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Interference alignment (IA) through beamforming in MIMO Interfering Broadcast Channels (IBC) allows to handle multi-cell interference with low latency. However, with multiple antennas on both ends, the MIMO setting requires global Channel State Information at the Transmitter (CSIT) (i.e. CSIT from the other transmitters (Tx) also). Though global CSIT can be(More)
This work deals with beamforming for the MIMO Interfering Broadcast Channel (IBC), i.e. the Multi-Input MultiOutput (MIMO) Multi-User Multi-Cell downlink (DL). The novel beamformers are here optimized for the Expected Weighted Sum Rate (EWSR) for the case of Partial Channel State Information at the Transmitters (CSIT). Gaussian (Posterior) partial CSIT can(More)
We consider a combined form of partial CSIT (Channel State Information at the Transmitter(s) (Tx)), comprising both channel estimates (mean CSIT) and covariance CSIT. In particular multipath induced structured low rank covariances are considered that arise in Massive MIMO and mmWave settings. For the beamforming optimization, we first revisit Weighted Sum(More)
We consider the Multi-Input Single Output (MISO) Interfering Broadcast Channel (IBC), in other words the multiuser (MU) multi-cell half duplex downlink in a cellular or heterogeneous network, aided by a full duplex MIMO relay. The Degrees of Freedom (DoF) are analyzed for joint coordinated beamforming by the base stations and interference neutralization by(More)
We propose a decentralized algorithm for weighted sum rate (WSR) maximization via large system analysis. The rate maximization problem is done via weighted sum mean-squared error (WSMSE) minimization. Decentralized processing relies on the exchange via a backhaul link of a low amount of information. The inter-cell interference terms couple the maximization(More)
The weighted sum rate (WSR) maximizing linear precoding algorithm is studied in large correlated multiple-input single-output (MISO) interference broadcast channels (IBC). We consider an iterative WSR design via difference of convex functions (DC) programming as in [1], [2] and [3], focusing on the version in [3]. We propose an asymptotic approximation of(More)
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