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Compared with traditional device-to-device (D2D) communication networks, the users in the cache-enabled D2D communication networks can easily obtain their requested contentsfromthenearbyusers,andreducethebackhaulcosts. In this paper, we investigate the caching strategy for the cacheenabled D2D communication networks, with the consideration of caching placement and caching delivery. The content popularity and user mobility are predicted by a machine learning approach of echo state networks (ESNs) in order to determine which content to cache and where to cache.

A terrestrial network deployment with mobile cell connectivity using a bandwidth limited satellite backhaul is being investigated in terms of the integration of state-of-the-art 4G commercial off-the-shelf network components. A testbed is analyzed for providing end-to-end quality of service and flexible resource allocation on the satellite link using a novel multi-carrier modem technology with dynamic bandwidth allocation.

The knowledge of the downlink (DL) channel spatial covariance matrix at the BS is of fundamental importance for large-scale array systems operating in frequency division duplexing (FDD) mode. In particular, this knowledge plays a key role in the DL channel state information (CSI) acquisition. In the massive MIMO regime, traditional schemes based on DL pilots are severely limited by the covariance feedback and the DL training overhead. To overcome this problem, many authors have proposed to obtain an estimate of the DL spatial covariance based on uplink (UL) measurements.

We consider a bi-directional Full-Duplex (FD) Multiple-Input Multiple-Output (MIMO) communication system in which nodes are capable of performing transitter (TX)- Receiver (RX) digital precoding/combining and multi-tap analog cancellation, and have individual Signal-to-Interference-plus- noise Ratio (SINR) requirements. We present an iterative algorithm for the TX powers minimization that includes closed- form expressions for the TX/RX digital beamformers at each algorithmic iteration step.

We consider the problem of jointly registering multiple point sets using rigid transforms. We propose a distributed algorithm based on consensus optimization for the least-squares formulation of this problem. In each iteration, the computation is distributed among the point sets and the results are averaged. For each point set, the dominant cost per iteration is the SVD of a square matrix of size d, where d is the ambient dimension. Existing methods for joint registration are either centralized or perform the optimization sequentially.