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This paper presents a novel cross-tier interference management solution for coexisting two-tier networks by exploiting cognition and coordination between tiers via the use of agile radios. The cognitive users sense their environment to determine the receivers they are interfering with, and adapt to it by designing their precoders using interference alignment (IA) in order to avoid causing performance degradation to nearby receivers. The proposed approach judiciously chooses the set of users to be aligned at each receiver as a subset of the crosstier interferers, hence is termed selective IA. The proposed solution includes identification of the subspace in which cross-tier interference signals would be aligned followed by a distributed algorithm to identify the precoders needed at the selected interferers. The intra-tier interference is then dealt with using minimum mean squared error (MMSE) interference suppression. Numerical results demonstrate the effectiveness of selective IA for both uplink and downlink interference management.

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Abstract-This paper considers uplink interference management for two-tier cellular systems by way of Interference Alignment (IA). In order to manage the uplink interference caused by macrocell users at the femtocell base stations (FBS), cooperation between macrocell users with the closest femtocell base stations is proposed with the goal of aligning the received signals of macrocell users in the same subspace at multiple FBSs. The precoder design for macrocell users is accomplished using successive semidefinite programming relaxations. The proposed solution aims to minimize the cross-tier interference leaked to the femtocells while providing the macrocell users with a minimum received signal to interference plus noise ratio (SINR) at the macrocell base station (MBS). Intra-tier femtocell interference is dealt with minimum mean squared error (MMSE) interference suppression. Numerical results demonstrate that the proposed two-tier interference management approach improves the performance of femtocell users, while maintaining the desired quality of the communication channel of macrocell users.Index Terms-Femtocells/small cells, two-tier networks, uplink interference management, interference alignment, MMSE interference suppression.

How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML keeps both the data and the model information-theoretically private, while allowing efficient parallelization of training across distributed workers. We characterize CodedPrivateML's privacy threshold and prove its convergence for logistic (and linear) regression. Furthermore, via experiments over Amazon EC2, we demonstrate that CodedPrivateML can provide an order of magnitude speedup (up to ∼ 34×) over the state-of-the-art cryptographic approaches.

Abstract-This paper proposes a method for applying the idea of Interference Alignment (IA) in femtocell networks. In order to manage the uplink interference caused by macrocell users at the femtocell base stations (FBS), cooperation between macrocell users with the closest femtocell base stations could be used to align the received signals of macrocell users in the same subspace at multiple FBS simultaneously. We develop a method to apply IA while providing the QoS requirements of macrocell users, in terms of minimum received SINR at the macrocell base station (MBS). With this approach, the BER performance of femtocell users is shown to improve, while maintaining the quality of the communication channel of macrocell users.