This work concerns a limited feedback scheme taking into account the channel time-correlation during the feedback design in slow fading environment. We consider the Coordinated MultiPoint (COMP) system model with other-cell interference (OCI). We determine the feedback updating period which is triggered by the transmitters to enable the receiver to send the CSI. This updating period is function of the channel temporal correlation, available feedback bits, and the number of transmit antennas. An optimal updating period is derived such that it guarantees that the average signal-to-interference-and-noise ratio (SINR) is greater than or equal to that of the conventional feedback scheme that does not adapt its feedback period to the temporal correlation. The numerical results show performance improvement in terms of overall capacity and feedback overhead reduction when comparing the proposed adaptive scheme with conventional feedback approaches.

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In a recent work, Nazer and Gastpar proposed the Compute-and-Forward strategy as a physical-layer network coding scheme. They described a code structure based on nested lattices whose algebraic structure makes the scheme reliable and efficient. In this work, we consider the implementation of their scheme for real Gaussian channels and one dimensional lattices. We relate the maximization of the transmission rate to the lattice shortest vector problem. We explicit, in this case, the maximum likelihood criterion and show that it can be implemented by using an Inhomogeneous Diophantine Approximation algorithm.

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In this paper, we consider the layered two-hop relay channel, operating under the linear rotate-and-forward (RF) scheme [1], where the relays rotate the received signal before its retransmission. In this work, a limited feedback channel is considered between the relays and the destination. The destination finds the optimal rotation vector and feeds it back to the relays. We propose an iterative algorithm to find this optimal rotation vector. The proposed algorithm is shown to have optimal performance with low complexity.