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To achieve the full multiplexing gain of MIMO interference networks at high SNRs, the interference from different transmitters must be aligned in lower-dimensional subspaces at the receivers. Recently a distributed “max-SINR” algorithm for precoder optimization has been proposed that achieves interference alignment for sufficiently high SNRs. We show that… (More)

We study distributed algorithms for updating transmit precoding matrices for a two-user Multi-Input/Multi-Output (MIMO) interference channel. Our objective is to maximize the sum rate with linear Minimum Mean Squared Error (MMSE) receivers, treating the interference as additive Gaussian noise. An iterative approach is considered in which given a set of… (More)

Achieving high spectral efficiencies in wireless networks requires the ability to mitigate and manage the associated interference. This becomes especially important in networks where many transmitters and receivers are randomly placed, so that in the absence of coordination a particular receiver is likely to encounter significant interference from a… (More)

- Changxin Shi, Randall Berry, Michael L. Honig
- 2009 IEEE International Symposium on Information…
- 2009

We study distributed algorithms for allocating powers and/or adjusting beamforming vectors in a peer-to-peer wireless network which may have multiple-input-single-output (MISO) links. The objective is to maximize the total utility summed over all users, where each user's utility is a function of the received signal-to-interference-plus-noise ratio (SINR).… (More)

- Changxin Shi, Randall Berry, Michael L. Honig
- 2008 42nd Annual Conference on Information…
- 2008

We present a distributed algorithm for allocating power among multiple interfering transmitters in a wireless network using orthogonal frequency division multiplexing (OFDM). The algorithm attempts to maximize the sum over user utilities, where each user's utility is a function of his total transmission rate. Users exchange interference prices reflecting… (More)

- Changxin Shi, R. Berry, M. Honig
- 2008 46th Annual Allerton Conference on…
- 2008

We study a distributed algorithm for adapting transmit beamforming vectors in a multi-antenna peer-to-peer wireless network. The algorithm attempts to maximize a sum of per-user utility functions, where each user's utility is a function of his transmission rate, or equivalently the received signal-to-interference plus noise ratio (SINR). This is… (More)

- David A. Schmidt, Changxin Shi, Randall Berry, Michael L. Honig, Wolfgang Utschick
- IEEE Transactions on Signal Processing
- 2013

This paper presents a comparative study of algorithms for jointly optimizing beamformers and receive filters in an interference network, where each node may have multiple antennas, each user transmits at most one data stream, and interference is treated as noise. We focus on techniques that seek good suboptimal solutions by means of iterative and… (More)

- Changxin Shi, Randall Berry, Michael L. Honig
- 2011 IEEE International Symposium on Information…
- 2011

We consider an interference network with multi-carrier transmission over M parallel sub-channels. There are K transmitter-receiver pairs, each transmitter transmits a single data stream with a rank-one precoding matrix, and the receivers are assumed to be linear. We show that a necessary condition for zero interference (alignment across sub-channels) is K… (More)

We study a distributed algorithm for adjusting beamforming vectors in a peer-to-peer wireless network with multiple-input multiple-output (MIMO) channels. Each transmitter precoding matrix has rank one, and a linear minimum mean squared error (MMSE) filter is applied at each receiver. Our objective is to maximize the total utility summed over all users,… (More)

- Changxin Shi, Randall Berry, Michael L. Honig
- 2010 44th Annual Conference on Information…
- 2010

We study distributed algorithms for adjusting beamforming vectors and receiver filters in multiple-input multiple-output (MIMO) interference networks, with the assumption that each user uses a single beam and a linear filter at the receiver. In such a setting there have been several distributed algorithms studied for maximizing the sum-rate or sum-utility… (More)