Flexible Backhaul Design and Degrees of Freedom for Linear Interference Networks

The considered problem is that of maximizing the degrees of freedom (DoF) in cellular downlink, under a backhaul load constraint that limits the number of messages that can be delivered from a centralized controller to the base station transmitters. A linear interference channel model is considered, where each transmitter is connected to the receiver having the same index as well as one succeeding receiver. The backhaul load is defined as the sum of all the messages available at all the transmitters normalized by the number of users. When the backhaul load is constrained to an integer level B, the asymptotic per user DoF is shown to equal (4B-1)/(4B), and it is shown that the optimal assignment of messages to transmitters is asymmetric and satisfies a local cooperation constraint and that the optimal coding scheme relies only on zero-forcing transmit beamforming. Finally, an extension of the presented coding scheme is shown to apply for more general locally connected and two-dimensional networks.Comment: Submitted to IEEE International Symposium on Information Theory (ISIT 2014

Dynamic Interference Management

A linear interference network is considered. Long-term fluctuations (shadow fading) in the wireless channel can lead to any link being erased with probability p. Each receiver is interested in one unique message that can be available at M transmitters. In a cellular downlink scenario, the case where M=1 reflects the cell association problem, and the case where M>1 reflects the problem of setting up the backhaul links for Coordinated Multi-Point (CoMP) transmission. In both cases, we analyze Degrees of Freedom (DoF) optimal schemes for the case of no erasures, and propose new schemes with better average DoF performance at high probabilities of erasure. For M=1, we characterize the average per user DoF, and identify the optimal assignment of messages to transmitters at each value of p. For general values of M, we show that there is no strategy for assigning messages to transmitters in large networks that is optimal for all values of p.Comment: Shorter version is in proceedings of the Asilomar Conference on Signals, Systems, and Computers, Nov. 201

Deep Neural Network Architectures for Modulation Classification

In this work, we investigate the value of employing deep learning for the task of wireless signal modulation recognition. Recently in [1], a framework has been introduced by generating a dataset using GNU radio that mimics the imperfections in a real wireless channel, and uses 10 different modulation types. Further, a convolutional neural network (CNN) architecture was developed and shown to deliver performance that exceeds that of expert-based approaches. Here, we follow the framework of [1] and find deep neural network architectures that deliver higher accuracy than the state of the art. We tested the architecture of [1] and found it to achieve an accuracy of approximately 75% of correctly recognizing the modulation type. We first tune the CNN architecture of [1] and find a design with four convolutional layers and two dense layers that gives an accuracy of approximately 83.8% at high SNR. We then develop architectures based on the recently introduced ideas of Residual Networks (ResNet [2]) and Densely Connected Networks (DenseNet [3]) to achieve high SNR accuracies of approximately 83.5% and 86.6%, respectively. Finally, we introduce a Convolutional Long Short-term Deep Neural Network (CLDNN [4]) to achieve an accuracy of approximately 88.5% at high SNR.Comment: 5 pages, 10 figures, In proc. Asilomar Conference on Signals, Systems, and Computers, Nov. 201