Wyner's Networks

We present upper and lower bounds on the per-user multiplexing gain (MG) of Wyner's circular soft-handoff model and Wyner's circular full model with cognitive transmitters and receivers with cache memories. The bounds are tight for cache memories with prelog $\mu\geq 2/3D$ in the soft-handoff model and for $\mu \geq D$ in the full model, where $D$ denotes the number of possibly demanded files. In these cases the per-user MG of the two models is $1+\mu/D$, the same as for non-interfering point-to-point links with caches at the receivers. Large receiver cache-memories thus allow to completely mitigate interference in these networks.Comment: Submitted to ITW 2016 in Cambridg